Disease monitoring devices and methods

Abstract

A method for determining an indicator of disease in a region of a subject's body is provided. The method includes receiving imaging data obtained by imaging the region of the subject's body, the imaging data including data representing a distribution in the region of a marker administered to the subject prior to the imaging, the marker binding to a biological target associated with the disease. The imaging data is processed to obtain an indicator of marker signal in the region. The indicator of marker signal is corrected to account for an effect of tissue structure in the region on the marker signal. The corrected indicator of marker signal is used to determine an indicator of disease in the region. Also provided is an apparatus configured to perform this method.

Description

[Technical Field]

[0001] The present invention relates to medical imaging, and in particular to medical imaging for disease monitoring. [Background technology]

[0002] The medical field needs better ways to identify which drugs patients are likely to respond to before treatment begins. Currently, more than 50% of disease-modifying drugs fail in the clinic due to suboptimal patient selection based on blood biomarkers. When a patient develops a disease, doctors prescribe a specific drug and then must wait several months to see if the drug is effective in controlling the condition. If the drug is ineffective, doctors prescribe a different drug, repeating the process until the optimal drug is found. This wastes valuable time and money in the medical field. This issue is becoming increasingly important as the cost of new drugs continues to rise. Furthermore, the pharmaceutical industry needs better methods to stratify participants in clinical trials of new drugs, such as anti-inflammatory and immunomodulatory drugs.

[0003] For example, chronic inflammation is a core process in the pathophysiology of the world's leading causes of morbidity and mortality. Cardiovascular and cerebrovascular diseases are increasingly recognized as inflammatory diseases and are the leading causes of death worldwide, followed by chronic obstructive pulmonary disease (COPD), which is characterized by the chronic activation of inflammatory pathways and is usually caused by repeated inhalation of toxic substances. Furthermore, autoimmune inflammatory diseases such as rheumatoid arthritis and inflammatory bowel disease are also chronic inflammatory diseases that cause significant morbidity worldwide, particularly in Europe and North America. Chronic inflammatory diseases represent a significant burden on health services, and novel disease-modifying therapies are desperately needed. While the introduction of primary and secondary prevention strategies has improved outcomes for cardiovascular disease, the development of additional therapeutic interventions is currently underway. Summary of the Invention [Problem to be solved by the invention]

[0004] Inflammatory diseases have seen advances in recent years in therapeutic approaches, with the introduction of monoclonal antibody therapy in diseases such as rheumatoid arthritis and Crohn's disease. Anti-TNF-α therapies, such as infliximab, and biologics, such as anti-CD20 (rituximab) and anti-IL-6 (tocilizumab or sarilumab), have been introduced as part of established treatment protocols in rheumatoid arthritis. In pulmonary diseases, the development of novel therapeutics to treat asthma has provided monoclonal antibody therapy for specific patient populations, with clear evidence of benefit, but similar progress has not been made in the search for disease-modifying therapies in COPD. Studies of anti-IL-5 therapy, anti-IL-5Rα therapy, and anti-TNF-α therapy have failed to demonstrate significant benefit in COPD patient populations.

[0005] The heterogeneity of the COPD disease population is likely a major factor in this apparent lack of efficacy, as it is now believed that certain inflammatory phenotypes or endotypes respond to certain therapies. Similarly, although anti-TNF-α antibodies are an established treatment for rheumatoid arthritis, heterogeneity in the patient population means that up to 40% of patients do not demonstrate a long-term therapeutic response to treatment, and suboptimal genotyping of patients based on blood biomarkers and clinical characteristics can lead to poor response to treatment. (1) There are currently no available tools to predict which patients will develop inflammatory bowel disease. Treatments for inflammatory bowel disease are also often ineffective.

[0006] The current lack of treatments and interventions that impact the pathology of patients with airway diseases, as well as the difficulty of selecting appropriate treatments for individual patients in other inflammatory diseases, necessitates a greater understanding of the complex immune processes involved. Therefore, a greater understanding of the mechanisms involved in these inflammatory pathways and the identification of therapeutic targets represent important scientific challenges.

[0007] Technologies are now available that allow immunological imaging of tissues and organs using single-photon emission computed tomography (SPECT) and positron emission tomography (PET) techniques. These techniques use radiolabeled fluorodeoxyglucose ( 18 Radiopharmaceuticals such as F-FDG and radiolabeled monoclonal antibodies are used to visualize processes, cells, or cytokines associated with the immune response. These nuclear medicine techniques have been used in oncology to date to treat a variety of cancers. (5,6) This form of imaging has enabled researchers to perform more extensive and less invasive procedures in ways previously only possible with tissue biopsies. (2~4) Active inflammation and pathological processes can be visualized without the need for a

[0008] Applying the same techniques to imaging the immune system not only offers the opportunity to study diseases such as chronic inflammatory processes in vivo, but may also enable the prediction of response to certain treatments, particularly monoclonal antibody therapies. Novel functional imaging of the immune system offers new insights into these processes and has potential as a tool for predicting response to disease-modifying therapies.

[0009] A number of early studies have been conducted in various chronic inflammatory diseases, and it has been suggested that increased radiolabeled antibody uptake, as seen by imaging, in inflamed tissues may be a good predictor of improved response to monoclonal antibody therapy or systemic therapy. (7~9) Some studies suggest that the presence of inflammatory tissue correlates with the adaptation of the immune system to disease. However, these studies are not yet conclusive, and further development is needed to develop methods and systems that can reliably identify disease markers, such as inflamed tissue, and evaluate the effectiveness of disease treatments. [Means for solving the problem]

[0010] According to a first aspect, there is provided a method for determining a measure of disease in a region of a body of a subject, the method comprising receiving imaging data obtained by imaging a region of the body of the subject, the imaging data including data representing the distribution in the region of markers that are administered to the subject prior to imaging and bind to biological targets associated with the disease, processing the imaging data to obtain a measure of marker signals in the region, correcting the measure of marker signals to take into account the effect of tissue structure in the region on the marker signals, and determining the measure of disease in the region using the corrected measure of marker signals.

[0011] In vivo molecular imaging has the potential to non-invasively assess disease, for example, by quantifying inflammatory processes in people with chronic inflammatory diseases. Correcting for the influence of tissue structure on signal eliminates confounding factors that make it difficult to distinguish signals due to inflammation from other factors. This could enable a new approach to precision medicine, allowing clinicians to select effective disease-modifying therapies for each individual patient right from the start, without invasive procedures.

[0012] Optionally, the corrected measure of marker signal represents the level of binding of the marker to tissue within the region, where marker bound to tissue is more likely to represent the amount of biological target within the tissue, as opposed to marker that is present in the region for other reasons.

[0013] Optionally, the disease is an inflammatory disease and the biological target is a component of the inflammatory pathway. Markers that bind to components of the inflammatory pathway have been tested and represent promising and valuable applications of this technology.

[0014] Optionally, correcting the marker signal index includes correcting the marker signal for vascular flow in the region. Markers are typically administered into the patient's bloodstream, meaning that significant signal arises from free marker in the blood. Furthermore, regions with high perfusion due to high vascular flow may have higher levels of bound marker because more marker is available for tissue interaction. Therefore, the signal in the region may be distorted by the amount of vasculature present. Correcting for this provides a more accurate picture of the presence of the biological target relative to the rest of the region.

[0015] Optionally, correcting the marker signal for vascular flow includes determining a vascular density within the region, which can be used to estimate the proportion of the signal within the region that is due to free marker in the blood, as opposed to marker bound to a biological target.

[0016] Optionally, determining the vascular density includes determining the total volume of blood vessels in the region, which can be used to estimate how much signal needs to be corrected for the total vascular flow in the region.

[0017] Optionally, the imaging data further includes data representative of tissue structure within the region, and determining the vascular density includes identifying blood vessels within the region using the data representative of the tissue structure. Using another source of structural data, such as another imaging modality suitable for imaging the vasculature, can improve the accuracy of the determination of vascular density.

[0018] Optionally, the data representing tissue structure is obtained by one or more of low-dose computed tomography, high-resolution computed tomography, multi-slice computed tomography, magnetic resonance imaging, plain radiography, and ultrasound, which imaging modalities may be particularly suited to determining tissue structure within a region.

[0019] Optionally, correcting the marker signal for vascular flow in the region includes determining a background marker signal corresponding to the marker signal from unbound marker in the blood, thereby taking into account the signal level due to free marker in the subject's blood at the time of measurement and providing a more accurate indication of relative marker activity.

[0020] Optionally, correcting the marker signal index includes correcting for tissue density within the region. Variations in tissue density within a region can give a false impression of the level of disease within the tissue. Even at the same disease level, denser tissue naturally absorbs more marker per unit volume than less dense tissue. This can lead to a false inference that the tissue is more diseased when it is simply denser. Correcting for this effect allows for a more accurate estimation of the level of disease.

[0021] Optionally, the imaging data further includes data representative of tissue structure within the region, and correcting for tissue density includes using the data representative of tissue structure to identify subregions within the region having reduced tissue density, whereby appropriate correction can be performed for the subregions.

[0022] Optionally, the data representing tissue structure is obtained by one or more of low-dose computed tomography, high-resolution computed tomography, multi-slice computed tomography, magnetic resonance imaging, plain radiography, and ultrasound, which imaging modalities are particularly suited to determining structural information.

[0023] Optionally, data representing tissue structure is acquired using X-rays, and if the X-ray attenuation in a subregion is below a predetermined threshold, the subregion is identified as a subregion with reduced tissue density. Reduced X-ray attenuation is a known method for identifying reduced tissue density, particularly in the respiratory system. Therefore, using this approach is convenient for integration with existing systems and processes.

[0024] Optionally, correcting for tissue density includes correcting the marker signal index based on the percentage of tissue in the region of reduced density, allowing the method to account for signal reductions that are not due to reduced levels of disease.

[0025] Optionally, correcting the marker signal index based on the proportion of tissue within the hypodensity region is determined using the marker signal index for a plurality of individuals with different proportions of tissue within the hypodensity region, thereby properly calibrating how changes in tissue density affect the marker signal.

[0026] Optionally, the marker signal measure is the mean marker signal within the region, and optionally the median marker signal. Using the mean marker signal provides a more accurate measure of the signal across the region. The median signal is less distorted by isolated extreme values ​​than other types of averages.

[0027] Optionally, the marker signal index is normalized by a background marker signal, which corresponds to the marker signal from unbound marker in the blood. Normalizing the index to the background signal provides a better indication of relative marker activity in the region.

[0028] Optionally, a background marker signal is distinguished from marker signals in major vessels, optionally in the aortic arch. Signals from major vessels are likely to be dominated by signals from unbound markers in the blood, making them a useful source of background signal. Optionally, the background marker signal is an average marker signal, optionally an arithmetic mean marker signal, of major vessels. Averaging across vessels provides a more representative measure of background activity.

[0029] Optionally, biological targets include immune and / or inflammatory signaling proteins, such as cytokines, chemokines, cell surface receptors, or extracellular matrix components, all of which may be indicative of disease in the body and thus can be used to determine disease indicators.

[0030] Optionally, the marker comprises a radioactive marker and / or the marker comprises a monoclonal antibody, which can be designed to have highly specific binding activity to target a particular component, and the radioactive marker can be easily detected using non-invasive imaging techniques.

[0031] Optionally, data representing the distribution of markers within a region are acquired using single-photon emission computed tomography, SPECT, positron emission tomography, PET, planar scintigraphy, or magnetic resonance imaging, all of which can provide adequate resolution imaging of various types of markers.

[0032] Optionally, the region of the subject's body includes an organ of the subject, for example, one or both of the subject's lungs, or a joint of the subject, which are areas where common pathological processes such as inflammation are of particular concern and may be of particular interest in diagnosing a patient or evaluating a treatment plan.

[0033] Optionally, the imaging data includes data representing the distribution within the region of each of a plurality of markers administered to the subject prior to imaging, each of the plurality of markers binding to a different biological target, processing the imaging data includes processing the imaging data to obtain a marker signal index for each of the plurality of markers, correcting the marker signal index includes correcting each of the marker signal indexes, and determining the disease index uses the corrected index of the plurality of marker signals. Integrating the plurality of markers can distinguish between different biological targets. Furthermore, the effectiveness of different treatments targeting different biological targets can be simultaneously evaluated, thereby improving the speed and convenience of diagnosis for patients.

[0034] Optionally, the data representing the tissue structure and the data representing the distribution of markers have different resolutions and / or volumetric divisions, are measured in different states of the subject's body, and / or are measured at different times, and the method further comprises aligning the data representing the tissue structure and the data representing the distribution of markers, thereby allowing different measurements to be performed consecutively and providing rest for the patient to improve comfort. It is also possible to integrate data measured with devices of different specifications.

[0035] Optionally, the matching includes using a non-rigid registration algorithm, which allows the matching to take into account deformations of body regions, such as due to lung expansion during breathing.

[0036] Optionally, matching includes transforming the data representing the tissue structure to match the data representing the distribution of the markers, where matching the data representing the tissue structure to the data representing the distribution of the markers is preferred over the other way around, as transforming the data representing the distribution of the markers is likely to distort the measure of the marker signal.

[0037] According to a further aspect, there is provided a computer program comprising instructions which, when executed by a computer, cause the computer to carry out the method.

[0038] According to a further aspect, there is provided a computer-readable medium comprising instructions that, when executed by a computer, cause the computer to perform the method.

[0039] According to a further aspect, there is provided an apparatus for determining an indicator of inflammation in a body region of a subject, comprising a processor configured to perform the steps of the method. [Brief explanation of the drawings]

[0040] Embodiments of the present invention will now be described, by way of non-limiting example only, with reference to the accompanying drawings in which corresponding reference characters indicate corresponding parts and in which several embodiments of the present disclosure are illustrated. [Figure 1] 1 is a flow chart illustrating a method for determining an index of disease. [Figure 2] 10 is a flowchart showing details of a method for correcting an index of a marker signal. [Figure 3a] FIG. 1 shows the alignment of data representing tissue structure with data representing marker distribution. [Figure 3b] FIG. 1 shows the alignment of data representing tissue structure with data representing marker distribution. [Figure 4] 1 is a detailed flowchart of a process used in a specific embodiment. [Figure 5a] Box plots of median counts at different time points. [Figure 5b] Box plots of median counts at different time points. [Figure 6a] FIG. 1 shows normalized median counts at different time points. [Figure 6b] FIG. 1 shows normalized median counts at different time points. [Figure 6c] FIG. 1 shows normalized median counts at different time points. [Figure 6d] FIG. 1 shows normalized median counts at different time points. [Figure 6e] FIG. 1 shows normalized median counts at different time points. [Figure 7] FIG. 10 shows the difference in vessel density calculated using scans from different time points. [Figure 8a] FIG. 1 shows the effect of vessel density on median normalized counts. [Figure 8b] FIG. 1 shows the effect of vessel density on median normalized counts. [Figure 9a] FIG. 10 shows the effect of reduced tissue density on median normalized counts. [Figure 9b] FIG. 10 shows the effect of reduced tissue density on median normalized counts. [Figure 9c]FIG. 10 shows the effect of reduced tissue density on median normalized counts. [Figure 10a] Box plot of median normalized counts corrected for the effect of vascular flow. [Figure 10b] Box plot of median normalized counts corrected for the effect of vascular flow. [Figure 11a] Box plot of median normalized counts corrected for the effect of reduced tissue density. [Figure 11b] Box plot of median normalized counts corrected for the effect of reduced tissue density. [Figure 11c] Box plot of median normalized counts corrected for the effect of reduced tissue density. [Figure 11d] Box plot of median normalized counts corrected for the effect of reduced tissue density. [Figure 12a] FIG. 1 shows the marker signals in the right lung of two study participants. [Figure 12b] FIG. 1 shows the marker signals in the right lung of two study participants. [Figure 13] 1 is a flowchart of an example workflow of a software platform. [Figure 14] 14a and 14b illustrate the possibility of implementing the method as a clinical platform. DETAILED DESCRIPTION OF THE INVENTION

[0041] New biological therapies are becoming increasingly widely available for a variety of diseases, including cancer and inflammatory disorders such as asthma or rheumatoid arthritis. These therapies can be used not only to treat disease but also for diagnostic applications, such as screening for cancer or identifying tumor types or infectious agents, without the need for invasive biopsies.

[0042] While biologic treatments are highly effective, not all patients respond equally to the same treatment. Biologics, such as monoclonal antibodies, target specific arms of disease-causing pathways, the importance of which varies from patient to patient. These drugs are expensive, and there is an urgent need for methods to determine which biologic is best for a particular patient in order to provide effective treatment from the start. In addition to reducing associated healthcare costs, ensuring patients receive the most effective treatment as early as possible could significantly reduce the burden of disease.

[0043] In vivo molecular imaging offers the possibility to evaluate biological processes in individuals suffering from diseases such as chronic inflammatory disorders. This new approach to precision medicine may enable clinicians to select disease-modifying therapies and deliver appropriate treatment the first time without invasive procedures. However, a major limitation in the clinical application of molecular imaging is the limited availability of imaging techniques such as X-rays, MRIs, and CT scans. (16) The challenges are the significant complexity of the imaging analysis required compared to anatomical medical imaging such as MRI, and the ability to quantify biological signals caused by the abundance of biological targets such as inflammatory cytokines or molecular immune targets within an organ.

[0044] This method allows for the quantification, visualization, and understanding of disease biology in vivo using a non-invasive approach. This method can form the basis of an imaging platform that can determine the activation of specific biological pathways in organ systems. This method can also predict patient treatment response in clinical settings and optimize the selection of specific treatments to which an individual will respond.

[0045] 1 is a flow chart illustrating a method for determining a measure of disease in a region of a subject's body. For example, the disease may be an inflammatory disease and the measure is a measure of inflammation. Alternatively, the disease may be an infectious disease, cancer, or a specific type of cancer.

[0046] The method includes receiving S10 imaging data 10 obtained by imaging a region of a subject's body. The region may be any region of interest where the level of disease is to be determined or the effectiveness of a treatment is to be monitored. For example, the region of the subject's body may include an organ of the subject. This may be, for example, one or both of the subject's lungs or a joint of the subject (e.g., a knee joint or a knuckle). Lung monitoring may be desirable for assessing COPD, and joint monitoring may be desirable for assessing rheumatoid arthritis (RA).

[0047] The imaging data 10 includes data representing the distribution of markers within a region administered to a subject prior to imaging.

[0048] Data representing the distribution of the marker within a region can be obtained using any suitable imaging technique capable of detecting the marker administered to the subject. For example, data representing the distribution of the marker within a region can be obtained using either single-photon emission computed tomography (SPECT), positron emission tomography (PET), planar scintigraphy, or magnetic resonance imaging (MRI). Combinations of these imaging modalities can also be used, such as SPECT-CT, PET-CT, PET-MRI, and SPECT-MRI. These options are advantageous because they utilize established imaging approaches that are familiar to clinicians and already utilized in many settings.

[0049] Optionally, and as described further below, the imaging data 10 may further include data representing tissue structure within the region. The data representing tissue structure may be obtained by one or more of low-dose computed tomography, high-resolution computed tomography, multi-slice computed tomography, MRI, plain radiography, ultrasound, and Doppler ultrasound. Any suitable combination of methods may be used to obtain the data representing tissue structure and the data representing the distribution of markers. The data representing tissue structure may be used to determine the volume of a body region, particularly if the imaging data covers more of the body than the region in which disease indicators are to be determined.

[0050] The choice of marker depends on the imaging technique used to obtain data representing the distribution of the marker. For example, markers include radioactive markers (also called tracers or tracer agents). If MRI is used, markers include magnetic markers or MRI contrast agents.

[0051] The data representing the distribution of the marker within the region may include an index for each of a plurality of voxels within the region, for example, if the marker includes a radioactive marker, the index may be an activity count for each voxel within a predetermined time period.

[0052] The marker binds to a biological target associated with the disease. The biological target may be a component of a biological pathway, such as a biological pathway known to be associated with the disease. For example, if the disease is an inflammatory disease, the biological target may be a component of an inflammatory pathway. If the disease is cancer, the biological target may be a cancer protein, e.g., a protein associated with the particular type of cancer being detected and / or treated. If the disease is an infectious disease, the biological target may be an infectious pathogen, such as a virus or bacterium.

[0053] Biological targets may include immune and / or inflammatory signaling proteins, such as cytokines, chemokines, cell surface receptors, and extracellular matrix components. One specific example used in the examples below is tumor necrosis factor, TNF. TNF-α is a pro-inflammatory cytokine released by macrophages and airway epithelial cells in the lungs of COPD patients. It is known that TNF-α concentrations are elevated in the sputum of COPD patients. (14) TNF-α plays a central role in many inflammatory conditions, including the secretion of matrix metalloproteinases (MMPs) and fibroblast proliferation, and may also be involved in the cachexia and weight loss experienced as part of the disease.

[0054] The marker may comprise a monoclonal antibody. Monoclonal antibodies have the advantage of binding to highly specific targets. For example, when assessing COPD, the marker may comprise an anti-TNF-α monoclonal antibody.

[0055] The method includes processing the imaging data to obtain an index of the marker signal in the region S20. The index of the marker signal may be an index representative of the entire region. If the data representing the distribution of the marker in the region includes measurements of multiple voxels in the region, obtaining the index of the marker signal may include combining measurements from two or more of the voxels. The index of the marker signal may be the average marker signal for the region, or optionally, the median marker signal. Obtaining the index of the marker signal may further include other standard techniques and corrections appropriate for the imaging modality used to acquire the data. For example, when using an imaging modality whose spatial resolution is lower than or close to the size of the feature being measured, a correction may be applied for partial volume effects. For example, this type of correction may be used when PET or SPECT is used.

[0056] The marker signal index can be normalized to the background marker signal, which corresponds to the marker signal from unbound marker in the blood. The use of the target / background ratio has been described in previous literature. (8,9) This can be used to normalize the biological clearance of the marker when measurements are taken at different times after the marker is administered to the subject and / or to allow comparisons between different subjects who may have different rates of biological clearance.

[0057] The background marker signal can be distinguished from the marker signals of major vessels, optionally the aortic arch. The background marker signal can be determined using voxels corresponding to major vessels in data representing the distribution of markers within a region. If the major vessels are not within the region from which imaging data is acquired, the background marker signal can be determined using other data, which should be measured as close in time as possible to the imaging data. The background marker signal can be the mean marker signal of the major vessels, or optionally, the arithmetic mean marker signal. The background marker signal can also be expressed per unit volume.

[0058] The method includes correcting S30 the indices of the marker signals for the effect of tissue structure within the region on the marker signal. The corrected indices of the marker signals can represent the level of binding of the marker to tissue within the region. In this manner, the correction determines "tissue-bound" markers as meaningful indicators of inflammatory activity. Step S30 of correcting the indices of the marker signals is described in further detail below.

[0059] The method includes determining S40 a disease index of the region using the corrected index of the marker signals. The disease index can represent the level of disease in the region. For example, if the disease is an inflammatory disease, the disease index can represent the level of inflammation in the region. The disease index can represent the amount of a biological target. The corrected index of the marker signals can be used directly as the disease index. Optionally, the corrected index of the marker signals can be subjected to further processing, such as normalization to a normal or average amount of the biological target, e.g., calculated from measurements of healthy subjects, to obtain the disease index.

[0060] Correcting the indices of the marker signals S30 may include correcting the marker signals by the vascular flow rate of the region, thereby allowing the method to correct for pulmonary vascular density / perfusion in the region.

[0061] The inventors have found that the number of blood vessels per unit volume (or vascular density) correlates with the signal detected within a region. Higher background vascular flow of marker through tissue within a region can result in a higher marker signal, as unbound marker in the blood provides additional signal. This additional signal reduces the usefulness of the uncorrected marker signal as an indicator of disease. The additional signal increases the marker signal without indicating a higher level of the biological component. Additionally, higher levels of available marker can result in a higher amount of marker locally binding to the tissue compared to regions with lower vascular flow, even if the underlying disease level within the tissue is similar. Therefore, by correcting the marker signal for vascular flow, the method can provide a more accurate indicator of disease.

[0062] 2 is a flow chart showing further details of an implementation of step S30 of correcting a measure of the marker signal for the effect of tissue structure in the region on the marker signal. Steps S110 and S120 correspond to correcting the marker signal for vascular flow in the region. Steps S210 and S220 correspond to correcting the marker signal for tissue density in the region. Following steps S110, S120, S210, and S220, the method includes applying the correction to the marker signal at S230.

[0063] Steps S110 and S120 are shown in FIG. 2 as being performed in parallel with steps S210 and S220. However, steps S110 and S120 may instead be performed before or after steps S210 and S220, or steps S110 and S120 may be omitted entirely. Step S230 of applying a correction to the marker signal follows both S120 and S220. This reflects the fact that step S230 of applying a correction to the marker signal can use one or both of vascular flow and tissue density corrections, depending on the particular implementation.

[0064] In the embodiment of Figure 2, correcting the marker signal for vascular flow includes determining S110 a vascular density in the region. Determining vascular density S110 can include determining a total volume of blood vessels in the region. Blood vessel density can be defined as the total volume of blood vessels in the region divided by the total volume of the region.

[0065] The vessel density may be determined by any suitable means based on the imaging data 10 and / or based on other data sources about the patient, such as known or expected characteristics of undetected small blood vessels. In particular, the imaging data 10 may further include data representative of tissue structure within the region. In this case, determining the vessel density S110 includes identifying blood vessels within the region using the data representative of tissue structure.

[0066] Blood vessels can be identified using automated segmentation techniques applied to data representing tissue structures. Automated segmentation may also be used to determine the volume of a body region from data representing tissue structures. Automated segmentation techniques can be implemented using machine learning algorithms. Automated segmentation techniques can identify structural features of tissue, such as the vessels and fissures of the lungs, and provide a representation of the vascular tree within the region. Automated segmentation techniques can utilize prior shape models to detect anatomical regions in particular organs or body regions and / or to assist in identifying the vasculature in the image data 10.

[0067] Determining vessel density S110 can also take into account data from other sources, such as scans of tissue specimens previously excised from the same region of the body (either from the subject or from other individuals). Such data can provide further information regarding the expected density of small vessels that may not be detected by clinical imaging of the subject. These data can be incorporated into the determination of vessel density, for example, by providing an estimate of the typical additional volume of vessels that may not be identifiable from the imaging data 10 alone.

[0068] 2, correcting the marker signal for vascular flow in the region further includes determining S120 a background marker signal corresponding to the marker signal from unbound marker in the blood, which may be obtained as described above, for example, using marker signals in major vessels such as the aortic arch.

[0069] Once the background signal and blood vessel density have been determined, they can be combined to estimate the signal due to unbound markers of the vasculature. The fraction of the area occupied by blood vessels can be assumed to be equal to the blood vessel density, and multiplied by the background signal to determine the expected signal due to the vasculature.

[0070] 2, applying a correction to the marker signal S230 includes subtracting the signal due to the vasculature from the index of the marker signal. A specific embodiment is provided by the following equation:

number

[0071] where N' T is the normalized index of the marker signal corrected for vascular flow, M AO is the background marker signal, D V is the blood vessel density of the body region, M T is a measure of the marker signal, in this case the mean (median) marker signal of the region.

[0072] In this case, the background signal M AO is the value per volume,

number

number

[0073] Equation (3) can be explained by considering a hypothetical situation where the entire region is filled with blood and no tissue. If this is the case, the index of the marker signal from the region is expected to be equal to the background signal, so the normalized index of the marker signal, N T becomes 1. In this case, the blood vessel density D Vwill also be 1 (because 100% of the area is filled with blood) and the corrected index of the marker signal will be zero.

[0074] Correcting the index of the marker signals S30 may include correcting for tissue density of the region.

[0075] Some experiments identified the presence of emphysema in COPD patients as a factor that may affect the marker signal index. Based on this, correction of the marker signal index S30 may include correction for regional tissue density, such as variations caused by the presence of emphysema. Investigations identified the presence of emphysema as a confounding factor. A strong correlation was observed between the marker signal and the degree of emphysema.

[0076] Without wishing to be bound by theory, it is hypothesized that in areas of reduced tissue density, markers cannot bind as easily or effectively to their biological targets, and vascular flow through the tissue is also affected. The correction of tissue density likely represents a combination of factors, including loss of small blood vessels, loss of small tissue structures (e.g., airways and airway epithelium in the lungs), and loss of tissue density. All of these factors likely mediate the marker signal, either due to the presence of biological targets within the tissue or due to background movement of the marker (e.g., diffusion of the marker within the tissue).

[0077] As mentioned above, steps S210 and S220 of Figure 2 correspond to correcting the marker signal for vascular flow within the region. Steps S210 and S220 are shown in Figure 2 as being performed in parallel with steps S110 and S120. However, as noted above for steps S110 and S120, steps S210 and S220 may instead be performed before or after steps S110 and S120, or steps S210 and S220 may be omitted entirely. Step S230, which applies a correction to the marker signal, may use one or both of the vascular flow and tissue density corrections, depending on the specific implementation.

[0078] 2, the imaging data 10 further includes data representative of tissue structure within the region, and correcting for tissue density includes identifying subregions of reduced tissue density within the region using the data representative of tissue structure S210. The data representative of tissue structure may be any suitable data, such as those described above.

[0079] For example, if data representing tissue structure is obtained using X-rays, a subregion may be identified as having reduced tissue density if the attenuation of the X-rays in the subregion is below a predetermined threshold, which may be selected based on the typical attenuation of healthy tissue in the region of the body, for example, as determined from indices of other healthy subjects.

[0080] In the embodiment of FIG. 2, correcting for tissue density includes correcting the marker signal index based on the percentage of tissue in the hypodensity region. This is accomplished by determining the percentage of tissue in the hypodensity region S220 and correcting the marker signal index based on that percentage. In the case of emphysema, a commonly used index of the percentage of tissue in the hypodensity region is -950 Hounsfield units (%LAA -950 ) is the proportion of low attenuation areas in the

[0081] 2, applying a correction to the marker signal S230 includes applying a correction to the index of the marker signal that is proportional to the proportion of tissue in the hypodensity region. A specific embodiment is provided by the following formula:

number

[0082] Correction of the marker signal index based on the percentage of tissue in the hypodensity region can be determined using marker signal measurements in multiple samples with different percentages of tissue in the hypodensity region. A correction factor can be determined by fitting (e.g., using linear regression) the marker signal index (uncorrected, but optionally normalized) as a function of the percentage of tissue in the hypodensity region. For example, using linear regression on the normalized marker signal index yields a linear fit of the form:

number

[0083] Different correction factors can be applied depending on the type of individual measurements used to obtain the correction factor. In particular, the timing of measurements after administration of the marker to an individual can affect the correction factor due to changes in the level of unbound marker in the subject's blood.

[0084] Late time points (e.g., 24 hours post-dose) are generally preferred for determining correction factors because this is the time point at which the marker is more likely to be bound to the biological target or at least evenly distributed throughout the body. However, earlier time points (e.g., 6 hours post-dose) may be preferable in some circumstances because higher levels of unbound marker in the blood may allow for more accurate background correction.

[0085] As described above, the present method can use imaging data 10 that includes data representing tissue structure in addition to data representing marker distribution. In this case, different imaging techniques or modalities may be required to acquire the data representing tissue structure and the data representing marker distribution. Furthermore, the scans used to acquire the imaging data 10 are often time-consuming and may require the subject to rest between scans acquiring different portions of the imaging data 10.

[0086] For this reason, the data representing the tissue structure and the data representing the marker distribution may have different resolutions and / or volumetric divisions, may be measured in different states of the subject's body, and / or may be measured at different times. In this case, the method may further include aligning the data representing the tissue structure and the data representing the marker distribution. This allows the method to take into account factors such as the subject's position relative to the imaging device (e.g., if the subject stands or moves around between scans) and organ movement as part of a physiological process (e.g., respiratory motion, cardiac motion).

[0087] Preferably, the matching includes transforming the data representing the tissue structure to match the data representing the distribution of the markers. This can be achieved by dividing the body region into voxels of data representing the tissue structure and mapping these to the data representing the distribution of the markers. Transforming the data representing the tissue structure is preferred over the data representing the distribution of the markers, as this results in less distortion of the marker signal indices. Once the regions have been mapped in this manner, the marker signals within each region are quantified to obtain marker signal indices, which are further analyzed to determine the disease indices as described above. The matching includes using a non-rigid registration algorithm.

[0088] Figure 3 shows an example of aligning two data sets. Figure 3a shows two overlapping lung data sets, one acquired during tidal breathing and the other during maximum inspiration. Figure 3b shows how the two data sets are aligned after applying a non-rigid registration algorithm.

[0089] The alignment may also include other processing such as downsampling one of the data representing the tissue structure and the data representing the distribution of the markers, which is particularly useful when the two types of data have different resolutions.

[0090] The imaging data 10 can also include data obtained from tissue samples simultaneously collected from the subject being studied. For example, measuring lung disease can be achieved using bronchoscopy and lung biopsies from the subject, or by including patients scheduled for lung resection surgery and imaging those participants prior to intraoperative tissue collection for laboratory study. These additional data, optionally including other data such as immunohistochemical detection of biological targets within the same tissue (if appropriate for the biological target under consideration), can be used to calibrate correction factors used for both vascular flow and tissue density. For example, they can be used to improve estimation of the proportion of hypodensity tissue or to train algorithms used for segmentation to identify blood vessels in regions of the body.

[0091] In another variation of the method, multiple markers can be used simultaneously. In such an embodiment, the imaging data includes data representing the distribution within the region of each of multiple markers administered to the subject prior to imaging, each of the multiple markers binding to a different biological target. Processing the imaging data S20 includes processing the imaging data to obtain an indication of the marker signal for each of the multiple markers. For example, each of the markers can include a different radioisotope, so that activity from each marker can be separated in the imaging data 10. For example, SPECT-CT can individually determine the activity of different radioisotopes by detecting the specific energy of photons emitted from each marker (processing "windowing" to specific energies), thereby detecting the specific binding activity of each marker and, therefore, the subject's most active biological target and / or pathway.

[0092] Correcting the indices of the marker signals S30 includes correcting each of the indices of the marker signals, and determining the disease indices S40 uses the corrected indices of the marker signals.

[0093] Determining a disease index using the corrected multiple indices of marker signals S40 can include integrating the corrected multiple indices into a single disease index. For example, biological targets that exhibit increased activity due to disease may differ between different diseases and / or subjects. Thus, determining an integrated disease index based on multiple corrected indices may be more indicative of the overall activity of relevant biological pathways or a particular endotype of disease.

[0094] Determining disease indicators using multiple correct indicators S40 may alternatively or additionally include determining separate disease indicators for each biological target. By simultaneously using multiple markers, each binding to a different biological target (examples include various cytokines or cell surface receptors), the method can simultaneously detect and quantify the activity of each biological target. This potentially allows for the simultaneous assessment of multiple diseases or disease subtypes.

[0095] It is also possible to simultaneously assess the effectiveness of different therapeutic approaches in binding to biological targets, such as when markers are conjugated to therapeutic agents such as monoclonal antibodies. While disease treatments can be highly effective, not all patients respond equally to the same drugs. This can be due to varying levels of different biological targets, particularly the specific targets targeted by the therapy. Determining disease indicators using multiple different markers that bind to different targets can be used to determine which targets should be targeted for therapy in an individual subject.

[0096] Another potential application of co-injecting multiple markers is to examine nonspecific signals from markers that may have diffused into tissues in a body region but are not bound to their target component. One of the markers may be a specific marker that targets the biological target of interest, while the other may be a "negative control" marker that is not expected to exhibit specific binding to any target in the body region. The negative control marker provides data on how the marker behaves in tissues without binding to the biological target, and therefore how much of the signal detected from a particular marker is likely to be bound to the biological target.

[0097] The method may be performed by an apparatus for determining an indicator of disease in a region of a subject's body, the apparatus comprising a processor configured to perform the steps of the method. The apparatus comprises a receiving unit for receiving imaging data obtained by imaging a region of the subject's body, the imaging data including data representing the distribution in the region of a marker administered to the subject prior to imaging, the marker binding to a component of an inflammatory pathway. The apparatus comprises a processing unit configured to process the imaging data to obtain an indicator of marker signal in the region. The apparatus comprises a correction unit configured to correct the indicator of marker signal for effects on the marker signal of tissue structures in the region. The apparatus comprises a determination unit configured to determine an indicator of inflammation in the region using the corrected indicator of marker signal. The apparatus may be a general-purpose computer configured to perform the method. The configuration of the apparatus may be modified as appropriate to accommodate any details of the above-described method.

[0098] [Example] The following example demonstrates the applicability of this method to the specific case of using a marker that binds to TNF-α to determine an index of inflammation in subjects with COPD.

[0099] [Selection of participants for SPECT-CT studies] This study, conducted at Southampton University Hospital in the UK, recruited five participants with severe to very severe COPD (according to the Gold and White Lung Disease (GOLD) criteria) and five participants with no history of underlying lung disease. Participants with COPD had a previous clinical diagnosis of COPD, a smoking history of more than 10 pack-years, and severe or very severe COPD according to the Gold and White Lung Disease (GOLD) criteria, with a post-bronchodilator FEV1 / FVC < 0.7 and an FEV1 < 50% predicted. Healthy volunteers were nonsmokers with no history of lung disease and a smoking history of less than one pack-year. Participant characteristics are shown in Tables 1–3.

[0100] Participants were excluded if they had a history of other respiratory illnesses, pneumonia risk factors, chronic kidney disease (CKD) with an estimated glomerular filtration rate <60, autoimmune disease, use of immunosuppressants or other monoclonal antibodies, a history of adverse reactions to monoclonal antibody medications, a body mass index (BMI) outside the range of 18–30, an indwelling urinary catheter, or regular antibiotic, antiviral, or respiratory investigational medication use within 30 days prior to the visit. Additionally, COPD subjects were required not to have experienced a symptom exacerbation requiring treatment with oral corticosteroids within 30 days prior to enrollment.

[0101] After recruitment, all participants underwent spirometry tests before and after bronchodilator administration, diffusing capacity for carbon monoxide (DLCO), and lung volumes measured by body plethysmography. Participants were instructed to discontinue their usual bronchodilator medications 12 hours before administration, if necessary. Anthropometric measurements and bioelectrical impedance analysis were also performed during this visit to determine body composition. Participants underwent blood sampling and TNF-α concentration measurement for safety assessment before imaging. Sputum induction was also performed to collect TNF-α in sputum before and after imaging, if possible within COVID-19 infection control guidelines.

[0102] [Table 1]

[0103] [Table 2]

[0104] [Table 3]

[0105] [ 99m’ Radiolabeling of anti-TNF-α with Tc Radiolabeling was performed at the radiopharmacy of University Hospital Southampton. Infliximab, an anti-TNF-α monoclonal antibody (Remicad®, Janssen Biotech), was used as previously reported. (10,11) Using the direct labeling method described in 99m The anti-TNF-α antibody was first purified by gel filtration chromatography and then isolated based on the optical density at 280 nm using a UV spectrophotometer. The antibody was then reduced with a molar excess of 2-mercaptoethanol to make free thiol groups available for labeling. After a 30-minute incubation with 2-mercaptoethanol, the reduced antibody was isolated using a PD-10 desalting column.

[0106] A methylene diphosphate (MDP) bone scanning kit (Draximage and Polatom) was reconstituted with 5 ml of 0.9% sodium chloride to yield a solution containing 5 mg of medronate, 0.34 mg of stannous fluoride, and 2 mg of para-aminobenzoic acid. 99 Mo / 99m The antibody was eluted from the Tc generator and obtained as sodium pertechnetate. 500 mcg of reduced antibody was added to the solution, followed by 450 MBq of pertechnetate to generate 99mTc-anti-TNF-α.

[0107] Radiochemical purity was measured using instant thin-layer chromatography (iTLC) paper strips by the cut-and-count method. The stationary phase was iTLC, and the mobile phase was 0.9% NaCl. Under these conditions, the Rf value of the labeled antibody was 0, and the Rf value of the impurities was 1. Radiochemical purity was maintained above 95% with this approach, except for one participant whose radiochemical purity was 91% (Table 4).

[0108] [Table 4]

[0109] [Radiation Dose Research] Radiation doses were analyzed for the first two participants in the study (one from each group). The estimated effective whole-body dose was 1.6–1.7 mSv (equivalent to 1.6–1.9 mSv at 370 MBq), well below the upper limit of 3.6 mSv allowed by the study protocol. The whole-body residence time was approximately 1.6 mSv in the healthy group, and the biological time 1 / 2 23.7 hours (effective t 1 / 2 4.8 hours in the COPD group and 19.5 hours in the COPD group (effective t 1 / 2 was calculated to fit the infliximab (12) This behavior most closely resembles that expected from an intact antibody of size 150 kDa.

[0110] [SPECT-CT imaging and biodistribution studies] Imaging was performed 6 hours (+ / - 60 minutes) and 24 hours (+ / - 4 hours) after the start of the infusion. Participants in both the healthy volunteer and COPD groups underwent whole-body planar imaging, chest SPECT imaging, and low-dose chest CT at each time point. Participants in the COPD group underwent an additional high-resolution CT (HRCT) of the chest 6 hours later. The first participant in each group also underwent whole-body planar imaging 3 hours (+ / - 30 minutes) after the infusion for additional radiation dose calculations.

[0111] All imaging was performed using a Symbia Intevo Bold (Siemens Healthcare GmbH, Germany) 16-row, dual-head gamma camera system. Whole-body planar imaging used a 256 × 1024 matrix with an acquisition speed of 15 cm / min for 3 h, 10 cm / min for 6 h, and 5 cm / min for 24 h. Six-hour SPECT imaging was performed with a 256 × 256 matrix, 60 views, and 20 seconds per view. Twenty-four-hour SPECT imaging was performed with a 256 × 256 matrix, 60 views, and 40 seconds per view.

[0112] SPECT imaging was performed with patients in a supine position with their hands on their head. Participants remained in this position for CT imaging, which was performed immediately after SPECT imaging. Free-breathing low-dose CT (LDCT) was performed in all participants at both time points. Inhalation and exhalation HRCT images were obtained for COPD patients only at the 6-hour time point.

[0113] [Imaging processing and analysis] After imaging was performed, the images were first stored using the hospital's Picture Archiving and Communication System (PACS), then all personal identifying information was removed and they were transferred to a workstation for further analysis.

[0114] CT data, both MSCT (multislice CT) and low-dose, were first analyzed using Southampton Lung Radiomics (SPR) software (version beta-e3b7ef8254). This analysis consisted of the following: 1) Manual identification of seed points near the upper trachea. 2) Automatic airway segmentation followed by semi-automatic editing to properly segment all necessary branches. 3) Automatic segmentation of left and right lungs. 4) Manual labeling of airway branches, especially the right and left main bronchi. 5) Semi-automated segmentation of lung lobes and fissures. 6) Automated analysis of lung lobes to identify areas of emphysema based on a -950 HU threshold. 7) Vessel segmentation using a fully automated, AI-driven approach.

[0115] The corresponding low-dose CT scan was used to manually identify the location of the aortic arch and place a spherical region of interest (RoI). The size and location of the RoI were manually adjusted for each case to capture as much of the aortic arch volume as possible.

[0116] Because the voxel dimensions of the SPECT and LDCT scans differ, regions identified in the LDCT scans were downsampled to match the dimensions of the corresponding SPECT scans. The downsampled regions were then used to directly mask out the regions of interest from the SPECT data.

[0117] However, the use of these regions (corresponding to the lung lobes and the entire left and right lungs) was complicated because these scans were acquired during stopped, full inspiration, whereas the SPECT scans were acquired during tidal breathing. As a result, significant differences in lung size and shape existed between the two scans, precluding the direct use of MSCT regions. This problem was resolved by employing a nonrigid registration algorithm that aligned regions identified in the MSCT scan with corresponding features identified in the low-dose CT scan. Because the low-dose CT and SPECT scans were automatically spatially aligned by the scanner, the registered MSCT regions could be assumed to be aligned with the corresponding regions in the SPECT scan.

[0118] Nonrigid registration was performed for each lung lobe separately to ensure maximum alignment accuracy and conserve computational resources. The binary mask obtained from each lobe segmentation was used to isolate the MSCT voxels corresponding to that lobe alone. Next, a volume corresponding to the isolated lobe alone was extracted from the entire MSCT scan, resulting in a smaller, more manageable volume on which further processing was performed. Zero padding of 10 voxels in each dimension was added to the isolated volume. An initial affine rigid registration was then calculated using a 1 + 1 evolutionary optimizer and the Mattes mutual information registration metric. Following the initial rigid registration, nonrigid registration was performed using a Daemons-based algorithm with cumulative field smoothing, five pyramid levels, and 100 iterations per level. This process was found to result in excellent alignment between features identified in the MSCT scan and corresponding regions in the low-dose CT and SPECT scans.

[0119] All further processing (including correction of marker signal indices for the effects of vascular and tissue density) was performed using Matlab (version 2021a, The Mathworks Inc, Natick, MA) according to the methods described above. A detailed flowchart of the processing used is shown in Figure 4.

[0120] [Statistical analysis] Because voxel counts were non-normally distributed, median normalized counts were used as summary statistics for each patient. Student's t-test was used for between-group comparisons. Pearson's correlation coefficient was used to correlate median normalized counts with other factors.

[0121] 〔result〕 First, raw counts were determined from the SPECT images within the region of interest defined by the LDCT image taken during the SPECT scan. The raw counts in this example correspond to the marker signal indices described above. Counts were averaged across the entire ROI. Because the distribution of counts was non-normal across the examined volume, median counts were used as summary statistics for each participant. Boxplots depicting these results are shown in Figure 5.

[0122] Figure 5a shows that these mean values ​​+ / - SD at 6 hours were 5725.0 + / - 1121.3 for the healthy group and 3182.0 + / - 631.7 for the COPD group. Figure 5b shows that at 24 hours, the mean values ​​were 2900.0 + / - 471.5 for the healthy group and 1864.0 + / - 282.6 for the COPD group. Raw counts were consistently higher in the healthy group at both time points.

[0123] Analysis of MSCT scans yielded much more detailed regions than the corresponding low-dose CT scans. Normalization to the ROI area / volume used was applied. (15) .

[0124] The downsampled aortic arch Region of Interest (ROI) was used to isolate SPECT voxels corresponding to blood within the aortic arch. The sum and mean of these voxel values ​​were calculated and used as an index of the "background" level of activity.

[0125] The sum, mean, and median values ​​were calculated in a similar manner for each downsampled region identified from the low-dose CT scan (corresponding to the lung lobes and the entire left and right lungs). Furthermore, to normalize for biological clearance, a target-to-background (T / B) ratio was formed to calculate normalized counts. This was done by dividing each voxel value by the average activity identified in the aortic arch. Raw voxel counts were converted to MBq units by multiplying the count value at each voxel by the spatial volume of the voxel and dividing by 1 million.

[0126] Normalized counts were calculated on a regional and bipulmonary basis at both 6 and 24 hours. Because the distribution of normalized counts per voxel was non-normal, median counts were calculated as summary statistics for each participant.

[0127] Figure 6a shows a boxplot of the distribution of median normalized counts in the healthy and COPD groups at 6 hours. The mean (+ / - standard deviation) of the summary statistic was 0.182 + / - 0.027 in the healthy group, while it was 0.087 + / - 0.019 in the COPD group.

[0128] Figure 6b shows boxplots of the distribution of median normalized counts in the healthy and COPD groups at 24 hours (B). The mean summary statistics were 0.250 + / - 0.089 in the healthy group and 0.147 + / - 0.056 in the COPD group.

[0129] Figure 6c shows the difference in normalized counts between a healthy subject (left) and a subject with COPD (right).

[0130] A significant difference was observed between groups, with median normalized counts being higher in the healthy group at 6 hours (p<0.001) but not at 24 hours, and this difference was attributed to factors such as vascular flow and tissue density as previously discussed.

[0131] Figure 6d shows that the absolute difference in median normalized counts detected at 6 and 24 hours was not significantly different between groups, and Figure 6e shows that the increase in median normalized counts compared to the 6-hour scan was greater in the COPD group, but the difference did not reach statistical significance.

[0132] The difference in median normalized counts for each participant between the 6-hour and 24-hour scans was also calculated. Median normalized counts increased at 24 hours in both groups. The absolute difference + / - SD in median normalized counts was 0.067 + / - 0.073 in the healthy group and 0.059 + / - 0.040 in the COPD group. The difference between groups was not statistically significant. When the difference in median normalized counts was calculated as a percentage of each patient's 6-hour scan, the increase in median normalized counts was greater in the COPD group. Median normalized counts increased by an average of 35.38% + / - 34.33 in the healthy group and 64.88% + / - 31.04 in the COPD group, but the difference between groups did not reach statistical significance.

[0133] When calculated as a percentage of the 6-hour scan, the difference in median normalized counts was greater in the COPD group, which may be an indication of more specific TNF-α binding at 24 hours in this group.

[0134] Previous literature suggests that small vessels detectable on CT disappear with progression of airflow obstruction and emphysema. (13) Therefore, variation in vascular density is to be expected in the study population.

[0135] Vascular density was determined using the automated segmentation technique described above for scans acquired at 6 and 24 hours. Some variability was observed between vascular density determined using scans at 6 and 24 hours, but this was generally small. The mean ± SD for both lungs was 0.109 ± 0.028 (range 0.069-0.158) at 6 hours and 0.112 ± 0.033 (range 0.070-0.171) at 24 hours.

[0136] Figure 7 shows that when examining the individual components of the calculation, vascular volume differs minimally compared to total lung volume measured by CT, and that the difference in vascular density between scans is due to changes in total lung volume.

[0137] To examine the effect of detectable vascular flow through lung tissue on SPECT activity, the mean vascular density detected by CT was plotted against the median normalized count detected by SPECT. Figure 8a shows the plot at 6 hours, and Figure 8b shows the plot at 24 hours. A significant positive correlation was observed at both time points, with Pearson correlation coefficients of 0.824 (p = 0.003) at 6 hours and 0.862 (p = 0.001) at 24 hours. This supports the conclusion that vascular density influences marker signal metrics and supports the validity of the method described above.

[0138] The proportion of areas showing decreased tissue density varied in the study population. Specific examples include decreased tissue density due to emphysema in patients with COPD. Figure 9a shows the %LAA determined by high-dose inspiratory CT scans. -950 1 shows the variation in emphysema score, expressed as a CT index of tissue density indicating the presence of emphysema, among subjects with COPD.

[0139] %LAA -950The values ​​were plotted against the median normalized counts detected by SPECT. Figure 9b shows the results at 6 hours, and Figure 9c shows the results at 24 hours. A clear negative correlation was observed in the HRCT images at both time points. HRCT data were only available for participants with COPD. At 6 hours, the Pearson correlation coefficient was -0.884 (p = 0.047), and at 24 hours, Pearson's r was -0.954 (p = 0.012).

[0140] To control for the effect of vascular density, the method described above was applied. Figure 10a shows a boxplot of the median normalized counts at 6 hours after correcting for vascular flow using the method described above. Figure 10b shows the corresponding corrected counts at 24 hours. Participants' median normalized counts decreased by an average of 0.110 + / - 0.030, but were overall higher in the healthy group compared to the COPD group. With continued improvements in segmentation algorithms that can detect a higher proportion of the entire vascular tree from LDCT images, larger effects may be detectable.

[0141] To correct for the effect of decreased tissue density due to emphysema, the %LAA was calculated for each participant in the COPD group to determine the degree of emphysema. -950 Analysis of inspiratory MSCT using a density mask was first performed.

[0142] %LAA -950 The values ​​of %LAA were determined using a density mask of identified lung regions in the HRCT images. HRCT data were only available for the COPD participants (HRCT was not part of the scanning protocol for the healthy volunteers). -950 The results were plotted against median normalized counts and correlated by linear regression. The resulting regression coefficients were calculated as the correlation between median normalized counts and %LAA, as described above. -950 was used to adjust the regression line to the value expected if σ was zero. The effectiveness of this correction method depended on whether the regression line was determined from the 6-hour or 24-hour results.

[0143] The box plots in Figures 11a and 11b show that when the equation for the 6-hour regression line was used, the adjusted counts for the healthy group remained elevated at both the 6-hour (Figure 11a) and 24-hour (Figure 11b) time points. Using the 6-hour regression line, the adjusted counts at 6 hours were (mean + / - SD) 0.182 + / - 0.027 for the healthy group and 0.129 + / - 0.009 for the COPD group. At 24 hours, this was 0.250 + / - 0.089 for the healthy group and 0.189 + / - 0.041 for the COPD group.

[0144] However, the boxplots in Figures 11c and 11d show that when the 24-hour regression line was used instead, the adjusted counts were higher in the COPD group at both the 6-hour (Figure 11c) and 24-hour (Figure 11d) time points. Using the 24-hour regression line, the adjusted counts at 6 hours were 0.182 + / - 0.027 in the healthy group and 0.225 + / - 0.038 in the COPD group. At 24 hours, the adjusted counts were 0.250 + / - 0.089 in the healthy group and 0.285 + / - 0.017 in the COPD group.

[0145] [Discussion] The imaging platform demonstrated adequate quantification of the radiopharmaceutical signal, enabling both regional and bi-lung-based quantitative analysis. Figure 12 shows three-dimensional quantification of the radiopharmaceutical signal in the right lung of two study participants at both 6 and 24 hours. Figure 12a shows subject 1 (healthy), and Figure 12b shows subject 10 (COPD). While counts were determined regionally, analysis could also be performed using median counts across both lung RoIs. Due to the presence of emphysema in the COPD group, tissue density variability was observed between groups.

[0146] The results of this study show that the uncorrected signal is high in the healthy group, but an empirical approach can be used to distinguish between the presence or absence of emphysema (HRCT %LAA -950 Applying a correction for the 24-hour counts (defined as 24-hour counts) showed that the median normalized counts were higher in the COPD group when the regression line obtained from the counts detected at 24 hours was used as the correction formula.

[0147] Biodistribution and clearance in this study corresponded to data from previous studies, with early activity in the vascular compartment, metabolism and clearance via the hepatic and renal systems, and uptake in the liver and spleen. (8,11) At a later time point, rheumatoid arthritis (9) and sarcoidosis (8) As previously seen in studies in, more specific binding to the target occurred with increasing T / B ratio.

[0148] Furthermore, the greater increase in T / B ratio in the COPD group compared with the 6-hour scan appears to suggest that the radiopharmaceutical is more specifically bound to its target, TNF-α, in these patients, which corresponds to previous literature in patients with sarcoidosis and suggests that systemic therapy may be beneficial. (8) The rate of change in the T / B ratio was found to be greater in patients who were eligible for rheumatoid arthritis. (9) study also found that responders to anti-TNF-α therapy had greater absolute increases in T / B counts. The more heterogeneous nature of lung tissue compared with synovial tissue may be the reason why no increases in absolute counts were observed in this study. Relative changes may be more appropriate.

[0149] Given the central role of TNF-α as an inflammatory cytokine in COPD, the present study compared the 6-hour scan 99m The quantitatively increased uptake of Tc-infliximab and the larger T / B ratio after applying the correction factor would correspond to the expected increased expression of TNF-α in the lungs of COPD patients.

[0150] This study provides evidence that it is possible to image active inflammation in the lungs of COPD patients using technetium-labeled anti-TNF-α as a marker of inflammation. Previous studies in other disease groups have shown that uptake can predict response to treatment, indicating that these techniques can be used to evaluate the efficacy of various treatments. Due to the paucity of disease-modifying therapies in COPD, tools to identify treatment responders are particularly needed in clinical trials and in the clinic.

[0151] This study demonstrates the effectiveness of this novel quantitative method for noninvasively detecting target cytokine activity as an indicator of active inflammation in the lungs of patients with airway diseases. Previous studies have demonstrated this method in patients with rheumatoid arthritis, Crohn's disease, and sarcoidosis. (8,9,11) The use of SPECT-CT and scintigraphic approaches to determine target cytokine activity in other inflammatory diseases, such as inflammatory bowel disease, supports the conclusion that this method may also be applicable to the detection of other inflammatory diseases and other components of the inflammatory pathway.

[0152] 〔application〕 While the method was tested in the lungs in the above example, it can be readily adapted to other diseases and / or organs, such as inflammatory diseases of the joints, such as RA. For example, the method can be applied to quantify the activity of components such as IL-5, IL-5Rα, and IgE in asthma, or IL-6 in rheumatoid arthritis. Other potential applications include inflammatory diseases of the intestine or liver, or other disease groups such as cancer and infectious diseases.

[0153] This method can be used for a variety of purposes. Since disease indicators can be determined based on specific biological targets, it can be used to elucidate the underlying mechanisms of disease activity. Disease imaging using this method can also be used as a biomarker to distinguish specific endotypes or phenotypes within a disease group. For example, this method can be useful for analyzing and identifying cancer subtypes or latent infections. This method can provide initial subject stratification and final outcome generation in clinical trials, for example, to select subgroups of patients with specific diseases most likely to respond to investigational new drugs (IMPs).

[0154] The method can also predict the probability of response to a particular treatment for an individual subject based on the level of the biological target targeted by the treatment, as described above. This provides a precision medicine approach to determining optimal treatment options for patients and can rapidly identify the best drug for a particular patient. This allows physicians to provide the right treatment the first time and reduces the time and expense spent trying different treatments to find one that works for a particular subject.

[0155] "This method contributes to the research of new drugs and their application in other disease areas by providing a means to rapidly quantify the impact of drugs on disease using a non-invasive method. It can also be applied to stratify study participants, leading to increased efficiency and faster results. This could be invaluable in disease groups like COPD, where the heterogeneity of study populations is thought to be the reason why no disease-modifying therapy has been successful in clinical trials to date."

[0156] The method can be implemented as part of a platform deployment via a software-as-a-service (SAAS) model that augments current imaging capabilities. The method may ultimately be developed as a clinical imaging platform integrated into existing imaging processes, advantageously automating the analysis required for this process. The software platform can interface with hospital computer systems to access the imaging data 10 and present results to clinicians to inform treatment decisions. From the patient's perspective, they simply attend a routine imaging appointment at their local hospital. Part of the platform may be provided within the hospital, while the rest may be provided remotely. For example, as shown in the exemplary flowchart of FIG. 13, basic data validation of the imaging data 10 may be performed locally, such as when imaging is performed. Analysis to determine disease indicators may be performed by a remote system. The division of processing can be selected based on the availability of computational resources as well as other considerations, such as limitations due to data protection concerns.

[0157] Figure 14 illustrates a potential implementation of this method as a clinical platform. Figure 14a illustrates the overall platform process, targeting a specific biological target: components of the inflammatory pathway in COPD. Figure 14b illustrates a potential clinical application using the SPECT-CT imaging platform, enabling a precision medicine approach to biological treatment selection. By injecting multiple monoclonal antibodies, each labeled with a different radioisotope, multiple biological pathways can be simultaneously investigated. For example, if three monoclonal antibodies are determined to be effective in a patient, each can be labeled with a different radioisotope and infused simultaneously. Quantifying their uptake can identify the most active pathways and select the most effective treatment for the patient.

[0158] [References] 1.Bek, S. et al. Systematic review and meta-Analysis: Pharmacogenetics of anti-TNF treatment response in rheumatoid arthritis. Pharmacogenomics J. 17, 403-411 (2017). 2.Jiemy, W. F. et al. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) imaging of macrophages in large vessel vasculitis: Current status and future prospects. Autoimmun. Rev. 17, 715-726 (2018). 3.Jamar, F., Versari, A., Galli, F., Lecouvet, F. & Signore, A. Molecular Imaging of Inflammatory Arthritis and Related Disorders. Semin. Nucl. Med. 48, 277-290 (2018). 4.Jones, H. A., Marino, P. S., Shakur, B. H. & Morrell, N. W. In vivo assessment of lung inflammatory cell activity in patients with COPD and asthma. Eur. Respir. J. 21, 567-573 (2003). 5.Mariani, G. et al. A review on the clinical uses of SPECT / CT. Eur. J. Nucl. Med. Mol. Imaging 37, 1959-1985 (2010). 6.Israel, O. et al. Two decades of SPECT / CT - the coming of age of a technology: An updated review of literature evidence. Eur. J. Nucl. Med. Mol. Imaging 46, 1990-2012 (2019). 7.Pacilio, M., Lauri, C., Prosperi, D., Petitti, A. & Signore, A. New SPECT and PET Radiopharmaceuticals for Imaging Inflammatory Diseases: A Meta-analysis of the Last 10 Years. Semin. Nucl. Med. 48, 261-276 (2018). 8.Vis, R. et al. 99mTc-anti-TNF-α antibody for the imaging of disease activity in pulmonary sarcoidosis. Eur. Respir. J. 47, 1198-1207 (2016). 9.Conti, F. et al. Role of scintigraphy with 99mTc-infliximab in predicting the response of intraarticular infliximab treatment in patients with refractory monoarthritis. Eur. J. Nucl. Med. Mol. Imaging 39, 1339-1347 (2012). 10.Mather, S. J. & Ellison, D. Reduction-mediated technetium-99m labeling of monoclonal antibodies. J. Nucl. Med. 31, 692-697 (1990). 11.D’Alessandria, C. et al. Use of a 99m-Tc labeled anti-TNF alpha monoclonal antibody in Crohn’s disease: in vitro and in vivo studies. Q. J. Nucl. Med. Mol. Imaging 51, 334-342 (2007). 12.Valentin, J. Radiation dose to patients from radiopharmaceuticals. Addendum 3 to ICRP Publication 53. ICRP Publication 106. Approved by the Commission in October 2007. Ann. ICRP 38, 1-197 (2008). 13.Matsuoka, S. et al. Quantitative CT Measurement of Cross-sectional Area of Small Pulmonary Vessel in COPD. Acad. Radiol. 17, 93-99 (2010). 14.Keatings, V. M., Collins, P. D., Scott, D. M. & Barnes, P. J. Differences in interleukin-8 and tumor necrosis factor-alpha in induced sputum from patients with chronic obstructive pulmonary disease or asthma. Am. J. Respir. Crit. Care Med. 153, 530-534 (1996). 15.Galli, F., Lanzolla, T., Pietrangeli, V., Malviya, G., Ricci, A., Bruno, P., Ragni, P., Scopinaro, F., Mariotta, S., & Signore, A. (2015). In vivo evaluation of TNF-alpha in the lungs of patients affected by sarcoidosis. BioMed Research International, 2015. https: / / doi.org / 10.1155 / 2015 / 401341 16.Dammes, N., & Peer, D. (2020). Monoclonal antibody-based molecular imaging strategies and theranostic opportunities. In Theranostics (Vol. 10, Issue 2, pp. 938-955). Ivyspring International Publisher. https: / / doi.org / 10.7150 / thno.37443

Claims

1. 1. A method for determining a measure of disease in a body region of a subject, comprising: receiving imaging data obtained by imaging the region of a subject's body, the imaging data including data representing a distribution within the region of a marker that was administered to the subject prior to the imaging and that binds to a biological target associated with the disease; processing the imaging data to obtain an indication of marker signals in the region; correcting an index of the marker signal taking into account the effect of tissue structure within the region on the marker signal; The corrected index of marker signals is used to determine an index of disease in said region.

2. 10. The method of claim 1, The method wherein the corrected measure of the marker signal represents the level of binding of the marker to tissue in the region.

3. 3. The method according to claim 1 or 2, The method wherein the disease is an inflammatory disease and the biological target is a component of an inflammatory pathway.

4. 4. The method according to claim 1, wherein The method, wherein correcting the index of the marker signal includes correcting the marker signal by a blood flow rate in the region.

5. 5. The method of claim 4, The method wherein correcting the marker signal for vascular flow includes determining vascular density within the region.

6. 6. The method of claim 5, The method wherein determining vascular density includes determining a total volume of blood vessels within the region.

7. 7. The method according to claim 5 or 6, The method, wherein the imaging data further includes data representing tissue structure within the region, and determining vascular density includes identifying blood vessels within the region using the data representing tissue structure.

8. 8. The method of claim 7, The method, wherein the data representing the tissue structure is acquired by one or more of low-dose computed tomography, high-resolution computed tomography, multi-slice computed tomography, magnetic resonance imaging, plain radiography, and ultrasound.

9. 9. The method according to any one of claims 4 to 8, The method wherein correcting the marker signal for vascular flow in the region includes determining a background marker signal corresponding to the marker signal from unbound marker in the blood.

10. 10. The method according to any one of claims 1 to 9, The method, wherein correcting the index of the marker signal comprises correcting for tissue density within the region.

11. 11. The method of claim 10, The imaging data further includes data representing tissue structure within the region, and correcting tissue density includes using the data representing the tissue structure to identify subregions within the region where tissue density is reduced.

12. 12. The method of claim 11, The method, wherein the data representing the tissue structure is acquired by one or more of low-dose computed tomography, high-resolution computed tomography, multi-slice computed tomography, magnetic resonance imaging, plain radiography, and ultrasound.

13. 13. The method of claim 12, The method, wherein the data representing tissue structure is acquired using X-rays, and a subregion is identified as a subregion with reduced tissue density if the attenuation of the X-rays in the subregion is below a predetermined threshold.

14. 14. The method according to any one of claims 10 to 13, The method wherein correcting for tissue density includes correcting an index of the marker signal based on the proportion of tissue in the region that has a reduced density.

15. 15. The method of claim 14, A method in which correcting the marker signal index based on the proportion of tissue in the region with reduced density is determined using the marker signal index for multiple samples with different proportions of tissue in the region with reduced density.

16. 16. The method of any one of claims 1 to 15, A method wherein the measure of the marker signal is the mean marker signal within said region, and optionally the median marker signal.

17. 17. The method of any one of claims 1 to 16, A method in which the marker signal index is normalized by a background marker signal, which corresponds to the marker signal from unbound marker in the blood.

18. 13. A method according to claim 9, or claim 17, or any claim dependent thereon, The method wherein said background marker signals are distinguished from marker signals in major vessels, optionally in the aortic arch.

19. 20. The method of claim 18, The background marker signal is the mean value marker signal, optionally an arithmetic mean value marker signal, of the major vessels.

20. 20. The method of any one of claims 1 to 19, The method wherein the biological target comprises an immune and / or inflammatory signaling protein, for example, a cytokine, a chemokine, a cell surface receptor, or an extracellular matrix component.

21. 21. The method of any one of claims 1 to 20, The method wherein the marker comprises a radioactive marker and / or wherein the marker comprises a monoclonal antibody.

22. 22. The method of any one of claims 1 to 21, The method wherein the data representing the distribution of the markers in the region is obtained using single photon emission computed tomography, SPECT, positron emission tomography, PET, planar scintigraphy, or magnetic resonance imaging.

23. 23. The method of any one of claims 1 to 22, The method wherein said region of the subject's body comprises an organ of the subject, for example, one or both lungs of the subject, or a joint of the subject.

24. 24. The method of any one of claims 1 to 23, the imaging data includes data representing a distribution within the region of each of a plurality of markers administered to the subject prior to the imaging, each of the plurality of markers binding to a different biological target; processing the imaging data includes processing the imaging data to obtain an indication of a marker signal for each of the plurality of markers; correcting the indices of the marker signals includes correcting each of the indices of the marker signals; The method of determining the disease index uses a corrected index of the multiple marker signals.

25. 10. A method according to claim 7, or claim 11, or any claim dependent thereon, the data representing tissue structure and the data representing the distribution of the markers have different resolutions and / or volume divisions, are measured in different states of the subject's body, and / or are measured at different times; The method further comprises aligning the data representing tissue structure with the data representing the distribution of the marker.

26. 26. The method of claim 25, The method, wherein the registering includes using a non-rigid registration algorithm.

27. 27. The method of claim 25 or 26, The method, wherein the matching comprises transforming the data representing tissue structure to match the data representing the distribution of the markers.

28. A computer program comprising instructions which, when executed by a computer, cause the computer to carry out the method of any one of claims 1 to 27.

29. A computer readable medium comprising instructions which, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 27.

30. 28. Apparatus for determining indicators of disease in a region of a subject's body, the apparatus comprising a processor configured to perform the steps of the method of any one of claims 1 to 27.

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