AI and Cancer Research ── Kidney ── 2026-11-01
Kidney cancer: treatment, trials and AI
- Abstract
Introduction: To develop and validate a multimodal CT-based model integrating intra-peritumoral radiomics, 2.5D deep learning (DL), and clinical-radiological features for preoperative prediction of World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade in older patients with clear cell renal cell carcinoma (ccRCC).
Methods: This multicenter retrospective study included 1,103 patients older than 60 years with pathologically confirmed ccRCC from four hospitals. Patients were allocated to a training cohort (n=459), an internal validation cohort (n=198), and two independent external test cohorts (test1, n=227; test2, n=219). WHO/ISUP grades I–II were classified as low grade and grades III–IV as high grade. Radiomics features were extracted from corticomedullary-phase CT images using intratumoral and progressively expanded intra-peritumoral regions of 1, 2, and 3 mm. In parallel, 2D and 2.5D DL features were extracted using ImageNet-pretrained ResNet34 networks, with the 2.5D approach integrating axial, coronal, and sagittal tumor views. After feature selection, five machine learning classifiers were evaluated. The optimal radiomics model, DL model, and clinical model were integrated using stacking-based decision-level fusion. Model performance was assessed using receiver operating characteristic analysis, calibration curves, decision curve analysis (DCA), and SHapley Additive exPlanations (SHAP).
Results: The random forest (RF) model based on intra-peritumoral 3-mm radiomics features achieved areas under the receiver operating characteristic curve (AUCs) of 0.754 (95% CI, 0.683–0.825) and 0.725 (95% CI, 0.655–0.794) in the test1 and test2 cohorts, respectively. The RF model based on 2.5D DL features achieved corresponding AUCs of 0.756 (95% CI, 0.682–0.830) and 0.713 (95% CI, 0.643–0.783). The multimodal combined model achieved numerically higher external AUCs than the selected single-modality models, reaching 0.775 (95% CI, 0.705–0.845) in test1 and 0.732 (95% CI, 0.664–0.800) in test2. DCA demonstrated favorable clinical net benefit for the combined model, while SHAP analysis identified the 2.5D DL component as the largest contributor to model predictions. Discussion A multimodal CT-based framework integrating intra-peritumoral radiomics, 2.5D DL, and clinical-radiological information enabled noninvasive preoperative prediction of WHO/ISUP grade in older patients with ccRCC and maintained predictive performance across independent external cohorts. This approach may provide complementary imaging and clinical information for preoperative risk stratification and individualized treatment planning.
Journal IF-equivalent: 2.7 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Wang Z, Liang Z, Lv D, Hu S, Zhou H, Li J, et al. Multimodal CT radiomics and 2.5D deep learning for preoperative grading of clear cell renal cell carcinoma in older patients: a multicenter study. Front Oncol. 2026 Oct 30;16:1982970. doi:10.3389/fonc.2026.1982970.Checked: Abstract only - Abstract
Background: Sarcomatoid renal cell carcinoma (sRCC) is a rare and aggressive subtype of renal cell carcinoma with a poor prognosis. Accurate prognostic stratification is critical for individualized treatment planning. This study aimed to develop and validate a machine learning-based prognostic model integrating multiphase CT radiomics and clinicopathological factors to predict overall survival (OS) in patients with pathology-confirmed sRCC.
Methods: A total of 113 patients with pathologically confirmed sRCC who underwent nephrectomy were retrospectively enrolled. Radiomic features were extracted from arterial phase (AP) and venous phase (VP) CT images. After feature selection using intraclass correlation coefficient (ICC > 0.75) and univariate Cox regression, a radiomics signature was constructed using least absolute shrinkage and selection operator (Lasso) regression and random survival forest (RSF) algorithms. A combined model was developed by integrating the radiomics score with independent clinical predictors identified through multivariate Cox regression. Model performance was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Internal validation was performed using bootstrap resampling and repeated cross-validation.
Results: The combined model, incorporating the AP+VP radiomics score, N stage, M stage, and Ki-67 index, showed numerically improved predictive performance compared with the clinical model. In the held-out test set, it achieved a C-index of 0.737 (95% CI: 0.612–0.846), which was comparable to the AP+VP radiomics model alone (C-index: 0.735) and numerically higher than the clinical model (C-index: 0.615); however, the improvement over the clinical model was not statistically significant (P = 0.115). AUCs for 1-, 2-, and 3-year OS were 0.833, 0.780, and 0.749, respectively. The model showed good calibration and clinical utility across a range of threshold probabilities. Significant survival stratification was achieved between high- and low-risk groups (log-rank P < 0.05).
Conclusions: The proposed machine learning model combining multiphase CT radiomics and clinicopathological factors may provide a noninvasive tool for predicting OS in sRCC patients. It showed numerically improved performance compared with conventional clinical models; however, this incremental benefit was not statistically significant, and its performance was comparable to the radiomics-only model. Further multicenter validation is warranted to confirm its generalizability.
Journal IF-equivalent: 2.7 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Liu T, Yao Y, Zhou Z, Hu Y, Sun L, Zhang G. Development and validation of a multiphase CT radiomics-based machine learning model for prognostic prediction in sarcomatoid renal cell carcinoma. Front Oncol. 2026 Oct 30;16:1982796. doi:10.3389/fonc.2026.1982796.Checked: Abstract only