AI and Cancer Research ── 2026-11-10

A wooden board where threads sort clay tokens into twenty slots, beside an identical empty board: AI classification awaiting outside validation. — What this means, as an image (AI-generated, GPT Image): A few routine clinical fields let AI narrow a cancer's origin, but it can't be trusted until tested at other sites.Download image (PNG, 2000×800)

4 papers in this issue. Lead: Pan-Cancer Classification in Clinical Applications: A Comprehensive Analysis using Machine Learning Models

New papers on cancer and AI, collected from journal feeds, PubMed (searched across all journals) and OpenAlex (a public index used instead of Google Scholar), judged by Jev (an AI that makes language judgements): original research, AI central, which of 13 themes, treatment or trials, and then ranked by importance up to a daily limit. Journals are not filtered by impact factor; each paper shows its journal's IF-equivalent (OpenAlex two-year mean citedness) and the date of that value. Each entry shows the full original abstract.

All cancers: Cancer biology（2）

Hematologic malignancies: AML（0） · CML（0） · ALL（0） · CLL（0） · Lymphoma（0） · Myeloma（0）

Solid tumors: Breast（1） · Lung（0） · Colorectal（0） · Pancreatic（0） · Prostate（0） · Brain（0） · Ovarian（0） · Endometrial（0） · Gastric（0） · Liver（1） · Kidney（0） · Bladder（0）

Rare cancers: ACC（0）

Cancer molecular biology and AI

Pan-Cancer Classification in Clinical Applications: A Comprehensive Analysis using Machine Learning Models
SUST Journal of Science and Technology (SUST JST) · 2026-11-08 · Evidence: Retrospective observational (model development on an existing clinical dataset; no external validation)
Abstract
Cancer affects millions of people worldwide, and accurate, timely classification of cancer type is closely tied to treatment selection and patient outcomes — particularly in metastatic disease or Cancer of Unknown Primary, where identifying the tissue of origin directly informs site specific treatment. Multiple clinical and genomic variables, including mutation count, tumor stage, diagnosis age, tumor purity, smoking status and sex are individually associated with cancer behavior, but whether their combined use improves classification across a broad range of cancer types — rather than within a single cancer type, as in most prior work — has rarely been tested directly. This study evaluates four machine learning classifiers — Random Forest, Extreme Gradient Boosting, Support Vector Machine, and Artificial Neural Network — on a clinical pancancer dataset spanning 20 cancer types (6,110 patients; 1,222 held out for testing), using ten routinely available clinical and summary genomic covariates. XGBoost achieved the strongestperformance (accuracy 87.0%, 95% CI: 85.0–88.8%; F1-score 0.869; ROC AUC 0.993), with Random Forest close behind; both outperformed SVM and ANN by a margin confirmed as statistically significant through 5-fold cross-validation. Rather than assuming that the selected variables jointly contribute to classification performance, this study tests that claim directly using permutation feature importance, finding that multiple features contribute distinct, non-redundant predictive value across all four models. This performance is broadly comparable to prior multi-class pancancer classification studies covering fewer cancer types (13–14) while relying on substantially higher-dimensional genomic inputs, suggesting that a smaller, more clinically accessible feature set can support classification across a wider range of cancer types. These results support the feasibility of clinical-data-driven pan-cancer classification as a direction for future decision-support research; external validation on independent, multi-institutional cohorts is required before any clinical application.
Journal IF-equivalent: 0.0 (OpenAlex 2-year mean citedness, value as of 2026-11-09, retrieved 2026-11-10; not the official Clarivate IF)
Reference: Md. Shadmim Hasan Sifat, Rahman MS. Pan-Cancer Classification in Clinical Applications: A Comprehensive Analysis using Machine Learning Models. SUST Journal of Science and Technology. 2026 Nov 8;35(2). doi:10.63512/sustjst.2025.2002.
Checked: Abstract only
ACAP-AG: A graph neural network model for anticancer activity prediction by integrating heterogeneous data.
Journal of pharmaceutical analysis · 2026-06-01 · Evidence: Preclinical (computational prediction model)
Abstract
• ACAP-AG overcomes the difficulty of heterogeneous data and large-scale prediction. • ACAP-AG accurately predict therapy sensitivity/resistance in cancer patients. • ACAP-AG prioritizes NP derivatives with optimal antihepatoma activity. [Figure: see text]
Journal IF-equivalent: 7.7 (OpenAlex 2-year mean citedness, value as of 2026-11-09, retrieved 2026-11-10; not the official Clarivate IF)
Reference: Wang YC, Hu MM, Li TZ, Li SZ, Chen JJ. ACAP-AG: A graph neural network model for anticancer activity prediction by integrating heterogeneous data. J Pharm Anal. 2026 Oct;16(10):101680. doi:10.1016/j.jpha.2026.101680. PMID: 42999687; PMCID: PMC13744312.
Checked: Abstract only

See all 2 Cancer biology papers →