Pretreatment determination of renal cell carcinoma aggressiveness may help to guide clinical decision-making. Purpose: To evaluate the efficacy of residual convolutional neural network using routine MRI in differentiating low-grade (grade I–II) from high-grade (grade III–IV) in stage I and II renal cell carcinoma. Study Type: Retrospective. Population: In all, 376 patients with 430 renal cell carcinoma lesions from 2008–2019 in a multicenter cohort were acquired. The 353 Fuhrman-graded renal cell carcinomas were divided into a training, validation, and test set with a 7:2:1 split. The 77 WHO/ISUP graded renal cell carcinomas were used as a separate WHO/ISUP test set. Field Strength/Sequence: 1.5T and 3.0T/T2-weighted and T1 contrast-enhanced sequences. Assessment: The accuracy, sensitivity, and specificity of the final model were assessed. The receiver operating characteristic (ROC) curve and precision-recall curve were plotted to measure the performance of the binary classifier. A confusion matrix was drawn to show the true positive, true negative, false positive, and false negative of the model. Statistical Tests: Mann–Whitney U-test for continuous data and the chi-square test or Fisher's exact test for categorical data were used to compare the difference of clinicopathologic characteristics between the low- and high-grade groups. The adjusted Wald method was used to calculate the 95% confidence interval (CI) of accuracy, sensitivity, and specificity. Results: The final deep-learning model achieved a test accuracy of 0.88 (95% CI: 0.73–0.96), sensitivity of 0.89 (95% CI: 0.74–0.96), and specificity of 0.88 (95% CI: 0.73–0.96) in the Fuhrman test set and a test accuracy of 0.83 (95% CI: 0.73–0.90), sensitivity of 0.92 (95% CI: 0.84–0.97), and specificity of 0.78 (95% CI: 0.68–0.86) in the WHO/ISUP test set. Data Conclusion: Deep learning can noninvasively predict the histological grade of stage I and II renal cell carcinoma using conventional MRI in a multiinstitutional dataset with high accuracy. Level of Evidence: 3. Technical Efficacy Stage: 2.
- deep learning
- histological grade
- renal cell carcinoma
- residual convolutional neural network
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging