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

Autor: Zhao Y; Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China., Chang M; Stanford School of Medicine, Palo Alto, California, USA., Wang R; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA., Xi IL; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA., Chang K; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA., Huang RY; Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA., Vallières M; Medical Physics Unit, McGill University, Montreal, Québec, Canada., Habibollahi P; Department of Radiology, Division of Interventional Radiology, UT Southwestern Medical School, Dallas, Texas, USA., Dagli MS; Department of Radiology, Division of Interventional Radiology, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA., Palmer M; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA., Zhang PJ; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA., Silva AC; Department of Radiology, Mayo Clinical Hospital, Scottsdale, Arizona, USA., Yang L; Department of Neurology, The Second Xiangya Hospital, Central South University, Changsha, China., Soulen MC; Department of Radiology, Division of Interventional Radiology, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA., Zhang Z; Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China., Bai HX; Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA., Stavropoulos SW; Department of Radiology, Division of Interventional Radiology, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Jazyk: angličtina
Zdroj: Journal of magnetic resonance imaging : JMRI [J Magn Reson Imaging] 2020 Nov; Vol. 52 (5), pp. 1542-1549. Date of Electronic Publication: 2020 Mar 28.
DOI: 10.1002/jmri.27153
Abstrakt: 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/T 2 -weighted and T 1 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.
(© 2020 International Society for Magnetic Resonance in Medicine.)
Databáze: MEDLINE
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