Deep Learning Based on MRI for Differentiation of Low‐ and High‐Grade in Low‐Stage Renal Cell Carcinoma
- 28 March 2020
- journal article
- research article
- Published by Wiley in Journal of Magnetic Resonance Imaging
- Vol. 52 (5), 1542-1549
- https://doi.org/10.1002/jmri.27153
Abstract
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 2Funding Information
- National Institute of Biomedical Imaging and Bioengineering (5T32EB1680)
This publication has 32 references indexed in Scilit:
- Cancer statistics, 2013CA: A Cancer Journal for Clinicians, 2013
- Treatment Trends for Stage I Renal Cell CarcinomaJournal of Urology, 2011
- Oncologic Results of Laparoscopic Renal Cryoablation for Clinical T1a Tumors: 8 Years of Experience in a Single InstitutionUrology, 2010
- A Critical Analysis of the Actual Role of Minimally Invasive Surgery and Active Surveillance for Kidney CancerEuropean Urology, 2010
- Young Age Is Independent Prognostic Factor for Cancer-Specific Survival of Low-Stage Clear Cell Renal Cell CarcinomaUrology, 2009
- Decreasing Size at Diagnosis of Stage 1 Renal Cell Carcinoma: Analysis From the National Cancer Data Base, 1993 to 2004Journal of Urology, 2008
- TNM staging system for renal-cell carcinoma: current status and future perspectivesThe Lancet Oncology, 2007
- Prognostic significance of morphologic parameters in renal cell carcinomaInternational Journal of Clinical Practice, 2004
- Prognostic value of nuclear grade of renal cell carcinomaCancer, 1995
- Renal cell carcinoma.Prognostic significance of morphologic parameters in 121 casesCancer, 1988