CT based deep learning model for differentiating primary renal sarcomas from large renal cell carcinomas

Abstract Background Due to the prognosis and treatment differences between primary renal sarcomas and renal cell carcinoma, preoperative differentiation between them is important but challenging. This study aims to explore and develop a diagnostic method based on computed tomography (CT) and clinical data for preoperatively differentiating primary renal sarcomas from large renal cell carcinomas. Methods Patients pathologically diagnosed with primary renal sarcoma from two center between 2009–2021 were retrospectively included, and large renal cell carcinomas were probably 2:1 compared to renal sarcomas as the control group. Clinical data, standard contrast-enhanced CT images and histological findings were obtained. A clinical model was established with independent indicators based on logistic regression analysis. The region of interest was outlined in each three modal CT images (unenhanced phase [UP], corticomedullary phase [CMP] and nephrographic phase [NP]) and formed 7 modal imaging datasets for deep learning models’ development. Reported performance metrics included accuracy and areas under the receiver operating characteristic curves (AUC). Results Totally, 27 renal sarcomas and 58 large RCCs were enrolled. Multivariate logistic regression showed that the independent indicators of renal sarcoma were intratumoral artery and Gerota’s fascia invasion (P < 0.05). The AUC of clinical model was 0.77 (95% confidence interval [CI]: 0.67–0.87), sensitivity 0.74, specificity 0.67, positive predictive value 0.51, and negative predictive value 0.85. The deep learning models yielded effective discrimination. The unenhanced phase model yielded AUC = 0.95±0.09 and accuracy (ACC) = 0.94±0.07, and UP + NP model yielded nearly AUC = 0.95±0.06, and ACC = 0.94±0.07. Conclusion The deep learning models based on multimodal CT images show good performance for differentiating renal sarcomas from large renal cell carcinomas, which assist in individualized management.