Accurate prognosis prediction is important for treatment planning in lung cancer, but deep learning-driven survival modelling is often limited by the scarcity of curated imaging cohorts with reliable outcome data. This study evaluates whether representations from a domain-specific foundation model can be used for multimodal survival prediction in data-constrained clinical settings.
arXiv:2607. 01001v1 Announce Type: cross Abstract: Radiomics is the established approach for CT-based lung cancer phenotyping, yet comparisons with foundation models rarely isolate contributions of feature extractor, classification head, and segmentation choice, or test cross-cohort robustness.
By Nils Neukirch, Martin Maurer, Nils Strodthoff
The paper introduces a multimodal dataset for survival prediction in resected pancreatic ductal adenocarcinoma, comprising 302 patients, 446 H&E whole-slide images, clinicopathological variables, targeted sequencing data for 154 patients, and overall survival outcomes. The authors evaluated fourteen survival‑prediction models, finding that a Ridge Cox regression on numeric clinicopathological variables achieved the highest concordance (≈0.65), while multimodal fusion of image and molecular data reached 0.619. These benchmarks provide a foundation for future research and external validation using this pancreas‑specific dataset.
By Anh-Tien Nguyen, Mawuko Tettey, Jacqueline Michelle Metsch, Teresa Zimmer, Niklas Ullrich, Mario Duker, Sandra Rungeling, Kirsten Reuter-Jessen, Tessa Rosenthal, Lena-Christin Conradi, Michael Ghadimi, Alexander Konig, Elisabeth Hessmann, Volker Ellenrieder, Philipp Strobel, Hanibal Bohnenberger, Anne-Christin Hauschild
arXiv:2608.24688v1 Announce Type: new
Abstract: Precision oncology necessitates a longitudinal model of patient state that captures cancer evolution and treatment over time, integrating multimodal ob...
By Eugene Vorontsov, Yi Kan Wang, Alican Bozkurt, Adam Casson, Ludmila Tydlitatova, Michal Zelechowski, Ezra E. W. Cohen, Jyoti D. Patel, Max Banaszak, Caitlin McWilliams, Shane Colley, Kate Sasser, Ryan Fukushima, Eric Lefkofsky, Razik Yousfi, Siqi Liu
arXiv:2512.08029v4 Announce Type: replace
Abstract: Clinical decision-making in oncology requires forecasting how disease evolves under treatment, yet most AI systems remain static predictors that ca...
By Tianxingjian Ding, Yuanhao Zou, Chen Chen, Mubarak Shah, Yu Tian
arXiv:2608.21571v1 Announce Type: new
Abstract: Lung cancer remains a leading cause of cancer-related mortality worldwide, and early diagnosis is critical for improving survival. However, early-stage...
By Olivera Kotevska, Ian Goethert, Michael McGee, Maria Mahbub, Sean R. Wilkinson, Rowena Yip, Myvizhi Esai Selvan, Zeynep H. Gumus, Claudia Henschke, Robert J. Klein, Providencia Morales, Samuel M Aguayo, Ioana Danciu, Mayanka Chandrashekar