{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T05:17:56Z","timestamp":1787807876658,"version":"build-2784847793"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"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>Introduction<\/jats:title>\n                    <jats:p>Although TNM staging is the standard framework for prognostic assessment in colorectal cancer (CRC), patients within the same stage can have heterogeneous outcomes. This study aimed to extract tumor-tissue features from whole-slide images (WSIs), develop an image-derived prognostic model for stage II\u2013III CRC, and conduct an exploratory evaluation of its performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A transfer-learning tissue classifier was used to identify tumor regions in CRC WSIs. Self-supervised contrastive learning was used to extract tumor features, which were summarized by patient-level K-Means clustering. An attention-based survival model generated a locked image-derived Risk Score. The actual analyzed population comprised 335 patients with locally advanced CRC: 318 patients recorded as stage III and 17 patients with T4N0 disease (high-risk stage II by AJCC eighth-edition anatomic staging). The 265-patient TCGA cohort was randomly divided 8:2 into a training set and an internal holdout set; 70 SAHSYU patients formed a single-center exploratory cohort. Discrimination was quantified with patient-level bootstrap confidence intervals. Cox regression, Kaplan\u2013Meier analysis, one-year calibration, and exploratory decision-curve analysis were performed. A sensitivity analysis excluded all N0 patients without retraining or changing the locked Risk Score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The C-index was 0.704 (95% bootstrap CI 0.637\u20130.768) in training, 0.663 (0.544\u20130.776) in the internal holdout set, and 0.706 (0.533\u20130.859) in SAHSYU. Per one training-set standard deviation, the Risk Score was associated with PFS internally (HR 1.688, 95% CI 1.080\u20132.640; Wald p=0.022) and exploratorily in SAHSYU (HR 6.010, 95% CI 1.170\u201330.877; p=0.032). In the sensitivity analysis excluding all N0 records without retraining, the C-index remained 0.647 internally and 0.705 in SAHSYU; fixed-threshold log-rank p values were 0.014 and 0.027, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The image-derived Risk Score showed preliminary PFS discrimination and risk-stratification potential in stage II\u2013III CRC. These findings require validation in larger multicenter cohorts with more events, longer follow-up, formal calibration, and clinical-utility assessment before treatment decisions can be informed.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1863967","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T04:27:05Z","timestamp":1787804825000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Construction and exploratory validation of a digital pathology prognostic model for stage II\u2013III colorectal cancer based on contrastive learning and attention mechanisms"],"prefix":"10.3389","volume":"16","author":[{"given":"Yizhan","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Medical Equipment, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology","place":["Luoyang, 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