Introduction The accurate characterization of renal tumors

particularly clear-cell renal cell carcinoma (ccRCC)

traditionally requires contrast-enhanced computed tomography (CECT)

which is contraindicated in many patients. Non-contrast CT (NCCT) is widely accessible but is considered limited for tumor analysis. This study aims to develop and validate a deep learning system for automated renal tumor localization and ccRCC classification using only NCCT. Methods In a multicenter retrospective study of 1

902 patients with paired NCCT and CECT series

expert annotations were propagated to NCCT via 3D hybrid affine and deformable registration. An integrated AI pipeline combining a 3D U-Net for segmentation with three 3D classifiers (ResNet-50

Vision Transformer

Swin Transformer) was developed. Three model configurations (NCCT-only

CECT-only

multiseries fusion) were evaluated using area under the curve (AUC)

accuracy

sensitivity

specificity

and DeLong’s test. Results The NCCT-based segmentation model achieved tumor Dice coefficients approaching the CECT-based model (0.89 ± 0.08 vs. 0.96 ± 0.05 internally). For classification

NCCT models showed performance that was noninferior to that of CECT models

with no significant AUC differences (internal: 0.80 vs. 0.78 for ResNet-50). On the external test set (n = 231)

NCCT model achieved AUCs of 0.76–0.82

which was comparable to the CECT model. The fusion model outperformed the NCCT model using ResNet-50

which consistently surpassed transformer-based architectures. Conclusions This study demonstrates an AI system for accurate renal tumor localization and ccRCC classification directly from NCCT

achieving performance comparable to CECT-based models. This contrast-free approach could enhance accessible renal cancer screening and provide diagnostic support for patients with contraindications to contrast media or in resource-limited settings.