AI and Cancer Research ── Prostate ── 2026-10-09

A figure on one new paper about prostate cancer and AI. It targets post-diagnostic prostate cancer using numerical, categorical and text features. The method has two modes: a comprehensive stage model and a minimal advanced-stage model. Results: ROC-AUC 0.95 on trial data, 0.86 cross-dataset, a 9.51 MB minimal model with inference under 50 ms. SHAP explanations and flagging of low-confidence predictions for expert review surround it. Readers compare the evidence stage and evaluation scope. — Image abstract — the whole article on one page (click to enlarge)

1 Prostate papers. Lead: Resource-efficient multimodal AI for post-diagnostic prostate cancer risk stratification for home-based monitoring

Journals covered and how papers are chosen: see the index

Prostate cancer: treatment, trials and AI

Resource-efficient multimodal AI for post-diagnostic prostate cancer risk stratification for home-based monitoring
Scientific Reports · 2026-10-06 · Evidence: Retrospective observational (model development on existing trial data with cross-dataset evaluation)
Abstract
Prostate cancer remains one of the most prevalent malignancies among men worldwide. Effective post-diagnostic management requires repeated staging and longitudinal risk assessment. However, existing clinical decision support systems (CDSS) rely predominantly on structured and resource-intensive clinical data, which limits their scalability and accessibility outside conventional care settings. This study presents a resource-efficient multimodal AI framework for post-diagnostic prostate cancer staging and advanced-stage risk stratification for home-based monitoring. The system integrates potentially home-accessible inputs, including numerical, categorical, and unstructured textual features, systematically selected from the Cancer Screening Trial dataset through supervised machine learning and is implemented as a standalone, user-facing application. The dual-mode framework consists of a comprehensive model for multiclass stage classification and a minimal model for binary advanced-stage risk stratification using a reduced feature set, balancing predictive performance with resource efficiency. The CDSS demonstrates discriminatory performance in a retrospective analysis of the Prostate, Lung, Colorectal and Ovarian cancer screening trial dataset (ROC-AUC 0.95), while cross-dataset evaluation of a feature-restricted model using the Surveillance, Epidemiology, and End Results dataset showed an ROC-AUC of 0.86 under limited feature overlap. The minimal model has a lightweight implementation with a serialized size of 9.51 MB and sub-50-ms CPU inference latency, supporting real-time deployment in resource-constrained settings. Model interpretability is supported through SHapley Additive exPlanations, while an uncertainty thresholding mechanism enables the system to flag low-confidence predictions for expert review. This work presents a patient-facing prototype for oncology risk assessment, with potential for future evaluation in resource-constrained, decentralized, and home-based care settings.
Journal IF-equivalent: 5.2 (OpenAlex 2-year mean citedness, value as of 2026-10-04, retrieved 2026-10-05; not the official Clarivate IF)
Reference: Khan AS, Tabassum S, Nag AK, Majumder S. Resource-efficient multimodal AI for post-diagnostic prostate cancer risk stratification for home-based monitoring. Sci Rep. 2026 Oct 7 [Epub ahead of print]. doi:10.1038/s41598-026-75102-1.
Checked: Abstract only

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