{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:01:04Z","timestamp":1790035264862,"version":"4.0.1"},"reference-count":33,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Tertiary lymphoid structures (TLS) are prognostic immune aggregates in the tumor microenvironment, but the value of location\u2010specific TLS features for predicting colorectal cancer (CRC) recurrence remains unclear. This study developed and internally validated a machine\u2010learning (ML) model integrating TLS features for recurrence prediction in stage II\u2010III CRC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We retrospectively included 224 patients with stage II\u2010III CRC after curative resection and split them into training (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009156) and held\u2010out internal validation (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200968) cohorts. TLS at intratumoral, invasive\u2010front, and peritumoral sites were semi\u2010quantitatively scored by two blinded pathologists. LASSO selected predictors from 39 candidate variables. Five ML algorithms were trained with stratified cross\u2010validation, predefined tuning, and training\u2010only preprocessing. Performance was evaluated using discrimination, calibration, decision\u2010curve\/clinical\u2010impact analyses, reclassification indices, SHAP interpretation, and Kaplan\u2013Meier risk stratification.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Recurrence occurred in 89 patients (39.7%). LASSO retained peritumoral TLS score, invasive\u2010front TLS score, and preoperative CEA. TLS scoring showed high interobserver agreement (weighted kappa: 0.82\u20130.86). LightGBM provided the most balanced performance, with AUCs of 0.872 (95% CI: 0.814\u20130.923) in training and 0.718 (95% CI: 0.572\u20130.839) in internal validation. SHAP ranked invasive\u2010front TLS score as the most influential predictor. The Youden cutoff (0.5017) separated high\u2010 and low\u2010risk groups with significantly different disease\u2010free survival (log\u2010rank\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001; hazard ratio\u2009=\u20095.132, 95% CI: 3.263\u20138.073). In exploratory comparison, validation AUCs were 0.727 for TLS\u2010only, 0.542 for clinical\u2010only, and 0.737 for combined models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A TLS\u2010based ML model showed moderate internal validation performance for CRC recurrence prediction. Location\u2010specific TLS features may support postoperative risk stratification, but external multicenter and molecularly integrated validation is required before clinical use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.72152","type":"journal-article","created":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T03:56:43Z","timestamp":1785556603000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Tertiary Lymphoid Structures as Predictors of Recurrence in Colorectal Cancer: Development and Validation of a Machine Learning\u2010Based Scoring Model"],"prefix":"10.1002","volume":"15","author":[{"given":"Xian\u2010Hua","family":"Lei","sequence":"first","affiliation":[{"name":"Department of Pathology, Ganzhou Cancer Hospital The Affiliated Cancer Hospital of Gannan Medical University  Ganzhou Jiangxi 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Hospital The Affiliated Cancer Hospital of Gannan Medical University  Ganzhou Jiangxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2839-2761","authenticated-orcid":false,"given":"Hui\u2010Juan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Pathology, Ganzhou Cancer Hospital The Affiliated Cancer Hospital of Gannan Medical University  Ganzhou Jiangxi 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