Evidence map›Paper›PMID 38837111›Full record

ArticleDigestive diseases and sciences2024

Prognostic Analysis Combining Histopathological Features and Clinical Information to Predict Colorectal Cancer Survival from Whole-Slide Images.

Chengfei Cai, Yangshu Zhou, Yiping Jiao, Liang Li, Jun Xu

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Article in Digestive diseases and sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Chengfei Cai *School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China. chengfeicai@nuist.edu.cn.
Yangshu Zhou *Department of Pathology, Zhujiang Hospital of Southern Medical University, Guangzhou, 510280, China.
Yiping Jiao *Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Liang Li *Department of Pathology, Nanfang Hospital of Southern Medical University, Guangzhou, 510515, China.
Jun Xu *Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China.

Funding

National Natural Science Foundation of China 61771249National Natural Science Foundation of China 62171230National Natural Science Foundation of China 81871352National Natural Science Foundation of China 91959207National Natural Science Foundation of China 92159301National Natural Science Foundation of China U1809205
6 · The paper itself

Abstract

backgroundColorectal cancer (CRC) is a malignant tumor within the digestive tract with both a high incidence rate and mortality. Early detection and intervention could improve patient clinical outcomes and survival.

methodsThis study computationally investigates a set of prognostic tissue and cell features from diagnostic tissue slides. With the combination of clinical prognostic variables, the pathological image features could predict the prognosis in CRC patients. Our CRC prognosis prediction pipeline sequentially consisted of three modules: (1) A MultiTissue Net to delineate outlines of different tissue types within the WSI of CRC for further ROI selection by pathologists. (2) Development of three-level quantitative image metrics related to tissue compositions, cell shape, and hidden features from a deep network. (3) Fusion of multi-level features to build a prognostic CRC model for predicting survival for CRC.

resultsExperimental results suggest that each group of features has a particular relationship with the prognosis of patients in the independent test set. In the fusion features combination experiment, the accuracy rate of predicting patients' prognosis and survival status is 81.52%, and the AUC value is 0.77.

conclusionThis paper constructs a model that can predict the postoperative survival of patients by using image features and clinical information. Some features were found to be associated with the prognosis and survival of patients.

Indexed as

Colorectal NeoplasmsFemaleHumansImage Interpretation, Computer-AssistedMalePredictive Value of TestsPrognosisDeep convolutional networksFeature extraction and selectionMulti-tissue segmentationPrognostic survival analysisWhole slide image

Identifiers

PMID38837111

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