Prediction of Mutations and Outcome in Gastrointestinal Stromal Tumors with Deep Learning: A Multicenter, Multinational Study
Year: 2026
Type: preprint
Abstract: Background Gastrointestinal stromal tumor (GIST) is the most common gastrointestinal mesenchymal tumor, driven by tyrosine-protein kinase KIT and platelet-derived growth factor receptor A (PDGFRA) mutations. Specific variants, such as KIT exon 11 deletions, carry prognostic and therapeutic implications, whereas wild-type (WT) variants derive limited benefit from tyrosine kinase inhibitors (TKIs). Given the limited reproducibility of established clinicopathological risk models, deep learning (DL) applied to whole-slide images (WSIs) emerged as a promising tool for molecular classification and prognostic assessment. Patients and methods We analyzed 8398 GIST cases from 21 centers in 7 countries, including 7238 with molecular data and 2638 with clinical follow-up. DL models were trained on WSIs to predict mutations, treatment sensitivity, and recurrence-free survival (RFS). Results DL predicted mutational status in GIST from WSIs, with area under the curve (AUC) of 0.87 for KIT , 0.96 for PDGFRA . High performance was observed for subtypes, including KIT exon 11 delinss 557–558 (0.67) and PDGFRA exon 18 D842V (0.93). For therapeutic categories, performance reached 0.84 for avapritinib sensitivity, 0.81 for imatinib sensitivity. DL models predicted RFS, with hazard-ratios (HR) of 8.44 (95%CI 6.14–11.61) in the overall cohort and 4.74 (95%CI 3.34–6.74) in patients receiving adjuvant therapy. Prognostic performance was comparable to pathology-based scores, with highest discrimination in the overall cohort and in patients without adjuvant therapy (9.44, 95%CI (5.87–15.20)). Conclusion DL applied to WSIs enables prediction of molecular alterations, treatment sensitivity, and RFS in GIST, performing comparably to established risk scores across international cohorts, providing a baseline for future multimodal predictors. Highlights Deep learning on histology predicts KIT and PDGFRA mutations in a large international cohort of GISTs from multiple centers Whole-slide image models stratify recurrence-free survival comparable to pathology-based risk scores Prognostic value of deep learning is preserved in adjuvant therapy subgroups, supporting treatment duration decisions Graphical abstract. Overview of study design and dataset characteristics. (A) Multinational collection of WSIs from seven countries (Spain, France, Italy, Germany, the Netherlands, Poland, and Japan), followed by standard image preprocessing with the STAMP pipeline and clinical data preprocessing/standardization via the Grammar Data Curation framework. The workflow was divided into two main branches: (i) molecular mutation and treatment sensitivity prediction, and (ii) RFS prediction. Model performance was evaluated using AUROC and F1 score for classification tasks, and Kaplan–Meier survival curves with hazard ratios for RFS. Model explainability was assessed through heatmaps of WSIs and identification of top predictive tiles. (B) Summary of clinical dataset composition: proportion of cases receiving adjuvant therapy, tumor location distribution, mutation distribution at the exon level, and mutation distribution at the codon level.
Source: medRxiv
Authors: A. Bonetti, V.L. Le, Z. I. Carrero, F. Wolf, M. Gustav, S.W. Lam, L. Vanhersecke, P. Sobczuk, F. Le Loarer, M. Lenarcik, P. Rutkowski, J. M. van Sabben, N. Steeghs, H. van Boven, I. Machado, S. Bagué, S. Navarro, E. Medina-Ceballos, C. Agra, F. Giner, G. Tapia, A. Hernández-Gallego, G. Civantos Jubera, M. Cuatrecasas, S. Lopez-Prades, R.E. Perret, I. Soubeyran, E. Khalifa, L. Blouin, E. Wardelmann, A. Meurgey, P. Collini, A. Voloshin, Y. Yatabe, H. Hirano, A. Gronchi, T. Nishida, O. Bouché, J.F. Emile, C. Ngo, P. Hohenberger, C. Cotarelo, J. Jakob, J.V.M.G. Bovee, H. Gelderblom, A. Szumera-Cieckiewicz, M. Jean-Denis, J. Bollard, N. Lassau, A. Lecesne, J.Y. Blay, A. Italiano, A. Crombé, J.M. Coindre, J. N. Kather
Institutions: University Hospital Carl Gustav Carus, Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden, Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Université de Bordeaux, Centre Inria de l'université de Bordeaux, Institut Bergonié, Institut de Mathématiques de Bordeaux, Leiden University Medical Center, The Maria Sklodowska-Curie National Research Institute of Oncology, Inserm, BoRdeaux Institute of onCology, The Netherlands Cancer Institute, Utrecht University, University Medical Center Utrecht, Universitat de València, Instituto de Salud Carlos III, Fundación Instituto Valenciano de Oncología, Centro de Investigación Biomédica en Red de Cáncer, Hospital de Sant Pau, INCLIVA Health Research Institute, Hospital General Universitario Gregorio Marañón, Hospital Universitari i Politècnic La Fe, Universitat Autònoma de Barcelona, Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Hospital Universitari Germans Trias i Pujol, Hospital Universitario Virgen del Rocío, Hospital Clínic de Barcelona, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Consorci Institut D'Investigacions Biomediques August Pi I Sunyer, Fundació de Recerca Clínic Barcelona-Institut d’Investigacions Biomèdiques August Pi i Sunyer, Universitat de Barcelona, University of Münster, University Hospital Münster, Centre Léon Bérard, Fondazione IRCCS Istituto Nazionale dei Tumori, National Cancer Center Hospital, University of Milan, Osaka Hospital, Japan Community Healthcare Organization, Centre Hospitalier Universitaire de Reims, Université Paris-Saclay, Assistance Publique – Hôpitaux de Paris, Hôpital Ambroise-Paré, Biomarqueurs et essais cliniques en Cancérologie et Onco-Hématologie, Institut Gustave Roussy, Heidelberg University, University Medical Centre Mannheim, Medizinische Fakultät Mannheim, University of Leeds, University Hospital Heidelberg, National Center for Tumor Diseases
Language: English
Subfield: Gastroenterology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7127374833