{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:22:36Z","timestamp":1787026956198,"version":"build-2736575974"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u20130.947), the ACC was 0.913, the sensitivity was 1.000, and the specificity was 0.846.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2025.1699430","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T05:43:53Z","timestamp":1768196633000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A two-stage deep learning prediction system for colon cancer microsatellite instability status using CT images"],"prefix":"10.3389","volume":"15","author":[{"given":"Songlin","family":"Cui","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology","place":["Kunming, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Xiong","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology","place":["Kunming, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xudong","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Colorectal Surgery, The Third Affiliated Hospital of Kunming Medical University","place":["Kunming, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfeng","family":"He","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of 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