ORIGINAL RESEARCH article
Front. Oncol.
Sec. Gastrointestinal Cancers: Colorectal Cancer
A two-stage deep learning prediction system for colon cancer microsatellite instability(MSI) status using CT images
Provisionally accepted
- 1Kunming University of Science and Technology, Kunming, China
- 2Department of Colorectal Surgery, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China
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Background: This study seeks to build a two-stage deep learning approach for identifying the MSI status of colorectal cancer based on CT scans without the requirement for manual segmentation. Methods: This study included 108 enhanced CT scans of colorectal cancer, including 68 cases of ascending colon, 14 cases of transverse colon, 18 cases of descending colon, and 8 cases of sigmoid colon; 56 cases of MSI-H and 52 cases of MSS.In the first stage, the segmentation model MSI-SAM was trained to accurately segment the lesion locations in the CT scans. In the second stage, the mask acquired from the MSI-SAM segmentation was multiplied by the original CT image (CT_Origin) bitwise, and the result was merged with the mask obtained from the MSI-SAM segmentation (Segment) to obtain CT_ROI. Both CT_ROI and CT_Origin were then diagnosed using the colorectal cancer MSI status diagnosis model. Results: The performance of the suggested CT segmentation model MSI-SAM in the ascending colon, transverse colon, descending colon, and sigmoid colon areas (DSC: mIoU) was (0.886: 0.798), (0.878: 0.783), (0.923: 0.857), and (0.854: 0.747), respectively. The AUC of the MSI status diagnostic model for colorectal cancer patients is 0.935 (95% CI 0.892–0.947), the ACC is 0.913, Sensitivity was 1.000 , and Specificity was 0.846 . Conclusions: The segmentation masks created by the trained deep learning segmentation model achieved a level comparable to that of expert radiologists, and the deep learning diagnostic model played an essential role in supporting doctors in diagnosis.
Keywords: Microsatellite Instability, Colon Cancer, deep learning, Segment Anything, LORA, Contrastive learning
Received: 05 Sep 2025; Accepted: 25 Nov 2025.
Copyright: © 2025 Cui, Xiong, Yang, He and Shen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jianfeng He
Tao Shen
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