{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T11:44:30Z","timestamp":1785843870891,"version":"3.56.0"},"reference-count":34,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:00Z","timestamp":1781740800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:00Z","timestamp":1781740800000},"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":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>To develop a multimodal computational pathology framework integrating handcrafted morphology and deep Vision Transformer (ViT) features to improve lymph node metastasis prediction and survival risk stratification in early\u2010stage lung squamous cell carcinoma. Whole\u2010slide images (WSIs) from 211 TCGA LUSC patients and an external cohort of 150 cases underwent standardized stain normalization, adaptive multiresolution patch extraction, and stringent artifact removal. Handcrafted morphological features were extracted using a customized CellProfiler pipeline, and deep embeddings were obtained from a fine\u2010tuned ViT model. After ICC reliability filtering and correlation reduction, LASSO, mutual information (MI), and Boruta selection strategies were applied. A unified early\u2010fusion vector was constructed and evaluated using SVM, XGBoost, TabNet, and TabTransformer classifiers. Survival analyses employed Cox\u2010LASSO, Elastic Net, Random Survival Forests, and Gradient Boosting Survival models, with performance assessed using AUC, C\u2010index, IBS, and time\u2010dependent AUC. Deep ViT\u2010derived features outperformed handcrafted morphology across all metrics (external AUC 0.82 vs. 0.71), and fusion significantly enhanced predictive performance, achieving test and external AUCs up to 0.94 and 0.92, respectively. The LASSO\u2010TabTransformer fusion pipeline provided the strongest metastasis discrimination, with recall approaching 87%. Survival modeling demonstrated similarly substantial improvements: fusion\u2010based Gradient Boosting Survival achieved C\u2010indices of 0.91 (test) and 0.88 (external), with 1\u2010year AUCs of 0.92 and 0.89. FDR\u2010validated imaging covariates remained independently prognostic, and Kaplan\u2013Meier curves confirmed robust stratification of LNM+ versus LNM\u2212 groups. Multimodal fusion of deep and handcrafted features produces highly accurate, generalizable models for LNM prediction and survival stratification in LUSC, offering a powerful tool for preoperative decision support.<\/jats:p>","DOI":"10.1002\/ima.70359","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T04:36:41Z","timestamp":1781843801000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Histopathology\u2010Driven Multimodal Modeling for Lymph Node Metastasis Prediction and Survival Stratification in Lung Squamous Cell Carcinoma"],"prefix":"10.1002","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2728-0304","authenticated-orcid":false,"given":"Rong","family":"Fu","sequence":"first","affiliation":[{"name":"Department of Oncology, The First Affiliated Hospital Qiqihar Medical University  Qiqihar 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