Tumor Grading via Blood Volume Map Frequency Distribution
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Solution Overview
Problem
Current tumor grading methods using blood volume maps are user-dependent, sensitive to noise, and unreliable due to incorrect reference values and heterogeneity issues, particularly for oligodendrogliomas, making it challenging to differentiate between high-grade and low-grade gliomas.
Innovation Solution
A method and system for tumor grading based on blood volume or cellular metabolism maps that exclude large blood vessels and necrotic areas, using frequency distribution analysis to assess heterogeneity, which reduces user interaction and noise sensitivity, and provides a more precise grading by analyzing the diversity of values across the tumor region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the hot-spot method is used to determine rCBV ratio, then tumor grading can be performed, but the method is highly user-dependent and sensitive to image noise
Solution Approach 1:
The patent extracts and excludes specific problematic regions (large blood vessels and necrotic areas) from the tumor volume before performing rCBV analysis. This extraction of harmful elements removes the sources of measurement error and user-dependent variability, leading to more reliable and objective tumor grading results that are less sensitive to image noise
Solution Approach 2:
The patent changes the analysis parameter from using a single hot-spot value to using the mean rCBV of the entire tumor volume (after excluding vessels and necrosis). This parameter transformation from localized peak value to global average value reduces sensitivity to local noise and user-dependent hot-spot selection, improving measurement reliability
2Productivity
If only a few image pixels are used to determine the rCBV hot-spot, then the grading can be performed quickly, but the method is inherently sensitive to image noise and spurious pixel values
Solution Approach 1:
The patent merges the rCBV values from all tumor voxels (after excluding vessels and necrosis) into a single mean value for grading. This combination of multiple data points into a comprehensive average reduces the impact of individual noisy or spurious pixels, maintaining grading speed while significantly improving measurement precision by utilizing the entire tumor volume rather than just a few pixels
3Measurement precision
If unaffected white matter rCBV values are used to derive the nCBV value, then normalization can be performed, but incorrect selection of reference rCBV values might result in under- or overestimation
Solution Approach 1:
The patent inverts the traditional normalization approach by not relying on external reference tissue (white matter) but instead using the tumor's own rCBV distribution characteristics. By analyzing the frequency distribution of rCBV values within the tumor itself and using the mean of the excluded regions as reference, the method eliminates the need for potentially incorrect external reference selection, improving both precision and reliability
4Reliability
If the mean nCBV for the tumor is used as the basis for grading, then the method is less user-dependent, but it does not reflect the diversity of values in the tumor
Solution Approach 1:
The patent introduces feedback by analyzing the frequency distribution of rCBV values within the tumor and using this distribution information to guide the grading process. The method examines how rCBV values are distributed across different ranges and uses this feedback to identify characteristic patterns associated with different tumor grades, thereby maintaining user-independence while preserving and utilizing tumor heterogeneity information
Data Source
AI summary
An embodiment of the invention is to make possible a non-invasive grading of a tumor based on parameters determined from a frequency distribution (histogram) of values in a map representing cerebral blood volume (CBV) or cellular metabolism in the tumor. The method is especially applicable to brain tumors such as gliomas where histological grading is difficult. The invention provides a precise and consistent grading since it relies on values selected from the whole tumor (not just from hot spots); since it takes the diversity or heterogeneity of the vascularization into account by analyzing the frequency distribution (not just a mean value); and since it involves and allows for a more automated procedure wherein any subjective contributions from human operators is not critical to the resulting grading. CBV maps may be obtained by perfusion imaging using MRI or CT scanning. Cellular metabolism maps may be obtained from a glucose metabolism map obtained by positron emission tomography (PET).