CT-based AI risk support for cystic renal masses: multicenter validation, reader study, and a randomized implementation study.

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Abstract

Cystic renal masses are frequently overtreated. We developed and evaluated the Kidney Imaging-based Triage Engine for Cystic Renal Masses (KITE-CRM), a computed tomography (CT)-based deep-learning pathway using artificial intelligence (AI) to support malignancy risk assessment in cystic renal masses (CRMs). We conducted a multicentre development and validation study in China. In phase 1 (January 2020-December 2021), we developed the system in a retrospective training cohort from a tertiary referral center. CRMs were defined as renal lesions with predominantly cystic features. Corticomedullary-phase contrast-enhanced CT was the sole model input. In phase 2 (January 2022-October 2025), we externally validated the model across five independent hospitals. The core task was lesion-level malignancy discrimination; secondary tasks classified malignant lesions as clear-cell renal cell carcinoma (ccRCC) versus other malignant subtypes and stratified ccRCC as high-grade versus low-grade disease. Retrospective cohorts comprised 3150 patients. The core task used 570 training cases (142 malignant), 235 internal-validation cases (137 malignant), and 528 external cases (160 malignant). Three-dimensional processing used one index lesion per patient and a fixed threshold of 0.586442888. In a randomized crossover multi-reader, multi-case study, six radiologists interpreted 117 external-cohort cases with and without AI; outcomes were area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, reading time, and user acceptance. In phase 3 (November 2025-February 2026), 5869 eligible consecutive adults undergoing contrast-enhanced abdominal CT at Changhai Hospital were randomized 1:1 to AI-assisted (n = 2934) or standard reporting (n = 2935). The primary clinical endpoint was the proportion of pathologically benign lesions among treated lesions. The trial was registered with the Chinese Clinical Trial Registry (ChiCTR2500112290); model code is publicly available. Internal-validation AUROC was 0.996 (95% confidence interval [CI], 0.990-0.999) and Brier score 0.046 (0.036-0.058). External AUROCs were 0.913-0.999, excluding Test 4 (AUROC 1.000; three malignant cases). External sensitivity ranged from 19/24 (79.2%) to 3/3 (100.0%) and specificity from 100/116 (86.2%) to 33/33 and 115/115 (100.0%). AI assistance increased reader-averaged AUROC from 0.825 to 0.973 (difference, 0.149; 95% CI, 0.091-0.206) and reduced mean interpretation time from 9.7 to 5.5 min. Benign pathology occurred in 28/171 (16.4%) treated lesions with AI versus 104/258 (40.3%) with standard care (difference, -23.9 percentage points; 95% CI, -31.7 to -15.4). The treated AI-evaluable CRM subgroup difference was non-significant (5/29 [17.2%] versus 19/56 [33.9%]; p = 0.105). In post hoc intention-to-treat analysis, benign intervention occurred in 28/2934 (0.95%) versus 104/2935 (3.54%) randomized participants (risk difference, -2.59 percentage points; 95% CI, -3.38 to -1.85; p < 0.0001). Median reporting time was 7.6 versus 18.5 min. KITE-CRM improved assisted interpretation and reporting efficiency. Management findings do not establish long-term oncologic safety or CRM-specific clinical superiority. The model complements, rather than replaces, Bosniak assessment, indicated multiphase CT or magnetic resonance imaging (MRI), and clinical judgment. Broader prospective evaluation is needed. National Natural Science Foundation of China and Shanghai municipal research programmes.

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