Frontiers in oncology · 2026
A two-stage deep learning prediction system for colon cancer microsatellite instability status using CT images.
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Cui Songlin, Xiong Xin, Yang Xudong, He Jianfeng, Shen Tao
Аннотация
BACKGROUND: This study seeks to build a two-stage deep learning approach for identifying the microsatellite instability (MSI) status of colon cancer based on computed tomography (CT) scans without the requirement for manual segmentation.
METHODS: This study included 108 enhanced CT scans of colon cancer, including 68 cases of ascending colon, 14 cases of transverse colon, 18 cases of descending colon, and 8 cases of sigmoid colon; there were 56 cases of MSI-H and 52 cases of microsatellite stability (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 colon 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: IoU) 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 patients with colon cancer was 0.935 (95% CI 0.892-0.947), the ACC was 0.913, the sensitivity was 1.000, and the 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.
METHODS: This study included 108 enhanced CT scans of colon cancer, including 68 cases of ascending colon, 14 cases of transverse colon, 18 cases of descending colon, and 8 cases of sigmoid colon; there were 56 cases of MSI-H and 52 cases of microsatellite stability (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 colon 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: IoU) 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 patients with colon cancer was 0.935 (95% CI 0.892-0.947), the ACC was 0.913, the sensitivity was 1.000, and the 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.