AI-based diagnosis of clear-cell renal cell carcinoma based on non-contrast CTOA

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.