Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables.

Journal: Cancer medicine

Published Date: Apr 1, 2024

Abstract

OBJECTIVE: This retrospective observational study aims to develop and validate artificial intelligence (AI) pathomics models based on pathological Hematoxylin-Eosin (HE) slides and pathological immunohistochemistry (Ki67) slides for predicting the pathological staging of colorectal cancer. The goal is to enable AI-assisted accurate pathological staging, supporting healthcare professionals in making efficient and precise staging assessments.

Authors

Yang Tan

Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.
Run Liu

Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Jia-Wen Xue

Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Zhenbo Feng

Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.

Keywords

Algorithms Artificial Intelligence Biomarkers, Tumor Colorectal Neoplasms Eosine Yellowish-(YS) Humans Ki-67 Antigen Nomograms Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (38545828)

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables.

Abstract

Authors

Keywords

External Resources

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