{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:14:16Z","timestamp":1786115656489,"version":"build-2736575974"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"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. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop and validate a preoperative multimodal model that predicts a high-risk recurrence phenotype indicative of poor disease-free survival (DFS), in order to stratify patients with stage IA non-small cell lung cancer (NSCLC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>This retrospective multicenter study enrolled 342 stage IA NSCLC patients from three independent centers. The high-risk recurrence phenotype (indicative of poor DFS) was defined by postoperative pathology as the presence of spread through air spaces (STAS), lymphovascular invasion (LVI), a predominant solid\/micropapillary\/complex glandular pattern, or a &amp;gt; 5% solid\/micropapillary component. Deep learning features were extracted from preoperative biopsy whole-slide images (WSI) using the UNI foundation model, and CT morphological and textural features were extracted from preoperative chest CT. An early-fusion multimodal model integrating clinical, radiomics, and pathomics features was developed and evaluated with five-fold cross-validation. The Kaplan\u2013Meier method with log-rank tests was used to assess associations between the model-predicted risk and DFS. Logistic regression identified clinical predictors of high-risk pathology. Model interpretability and clinical utility were examined with SHapley Additive exPlanations (SHAP) and calibration analysis, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The multimodal model achieved higher discriminative performance than each single-modality model in both the internal and external test sets. In the internal test set, it yielded an AUC of 0.76 (95% CI 0.58-0.90); in external validation, AUCs were 0.69 (95% CI 0.55-0.82) in Center 2 and 0.86 (95% CI 0.74-0.96) in Center 3. Multivariable analysis identified solid tumor density as the only independent predictor (OR\u00a0=\u00a05.13, 95% CI 1.53-17.24;\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0=\u00a00.008). Model-stratified high-risk patients showed significantly inferior DFS in both the training (2-year DFS 78% vs. 99%; log-rank\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.0001) and external (3-year DFS 86% vs. 100%; log-rank\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0=\u00a00.003) cohorts.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The multimodal model consistently predicted the high-risk recurrence phenotype across multiple centers. This phenotype may serve as a pragmatic indicator of poor DFS to guide earlier individualized treatment decisions, including adjuvant therapy and intensified surveillance, in stage IA NSCLC.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1915946","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T04:25:22Z","timestamp":1785990322000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An integrated multimodal model for early prediction of high-risk recurrence phenotype indicative of poor disease-free survival in stage IA NSCLC: a multicenter study"],"prefix":"10.3389","volume":"17","author":[{"given":"Linqiang","family":"Lai","sequence":"first","affiliation":[{"name":"Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University","place":["Lishui, 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China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinying","family":"Wu","sequence":"additional","affiliation":[{"name":"Lishui City People\u2019s Hospital, The Sixth Affiliated Hospital of Wenzhou Medical University","place":["Lishui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangjun","family":"Xie","sequence":"additional","affiliation":[{"name":"Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University","place":["Lishui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahao","family":"Wu","sequence":"additional","affiliation":[{"name":"Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University","place":["Lishui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruolan","family":"Mao","sequence":"additional","affiliation":[{"name":"Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University","place":["Lishui, 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