AI and Cancer Research ── Kidney ── 2026-10-04

Diagram of one new kidney cancer paper. In a retrospective study of 413 clear cell renal cell carcinoma patients, a radiomic model was built from CT features with LASSO and compared with the R.E.N.A.L. nephrometry score. Internal validation AUC was 0.886 vs 0.793 (DeLong P = 0.015). Readers compare cohort, method and validation scope; multi-center prospective validation is still needed. — Image abstract — the whole article on one page (click to enlarge)

1 Kidney papers. Lead: Radiomic model versus R.E.N.A.L. nephrometry score for predicting the likelihood of undergoing partial nephrectomy in clear cell renal cell carcinoma.

Journals covered and how papers are chosen: see the index

Kidney cancer: treatment, trials and AI

Radiomic model versus R.E.N.A.L. nephrometry score for predicting the likelihood of undergoing partial nephrectomy in clear cell renal cell carcinoma.
Frontiers in urology · 2026-09-18 · Evidence: Retrospective observational
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

← Back to this issue