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arXiv:2609.09510v1 Announce Type: cross Abstract: High-risk non-muscle-invasive bladder cancer (HR-NMIBC) carries substantial risks of recurrence and progression, while current clinical risk stratifi...
arXiv:2605. 25050v2 Announce Type: replace-cross Abstract: Integrating multimodal datasets in clinical oncology is frequently hindered by high dimensionality and blockwise missingness, where entire data sources are unavailable for specific patient subsets.
Active surveillance (AS) is the preferred strategy for favorable-risk prostate cancer, yet current protocols rely on scheduled repeat biopsies, most of which reveal no progression and are unnecessary. Existing risk-stratification tools operate on single time-point imaging or depend on explicit lesion segmentation, limiting their ability to capture longitudinal change and excluding patients without an MRI-visible lesion.
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.
arXiv:2606. 17115v1 Announce Type: cross Abstract: Foundation models (FMs) have emerged as powerful representation extractors for medical data, yet their generalizability to datasets under distribution shift remains underexplored.
arXiv:2607. 06919v1 Announce Type: cross Abstract: Artificial intelligence (AI) analysis of micro-ultrasound ($\mu$US) has shown promise for prostate cancer (PCa) detection.