Events

Failure of conventional imaging to detect pancreatic ductal adenocarcinoma (PDA) at its visually occult pre-diagnostic stage is a primary barrier to improving its otherwise poor survival. Over 85% of cases are diagnosed at an unresectable stage, and sporadic PDA — 85-90% of all cases — still has no recognised early detection strategy. This talk introduces REDMOD (Radiomics-based Early Detection MODel), a fully automated AI framework that identifies subvisual radiomic signatures of pre-diagnostic PDA on standard-of-care CT. REDMOD couples AI-driven volumetric pancreas segmentation with a heterogeneous ensemble classifier (logistic regression, random forest, XGBoost) trained on a 40-feature radiomic signature, and was deliberately validated in a low-prevalence setting (~6:1 control-to-case ratio) that reflects real early-detection cohorts rather than the balanced case-control designs used in earlier work.

On an independent test set of 493 scans, REDMOD detected occult PDA with an AUC of 0.82 and 73.0% sensitivity at a median lead time of 475 days before clinical diagnosis — nearly twice the sensitivity of board-certified radiologists (38.9%), widening to nearly threefold (68.0% vs 23.0%) for scans acquired more than 24 months before diagnosis. The model held 90-92% test-retest concordance across serial scans and generalised its specificity to independent multi-institutional (81.3%, n=539) and public NIH-PCT (87.5%, n=80) cohorts. Mechanistically, 90% of the selected signature came from multi-scale wavelet-filtered textural features, which significantly outperformed unfiltered features (AUC 0.82 vs 0.74) — suggesting the earliest detectable manifestations of PDA are subtle, diffuse architectural disruptions rather than any discrete mass. The work appears in Gut (2026).

Spatial transcriptomics enables spatial gene expression profiling, motivating computational models that capture spatially conditioned regulatory relationships. We introduce SAGE-FM, a lightweight spatial transcriptomics foundation model based on graph convolutional networks (GCN) trained with a masked-central-spot prediction objective. Trained on 416 human Visium samples spanning 15 organs, SAGE-FM learns spatially coherent embeddings that recover masked genes robustly, with 91% of masked genes showing significant correlations (p < 0.05).

SAGE-FM generalizes to downstream tasks, enabling 81% accuracy in pathologist-defined spot annotation in oropharyngeal squamous cell carcinoma and improving glioblastoma subtype prediction relative to MOFA. In silico perturbation experiments further show that the model captures directional ligand-receptor and upstream-downstream regulatory effects consistent with ground truth. These results demonstrate that simple, parameter-efficient GCNs can serve as biologically interpretable and spatially aware foundation models for large-scale spatial transcriptomics.

This work surfaces a class of failure mode the authors call “mirage reasoning” in multimodal foundation models used for medical image understanding. They show that frontier vision-language models will generate detailed, confident image descriptions — including pathology-laden clinical findings — for images that were never actually provided. The models also achieve surprisingly high scores on multimodal benchmarks with no image input at all; in one test, a leading model topped a chest X-ray benchmark while having no access to the underlying images.

When prompted to explicitly guess in the absence of an image rather than implicitly assume one was provided, performance dropped sharply — suggesting the models default to confident fabrication rather than conservative refusal whenever the prompt’s framing allows it. The authors argue this exposes fundamental vulnerabilities in current multimodal evaluation practice and propose B-Clean, an assessment framework for fair, vision-grounded evaluation of multimodal systems in medicine.