S1544
Physics - Autosegmentation
ESTRO 2026
Digital Poster 142 Automated body composition analysis in borderline resectable pancreatic cancer: robust AI, disappointing clinical translation William Gehin 1 , Aurélien Lambert 2 , Jean-Emmanuel Bibault 3 1 Radiation therapy, Institut de Cancérologie de Lorraine, Vandoeuvre-lès-Nancy, France. 2 Oncology, Institut de Cancérologie de Lorraine, Vandoeuvre-lès- Nancy, France. 3 Radiation therapy, Hôpital Européen Georges Pompidou, Paris, France Purpose/Objective: Artificial intelligence (AI) research in oncology and radiation oncology is expanding exponentially, with thousands of publications annually. Many works propose AI-based tools for therapeutic decision support, particularly in rare or complex settings such as borderline resectable pancreatic cancer (BRPC), where patients face a difficult balance between intensive multimodal therapy and palliative strategies prioritizing quality of life. Yet clinical translation of such tools remains scarce. We aimed to evaluate whether automated body composition analysis could serve as a prognostic biomarker in BRPC and illustrate the challenges of AI translation into clinical practice. Material/Methods: Baseline CT scans from 107 patients with evaluable imaging among 110 randomized in the PRODIGE 44 BRPC trial were analyzed with a fully automated segmentation pipeline integrated in the clinical workflow. Skeletal muscle index (SMI) and additional 2D/3D body composition biomarkers were extracted. Associations with overall survival (OS), progression- free survival (PFS), and completion of multimodal therapy (chemotherapy ± radiotherapy ± surgery) were tested both as continuous variables and using published sarcopenia cut-offs. Results: Automated segmentation achieved excellent accuracy (Dice >0.9 vs manual reference), confirming technical feasibility. Sarcopenia prevalence varied widely according to the cut-off applied (30–60%). Neither SMI nor other continuous 2D/3D body composition biomarkers consistently predicted OS, PFS, or treatment completion. The tool, while technically robust and seamlessly integrable in clinical workflows, did not provide actionable patient stratification in this homogeneous prospective cohort. Conclusion: This study illustrates the paradox of AI in oncology: technically strong algorithms, addressing legitimate clinical questions, but potentially disappointing clinical utility. Automated sarcopenia analysis failed to stratify BRPC patients for multimodal therapy in a randomized trial setting, despite methodological rigor. This
represents a typical example of the current challenges in translational AI research, where only very few tools are rigorously validated and ultimately adopted in routine oncology and radiation oncology practice. Keywords: sarcopenia, AI, clinical translation Digital Poster 273 GTV segmentation in MRI guided radiotherapy with promptable foundation models Tom Julius Blöcker 1 , Nikolaos Delopoulos 1 , Miguel A. Palacios 2 , Sebastian Klüter 3 , Juliane Hörner-Rieber 3,4 , Carolin Rippke 3 , Lorenzo Placidi 5 , Luca Boldrini 6,7 , Vincenzo Frascino 7 , Nicolaus Andratschke 8 , Michael Baumgartl 8 , Riccardo Dal Bello 8 , Sebastian N. Marschner 1 , Claus Belka 1,9 , Stefanie Corradini 1,10 , Denis Dudas 1 , Marco Riboldi 11 , Christopher Kurz 1 , Guillaume Landry 1,9 1 Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany. 2 Dept. of Radiation Oncology, Amsterdam UMC, Vrije Universiteit Medical Centre, Amsterdam, Netherlands. 3 Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany. 4 Department of Radiation Oncology, University Hospital Düsseldorf, Düsseldorf, Germany. 5 Medical Physics Unit, Dipartimento di Diagnostica per Immagini e Radioterapia oncologica, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy. 6 Institute of Radiology, Università Cattolica del Sacro Cuore, Rome, Italy. 7 Radiation therapy unit, Dipartimento di Diagnostica per Immagini e Radioterapia oncologica, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy. 8 Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland. 9 Bavarian Cancer Research Center (BZKF), Partner Site Munich, Munich, Germany. 10 Department of Radiation Oncology, Universitätsklinikum Erlangen, Erlangen, Germany. 11 Department of Medical Physics, Ludwig-Maximilians-Universität (LMU), Munich, Germany
Purpose/Objective: Magnetic resonance imaging (MRI) guided
radiotherapy (MRIgRT) requires the delineation of gross tumor volumes (GTV) in daily MRI from MRI- linacs. Specialised models have been developed for automatic segmentation of tumors from specific anatomical regions, but with limited performance, due to complex and varied GTV geometry and often subtle appearance.This study explores promptable foundation models for AI-assisted GTV segmentation across multiple tumor sites. Material/Methods: Promptable foundation models were driven by six
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