115 results about "Node metastasis" patented technology

The invention discloses a breast cancer axillary lymph node metastasis prediction method based on multi-instance learning. The breast cancer axillary lymph node metastasis prediction method is used for predicting the metastasis state of axillary lymph nodes through breast cancer pathological sections. Firstly, a data enhancement function containing various disturbance strategies is designed in a data enhancement part, and the adaptability of a model to small samples and variation data is improved; secondly, a ConvNeXt network is adopted to extract instance features in the image blocks, and a feature optimization structure based on a global attention mechanism, a feedforward enhancement module, multi-head attention pooling and a residual connection mechanism is designed on the basis, so that the global context modeling capability is enhanced, and the consistency and discrimination of feature representation are improved; in addition, an MS-AttnFusion architecture based on fusion of a Scattering2D two-dimensional wavelet scattering network and an attention mechanism is designed, and through multi-scale feature decomposition and key area adaptive enhancement, a packet-level feature vector with higher discrimination capability is generated; and finally, the classifier receives the packet-level feature vector as input, and outputs a prediction result of the axillary lymph node metastasis state.

The present application relates to a lymph node metastasis staging prediction method and apparatus, a computer device, and a storage medium. The method comprises: collecting a CT image and a digital pathological image of a patient; performing depth feature extraction on the CT image and the digital pathological image by means of a ResNet50 network and a ViT network, respectively, to obtain a first feature vector of the CT image and a second feature vector of the digital pathological image; combining the first feature vector and the second feature vector to obtain a fused feature vector; and inputting the fused feature vector into a fully connected neural network, and outputting a lymph node metastasis staging prediction result of the patient by means of the fully connected neural network. The present application uses a depth feature fusion strategy for a CT image and a digital pathological image to construct a lymph node metastasis staging prediction model, such that the generalization of the model can be improved while the prediction precision of the lymph node metastasis staging prediction model is improved.

The invention provides a method and a model based on transcriptome and intratumoral microorganisms, and particularly relates to a calculation model for evaluating cervical cancer lymph node metastasis risk and a related method, program, system and equipment. According to the model, abundance information of various gene markers (including CARD9, MNX1AS2, MRAS, OLFML2A and RPS28) and abundance information of multiple microorganisms (including Dialister, Catonella and Campylobacter) are fused as input characteristics, and a risk prediction model is constructed through a machine learning algorithm, so that quantitative evaluation on the lymph node metastasis risk of the cervical cancer patient is realized. The prediction precision of cervical cancer lymph node metastasis is improved, an auxiliary diagnosis decision basis is provided for clinic, and the method has important medical application value and industrial transformation potential.

The invention belongs to the technical field of medical image processing, and particularly relates to a non-functional pancreatic neuroendocrine tumor preoperative index prediction method and system. According to the method, on the basis of abdomen CT image lesion segmentation, pathological grading (low risk / high risk) and lymph node metastasis (LNM) risks are jointly predicted through a multi-task deep learning framework; according to the method, tumor morphological features, radiomics features and clinical parameters (such as tumor size and position) extracted by a segmentation network are combined, and high-precision prediction of preoperative key indexes is realized by using a feature fusion module and a lightweight classification head; through a cross-modal attention mechanism and multi-center data verification, the model achieves the pathologic classification AUC of 0.75 on an internal verification set, achieves the LNM prediction AUC of 0.78, and is significantly superior to traditional clinical experience judgment. According to the method, reliable decision support is provided for preoperative precise operation planning and personalized treatment.

The invention discloses a bladder cancer lymph node metastasis derived tumor organoid culture medium, a culture method and a rapid drug detection method, and belongs to the technical field of organoid and cell culture. The method comprises the following steps: culturing a bladder cancer lymph node metastasis-derived tumor organoid by using a bladder cancer lymph node metastasis-derived tumor organoid culture medium, immersing matrigel drops in the culture medium, and obtaining the bladder cancer lymph node metastasis-derived tumor organoid after the cultured organoid grows to 100-150 microns, to-be-detected drugs with different concentrations are directly added into the cell pore plate inoculated with the tumor organoids, the influence of the to-be-detected drugs on cell growth or metabolism is analyzed through a full-view bright-field imaging method, and the detection result of the drug for treating bladder cancer lymph node metastasis is obtained. Compared with a traditional ENR organ-like culture medium, the method has the advantages that the effect is better, and the cost is lower; the kit is used for screening medicines for bladder cancer lymph node metastasis, is simple and efficient, and can be used for accurately detecting medicines for efficiently killing bladder cancer lymph node metastasis cells.

The invention relates to a model for predicting an early gastric cancer lymph node metastasis risk and a construction method and application thereof. The input variables of the model comprise gender, tumor position, tumor maximum diameter, pathological differentiation degree, pT staging condition, submitted lymph node number and lymphatic vessel invasion condition, and the output value of the model is lymph node metastasis prediction probability. A model for predicting the lymph node metastasis risk of the early gastric cancer is designed, the lymph node metastasis risk of a patient with the early gastric cancer can be accurately evaluated through the model, a model for predicting the number of the lymph node metastasis is further obtained, a corrected pN stage (pNM) is provided based on the predicted number of the lymph node metastasis, and the establishment of the pNM stage can be used for predicting the lymph node metastasis risk of the patient with the early gastric cancer. The method can be applied to diagnosis of early gastric cancer patients who cannot perform sufficient lymph node cleaning due to various reasons, is helpful for clinicians to evaluate and calculate early gastric cancer patients who do not reach the sufficient number of detected lymph nodes, and provides a new way for accurate clinical evaluation.

The invention relates to an early tongue cancer latent lymph node metastasis prediction system, electronic equipment and a storage medium. The early tongue cancer latent lymph node metastasis prediction system comprises a data acquisition module used for acquiring head and neck MRI images, the head and neck MRI images being acquired MRI images of a target object; the automatic tumor detection model is used for performing target detection on the head and neck MRI image to obtain a tumor area image; the lymph node metastasis prediction model is used for performing lymph node metastasis prediction classification on the tumor area image to obtain a lymph node metastasis classification result; by means of the scheme, accurate tumor positioning of early oral tongue cancer can be achieved through the automatic tumor detection model, and accurate prediction of latent lymph node metastasis can be achieved through the lymph node metastasis prediction model.

The invention discloses application of ADRA1A as an anti-liver cancer diagnosis and treatment target. The invention provides application of an ADRA1A gene or protein in preparation of a product for diagnosing and / or treating liver cancer. The diagnosis comprises diagnosis for distinguishing liver cancer patients from healthy people, liver cancer staging and grading diagnosis, liver cancer malignancy degree diagnosis, liver cancer lymph node metastasis diagnosis, liver cancer vascular invasion and liver cancer prognosis diagnosis. According to the application disclosed by the invention, the effect of the neurotransmitter receptors in liver cancer development is researched, large-sample liver cancer sequencing data analysis is taken as an entry point, the influence of various neurotransmitter receptors on liver cancer prognosis is comprehensively analyzed, and the receptor subtype gene ADRA1A which remarkably influences liver cancer prognosis and has the most clinical transformation potential is screened out from the analysis point. The invention provides a new thought and target for clinical diagnosis and treatment of liver cancer.

The embodiment of the invention provides a method and device for identifying lymph node metastasis and prognosis prediction under non-small cell lung cancer carina based on dual-region radiomics and a deep learning model, a storage medium and electronic equipment, and relates to the technical field of lung cancer diagnosis. Compared with a traditional scheme for judging lymph node metastasis of a non-small cell lung cancer patient based on physician experience, an artificial intelligence model is constructed by using dual-region CT radiomics, and a highly reliable prediction result is obtained.

This invention provides a method, device, and equipment for preoperative risk stratification assessment of endometrial cancer. It involves segmenting and annotating preoperative ultrasound images to extract radiomics feature vectors, and simultaneously performing structured encoding and mapping of pathological biopsy data to generate pathological feature vectors. These two feature vectors are then input into a fusion network containing a cross-modal attention gating module. This gating module automatically generates dynamic weight vectors based on the consistency between the image and pathological features. The two feature vectors are then weighted and modulated separately before being concatenated to obtain a joint feature representation. This automatically reduces the weight of the lower-confidence modality and amplifies the weight of the higher-confidence modality when their assessment conclusions are inconsistent. Finally, the joint feature representation is input into a multi-task prediction network to output the probability of myometrial invasion depth and the probability of lymph node metastasis risk, thereby generating preoperative risk stratification results and surgical plan recommendations.

The present invention provides a method and a model based on transcriptome and intratumoral microorganisms, specifically relating to a computational model for evaluating the risk of lymph node metastasis in cervical cancer and related methods, programs, systems and devices. This model integrates the abundance information of multiple gene markers (including CARD9, MNX1_AS2, MRAS, OLFML2A and RPS28) and multiple microbial genera (including Dialister, Catonella and Campylobacter) as input features, and constructs a risk prediction model through machine learning algorithms, so as to realize the quantitative evaluation of the risk of lymph node metastasis in cervical cancer patients. The present invention improves the prediction accuracy of lymph node metastasis in cervical cancer, provides a basis for clinical auxiliary diagnosis and decision-making, and has important medical application value and industrial transformation potential.

The invention relates to the technical field of medical diagnosis, in particular to a papillary thyroid carcinoma diagnosis method based on hsacirc0076710 and a kit. The invention provides a papillary thyroid carcinoma detection kit based on hsacirc0076710, the expression condition of circular RNA hsacirc0076710 in a living body can be accurately and rapidly detected, a new gene detection means can be provided for early diagnosis and prognosis of papillary thyroid carcinoma, meanwhile, the invention provides a papillary thyroid carcinoma diagnosis method based on hsacirc0076710, and the papillary thyroid carcinoma detection kit can be applied to diagnosis of papillary thyroid carcinoma. According to the method, diagnosis is carried out by detecting the expression level of hsacirc0076710 in a sample, the areas (AUC) under an ROC curve in tissue, a fine needle biopsy eluent and a serum sample are 0.851, 0.838 and 0.769 respectively, and the sensitivity reaches 83.7%-95.1%. Further analysis shows that the hsacirc0076710 has high expression and high expression of lymph node metastasis (OR = 3.21, plt; 0.001) and advanced TNM (stage III / IV), and can be used for early diagnosis and prognosis risk assessment of papillary thyroid carcinoma.

The invention relates to the technical field of methylation detection site detection, and particularly provides an early gastric cancer lymph node metastasis risk prediction method, system, application and medium, and the method comprises the following steps: obtaining a gastric cancer public data set containing a DNA methylation chip data set and an RNA sequencing data set, and carrying out sample screening, quality control and grouping processing to obtain eight quality control grouping samples; carrying out methylation and RNA difference analysis on the quality control grouped samples to obtain a related gene set of differential methylation sites and differential methylation regions and an RNA differential expression gene set; a gene set of differential methylation sites and an RNA differential expression gene set are integrated and screened to obtain eight target genes. The system comprises a sample acquisition module, a gene analysis module and a gene screening module. Target genes are analyzed and screened on the basis of database biological information, and methylation detection sites for histopathological specimens are screened in combination with lymph node metastasis positive and negative early gastric cancer histological sample verification.

The invention discloses a tumor detection marker and application thereof in medicine preparation, and belongs to the technical field of biomedicine. Clinical sample qRT-PCR (quantitative real-time polymerase chain reaction) shows that ENST00000412203.1 is obviously up-regulated in breast cancer tissues, the area (AUC) under the ROC curve reaches 0.9822, and the diagnosis efficiency is excellent. In addition, the invention further finds that the expression of the polypeptide in lymph node metastasis tissues is obviously improved, and metastasis specificity is displayed. Meanwhile, the expression of ENST00000412203.1 is knocked down through siRNA, it is found that proliferation, migration and invasion of breast cancer cells are remarkably inhibited, and therefore the siRNA can be used for preparing targeted therapy drugs for breast cancer and has important clinical application value.

The application provides a breast cancer patient lymph node metastasis prediction model without clinical pathological characteristics, comprising the following steps: after three-dimensional reconstruction of two-dimensional lung enhanced CT films, an axillary lymph node atlas is established, all axillary lymph nodes in the atlas are selected as ROI regions, and more than 5 combined image features of each axillary lymph node are selected to distinguish whether breast cancer has axillary lymph node metastasis; and a logistic regression machine learning prediction model is used to construct the breast cancer patient axillary lymph node metastasis prediction model. The model established by the application can non-invasively predict whether breast cancer has axillary lymph node metastasis, the clinical pathological characteristics of the patient are not included in the model, and the image cutting in the model is not based on breast tumors, but based on axillary lymph nodes; the model is used to determine a suitable axillary treatment scheme, thereby avoiding unnecessary axillary surgery and complications, and helping to carry out more accurate surgery and adjuvant therapy mode of breast cancer.

The present disclosure relates to a method and apparatus for predicting mediastinal lymph node metastasis in non-small cell lung cancer based on information regarding a patient's age, a histological type of a tumor, a location of the tumor, a size of the tumor, a clinical lymph node stage determined by CT, and a clinical lymph node stage determined by PET-CT. According to an aspect of the present disclosure, a method and apparatus for predicting mediastinal lymph node metastasis in non-small cell lung cancer provides information on the presence of mediastinal lymph node metastasis in potentially operable lung cancer patients, specifically non-small cell lung cancer patients, and thus can be usefully used for decision-making on staging and treatment methods for non-small cell lung cancer patients, such as invasive mediastinal staging.

Provided is a treatment of lung cancer (in particular, small cell lung cancer) in a human subject with an anti-PD-1 antibody and, optionally, chemotherapy. In some embodiments, the small cell lung cancer is an advanced (e.g., locally advanced) or extensive stage (e.g., stage III or stage IV), unresectable or metastatic (e.g., with liver, lung or lymph node metastasis, e.g., with 2, 3 or more metastatic sites) lung cancer. In some embodiments, the chemotherapy is carboplatin and / or cisplatin, and optionally further etoposide. In some embodiments, the SCLC is small cell carcinoma. In some embodiments, the SCLC is combined small cell carcinoma. In some embodiments, the treatment is the first line treatment. In some embodiments, the claimed therapy is effective in the treatment of SCLC. In some embodiments, the human subject is a responder to treatment by at least one measure of response to treatment.

The present invention discloses a method, system and storage medium for predicting sentinel lymph node metastasis in breast cancer. The method includes: obtaining labeled WSIs as a training data set, and performing preprocessing to obtain an image patch set; constructing a WSI classification model; using the image patch set to pre-train a feature extractor to obtain a feature vector set; inputting the feature vector set into a prototype clustering module, and extracting multiple prototypes through clustering; dividing the sentinel lymph node WSI of breast cancer into image patches and inputting them into the feature extractor to extract image patch features; inputting the image patch features and prototypes into a feature fusion module for matching to generate a soft assignment histogram, and constructing a feature vector of the sentinel lymph node WSI of breast cancer; sending the feature vector of the sentinel lymph node WSI of breast cancer into a fully connected layer to obtain a WSI classification score and perform metastasis judgment. This method can better solve the problem of micro-metastasis recognition while maintaining accurate recognition of macro-metastasis, so as to accurately diagnose sentinel lymph node metastasis in breast cancer.

The invention discloses a collagen feature-based lymph node state judgment method and system, a terminal and a storage medium, and the method comprises the steps: obtaining a pancreatic duct gland data sample, and carrying out the collection of a multi-photon image, and obtaining a target multi-photon image; obtaining a macroscopic collagen feature in the target multi-photon image, and calculating a first score of the macroscopic collagen feature; obtaining a microscopic collagen feature in the target multi-photon image, and calculating a second score of the microscopic collagen feature; constructing a column graph model according to the first score and the second score; and acquiring current pancreatic duct gland data of the patient, and inputting the data into the column graph model to obtain a lymph node metastasis result. According to the method, the macroscopic collagen features and the microcosmic collagen features in the pancreatic duct gland data sample are extracted and combined to obtain the column graph model, and accurate judgment of lymph node metastasis can be achieved.

The present invention discloses metabolomics-based diagnostic markers for esophageal squamous cell carcinoma lymph node metastasis and their applications. The present invention provides the application of 1-methyl-2-pyridone-5-carboxamide, 1-methylnicotinamide, and Nicotinamide as markers for developing detection, auxiliary detection, screening, or auxiliary screening of esophageal squamous cell carcinoma lymph node metastasis. Experiments of the present invention have demonstrated that the use of three target compound concentrations can quickly diagnose whether esophageal squamous cell carcinoma patients have lymph node metastasis. This method has the advantages of high accuracy, high sensitivity, and strong universality, and can provide a strong scientific basis for clinical diagnosis and treatment.

The invention discloses a bladder cancer lymph node metastasis prediction method and device, equipment and a storage medium. The method comprises the following steps: acquiring a plurality of sliding window images corresponding to a urine cell slice image, and acquiring a plurality of cross section images corresponding to a nuclear magnetic resonance image; traversing and combining the plurality of sliding window images and the plurality of cross section images to obtain a plurality of image pairs, wherein each image pair comprises one sliding window image and one cross section image; lesion cell features of the sliding window image in the image pair are extracted, lesion area features of the cross section image in the image pair are extracted, and the lesion cell features and the lesion area features are fused through a mutual attention model to obtain fusion features; and splicing the fusion features of the image pairs, and inputting the spliced fusion features into a pre-trained classification network to obtain a bladder cancer lymph node metastasis result output by the classification network. By means of the technical means, the problem that in the prior art, the accuracy is low when lymph node metastasis is detected through single radiology is solved, and the misdiagnosis or missed diagnosis probability is reduced.

The present application relates to the technical field of biological medicine, and particularly relates to application of 1-DNJ in treating lymph node metastasis of gastric cancer. The present application firstly finds that 1-DNJ can play a role as a VCAM1 / VLA-4 interaction inhibitor, and further finds that 1-DNJ can be used for treating lymph node metastasis of gastric cancer, expands the application range of 1-DNJ in the field of antitumor, and provides a new technical scheme for prevention and treatment of lymph node metastasis of gastric cancer. 1-DNJ belongs to a small molecule compound, has the characteristics of convenient preparation, good potential tissue penetration ability and flexible drug administration mode, and has good prospects for drug development and clinical application.

This invention provides immunobiomarkers for the auxiliary diagnosis of esophageal cancer lymph node metastasis and their applications. Specifically, this invention provides the use of immunobiomarkers for esophageal cancer lymph node metastasis and their detection reagents, for the preparation of diagnostic reagents or kits for assessing the risk of esophageal cancer lymph node metastasis. Studies have shown that the immunobiomarkers of this invention can serve as markers of esophageal cancer lymph node metastasis with high accuracy.

The invention discloses application of an IGF2BP2-m6A-VCAN-TLR2 (Insulin-like Growth Factor 2BP2-m6A-VCAN-TLR2) signal axis in preparation of a medicine for treating lymphatic metastasis of intrahepatic cholangiocarcinoma. Aiming at the problems of unknown lymphatic metastasis mechanism and lack of effective targets of intrahepatic cholangiocarcinoma, the invention discovers and verifies that mRNA binding protein 2 of insulin-like growth factor 2 is modified by m6A to regulate and control the expression of pluripotent proteoglycan so as to activate a Toll-like receptor 2 signal, promote macrophages to secrete vascular endothelial growth factor C and drive a brand new pathway of lymphatic metastasis. Based on this, the invention provides an inhibitor targeting the signal axis, especially a small molecule compound 8010-8498. Experiments show that the compound can significantly inhibit migration, invasion and epithelial-mesenchymal transition of bile duct cancer cells, reduce macrophage secretion of vascular endothelial growth factors C, effectively inhibit tumor growth and lymphatic metastasis in a nude mouse popliteal lymph node metastasis model and a spontaneous bile duct cancer model, and present a synergistic effect when combined with gemcitabine and cis-platinum.

The invention provides a breast cancer axillary lymph node metastasis load state prediction method and system based on cross-domain ultrasonic data, and relates to the technical field of image processing, and the method comprises the steps: obtaining a training set, training a constructed breast cancer axillary lymph node metastasis load state prediction model through the training set, and obtaining a breast cancer axillary lymph node metastasis load state prediction model; obtaining a trained breast cancer axillary lymph node metastasis load state prediction model; and inputting the ultrasonic images of the breast primary focus area and the axillary lymph node area of a patient to be predicted into the trained breast cancer axillary lymph node metastasis load state prediction model to obtain a breast cancer axillary lymph node metastasis load state fine prediction result. According to the method, the breast cancer axillary lymph node metastasis load state prediction model can be utilized to effectively extract and fuse the features of the cross-domain ultrasonic data, and the fused information is classified and corrected to obtain a fine prediction result of the breast cancer axillary lymph node metastasis load state.

The invention discloses application of a NOD1 truncated splicing isomer as a marker and a target spot in preparation of a thyroid papillary minimal cancer detection reagent, an immune targeting drug and a screening method, and belongs to the technical field of biological medicine. Aiming at the problem of clinical diagnosis and treatment difficulty caused by lack of an effective lymph node metastasis prediction marker for papillary thyroid carcinoma, the invention discovers that a truncated splicing isomer NOD1-S can be used as a key transcription marker. According to the method, the transcriptional instinct of NOD1-S is specifically amplified by a specific primer pair, and a detection reagent or a kit can be prepared on the basis of the transcriptional instinct of NOD1-S; meanwhile, the NOD1-S can be used as a drug target for screening or preparing immune targeting drugs. The kit is mainly used for auxiliary detection of thyroid papillary minimal carcinoma lymph node metastasis and research and development and screening of immune targeting drugs.

The present application discloses a method, device, equipment and storage medium for predicting lymph node metastasis of bladder cancer. In the present application, a plurality of sliding window images corresponding to urine cell section images are obtained, and a plurality of cross-sectional images corresponding to nuclear magnetic resonance images are obtained; a plurality of image pairs are obtained by traversing and combining the plurality of sliding window images and the plurality of cross-sectional images, and each image pair includes a sliding window image and a cross-sectional image; the lesion cell features of the sliding window image in the image pair are extracted, the lesion area features of the cross-sectional image in the image pair are extracted, and the lesion cell features and the lesion area features are fused through a mutual attention model to obtain fused features; the fused features of each image pair are spliced and then input into a pre-trained classification network to obtain the metastasis result of the lymph nodes of bladder cancer output by the classification network. By the above technical means, the problem of low accuracy in detecting lymph node metastasis by single radiology in the prior art is solved, and the probability of misdiagnosis or missed diagnosis is reduced.

The present invention discloses an interpretable ultrasonic image nodule recognition method based on a hierarchical network, comprising: 1) establishing a thyroid ultrasonic image data set; 2) performing mosaic preprocessing on unlabeled data, and establishing a self-supervised learning network layer based on a 2D ultrasonic feature encoder to mine features of the unlabeled data; 3) establishing a nodule measurement network layer based on a 2D ultrasonic feature encoder to obtain the contour, size and aspect ratio of nodules; 4) establishing a self-supervised learning network layer based on a nodule ROI feature encoder to mine features of suspicious nodule ROIs; 5) establishing a nodule multi-factor quantification network layer to realize the recognition and quantification of edges, components, echo quality and calcification; 6) using a multi-layer perceptron to realize interpretable prediction of benign and malignant and lymph node metastasis. The experimental results prove that the present invention improves the accuracy of nodule contour measurement and multi-factor recognition, and further enhances the interpretability of automatically recognizing ultrasonic thyroid nodules.

The application discloses a method for predicting the lymph node metastasis risk of thyroid papillary carcinoma based on an integrated machine learning model. Specifically, the application provides a training method for an integrated risk prediction model for the lymph node metastasis of thyroid papillary carcinoma, which comprises the following steps: (s1) providing a data set comprising the expression data of marker genes of thyroid cancer patients with and without lymph node metastasis; (s2) training a basic model: training a basic prediction model based on the data set; (s3) training a fusion layer: training a stacked ensemble model and a bagging ensemble model based on the output results of the basic prediction model; (s4) training a decision layer: training a weighted fusion model based on the output results of the stacked ensemble model and the bagging ensemble model, thereby obtaining the integrated risk prediction model. The model can realize accurate prediction of the lymph node metastasis risk of thyroid papillary carcinoma.

The present application discloses an application of RAB32 in the preparation of products for the diagnosis, treatment and prognosis of non-small cell lung cancer. In the present application, RAB32, a member of the small GTPase RAB family, is significantly more highly expressed in non-small cell lung cancer tissues and cell lines than in adjacent cancer tissues and normal cells, and is associated with a poor prognosis. In vitro experiments show that knocking down the highly expressed RAB32 gene in tumor cells can significantly inhibit the proliferation of lung adenocarcinoma cells. In an in vivo tumor-bearing mouse model, knocking down the RAB32 gene significantly reduced the tumor volume and inhibited its growth in the knockdown group, while also inhibiting the lymph node metastasis ability of lung adenocarcinoma cells. Therefore, the RAB32 gene can be used in the preparation of products for the diagnosis, treatment and prognosis of non-small cell lung cancer.