Deep Learning Based on MRI for Differentiation of Low- and High-Grade in Low-Stage Renal Cell Carcinoma

Yijun Zhao, Marcello Chang, Robin Wang, Ianto Lin Xi, Ken Chang, Raymond Y. Huang, Martin Vallières, Peiman Habibollahi, Mandeep S. Dagli, Matthew Palmer, Paul J. Zhang, Alvin C. Silva, Li Yang, Michael C. Soulen, Zishu Zhang, Harrison X. Bai, S. William Stavropoulos

Research output: Contribution to journalArticle

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: 2.

Original languageEnglish (US)
JournalJournal of Magnetic Resonance Imaging
DOIs
StateAccepted/In press - Jan 1 2020

Keywords

  • deep learning
  • histological grade
  • MRI
  • renal cell carcinoma
  • residual convolutional neural network

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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    Zhao, Y., Chang, M., Wang, R., Xi, I. L., Chang, K., Huang, R. Y., Vallières, M., Habibollahi, P., Dagli, M. S., Palmer, M., Zhang, P. J., Silva, A. C., Yang, L., Soulen, M. C., Zhang, Z., Bai, H. X., & Stavropoulos, S. W. (Accepted/In press). Deep Learning Based on MRI for Differentiation of Low- and High-Grade in Low-Stage Renal Cell Carcinoma. Journal of Magnetic Resonance Imaging. https://doi.org/10.1002/jmri.27153