AI and Cancer Research ── 2026-10-04
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.
Read by cancer type: Cancer biology（0） · Breast（0） · Lung（0） · Pancreatic（0） · Prostate（1） · ACC（0） · Brain（0） · Ovarian（0） · Endometrial（0） · Gastric（0） · Liver（0） · Kidney（1） · Bladder（0）
Cancer molecular biology and AI
No new qualifying paper for this issue.
Breast cancer: treatment, trials and AI
No new qualifying paper for this issue.
Lung cancer: treatment, trials and AI
No new qualifying paper for this issue.
Pancreatic cancer: treatment, trials and AI
No new qualifying paper for this issue.
Prostate cancer: treatment, trials and AI
- Abstract
BACKGROUND: Advances in the care of patients with prostate cancer across the disease spectrum have resulted in substantial survival gains over recent decades. However, global disparities in prostate cancer survival, stage at diagnosis, and access to timely diagnosis and treatment remain among the greatest across all malignancies. We aimed to quantify country-level health system correlates of the prostate cancer mortality-to-incidence ratio (MIR). METHODS: We applied interpretable machine learning using CatBoost with SHAP (SHapley Additive exPlanations) to all 185 countries with prostate cancer MIR estimates in GLOBOCAN 2022. National age-standardized MIR estimates were linked to 11 health system indicators from the World Health Organization Global Health Observatory, World Bank World Development Indicators, United Nations agencies, and the Directory of Radiotherapy Centres. Model performance was evaluated using repeated leave-one-country-out cross-validation with bootstrap-based uncertainty estimation. CatBoost handled missing predictor values natively, allowing inclusion of all countries without imputation. RESULTS: The model demonstrated strong predictive performance for prostate cancer MIR (R² = 0.80, RMSE = 0.078, MAE = 0.060, Pearson correlation = 0.89). The health system features most strongly associated with lower MIR were gross domestic product per capita, the universal health coverage (UHC) service coverage index, and radiotherapy infrastructure. Country-level SHAP decompositions demonstrated heterogeneous feature contributions across settings, identifying context-specific priorities. Greater radiotherapy capacity and more comprehensive UHC were consistently associated with lower MIR, whereas higher aggregate health expenditure alone showed weaker associations with improved outcomes. CONCLUSIONS: Interpretable machine learning identified radiotherapy infrastructure, universal health coverage, and national wealth as the health system features most consistently associated with lower prostate cancer mortality-to-incidence ratios across countries. These findings suggest that targeted investment in treatment capacity and service coverage may have greater relevance to prostate cancer outcomes than increases in aggregate health spending alone. Although ecological and hypothesis-generating, this framework may inform future health systems research, prospective evaluation, and global prostate cancer control.
Journal IF-equivalent: 5.8 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-04; not the official Clarivate IF)Reference: Patel MS, Janopaul-Naylor J, Ting FIL, Wu JF, McBride SM, Rathkopf DE, et al. Interpretable machine learning identifies health system levers for survival outcomes of patients with prostate cancer. Prostate Cancer Prostatic Dis. 2026 Oct 2 [Epub ahead of print]. doi:10.1038/s41391-026-01156-x. PMID: 42827104.Checked: Abstract only
Adenoid cystic carcinoma: treatment, trials and AI
No new qualifying paper for this issue.
Brain tumours: treatment, trials and AI
No new qualifying paper for this issue.
Ovarian cancer: treatment, trials and AI
No new qualifying paper for this issue.
Endometrial cancer: treatment, trials and AI
No new qualifying paper for this issue.
Gastric cancer: treatment, trials and AI
No new qualifying paper for this issue.
Liver cancer: treatment, trials and AI
No new qualifying paper for this issue.
Kidney cancer: treatment, trials and AI
- Abstract
OBJECTIVES: To develop a CT-based radiomic model for predicting partial nephrectomy (PN) in clear cell renal cell carcinoma (ccRCC) and compare it with the R.E.N.A.L. Nephrometry Score (RNS). METHODS: This retrospective study included 413 ccRCC patients who underwent PN or radical nephrectomy. The RNS was calculated from preoperative CT. Radiomic features were extracted from corticomedullary-phase CT. Reproducibility was assessed using intraclass correlation coefficients (ICCs). LASSO regression was used for feature selection and Rad-score construction. Multivariable logistic regression models were built based on the total RNS sum and the Rad-score. Performance was evaluated by AUC, calibration, and decision curve analysis. RESULTS: Of 1210 extracted features (after excluding general_info fields), 851 showed good to excellent reproducibility (ICC > 0.75). The radiomic model (6 selected features) achieved an AUC of 0.886 (95% CI: 0.81-0.91) in the internal validation cohort, significantly outperforming the RNS model (AUC = 0.793, 95% CI: 0.72-0.85; DeLong test, P = 0.015). The radiomic model also demonstrated better calibration (Brier score: 0.11 vs. 0.16) and higher clinical net benefit. SHAP analysis identified tumor size and texture heterogeneity as the strongest predictors. CONCLUSION: A CT-based radiomic model provides superior accuracy over the RNS for preoperatively predicting PN likelihood in ccRCC and holds significant potential to augment surgical decision-making and personalize treatment planning. Prospective multi-center validation is warranted before routine clinical adoption.
Journal IF-equivalent: 1.3 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-04; not the official Clarivate IF)Reference: Liu Fd, Yu Hl, Wang Dj, Ju Xb, Yang Yh. Radiomic model versus R.E.N.A.L. nephrometry score for predicting the likelihood of undergoing partial nephrectomy in clear cell renal cell carcinoma. Front Urol. 2026 Sep 18;6:1928982. doi:10.3389/fruro.2026.1928982. PMID: 42827468.Checked: Abstract only
Bladder cancer: treatment, trials and AI
No new qualifying paper for this issue.