Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score. Issue 2 (21st August 2018)

• Record Type: Journal Article
• Authors: Ha, Richard; Chang, Peter; Mutasa, Simukayi; Karcich, Jenika; Goodman, Sarah; Blum, Elyse; Kalinsky, Kevin; Liu, Michael Z.; Jambawalikar, Sachin
• Is Part Of: Journal of magnetic resonance imaging. Volume 49:Issue 2(2019)
• Page Start: 518
• Page End: 524
• Publication Date: 2018-08-21
• Subjects: Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.26244
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: Oncotype Dx is a validated genetic analysis that provides a recurrence score (RS) to quantitatively predict outcomes in patients who meet the criteria of estrogen receptor positive / human epidermal growth factor receptor‐2 negative (ER+/HER2–)/node negative invasive breast carcinoma. Although effective, the test is invasive and expensive, which has motivated this investigation to determine the potential role of radiomics. Hypothesis: We hypothesized that convolutional neural network (CNN) can be used to predict Oncotype Dx RS using an MRI dataset. Study Type: Institutional Review Board (IRB)‐approved retrospective study from January 2010 to June 2016. Population: In all, 134 patients with ER+/HER2– invasive ductal carcinoma who underwent both breast MRI and Oncotype Dx RS evaluation. Patients were classified into three groups: low risk (group 1, RS <18), intermediate risk (group 2, RS 18–30), and high risk (group 3, RS >30). Field Strength/Sequence: 1.5T and 3.0T. Breast MRI, T1 postcontrast. Assessment: Each breast tumor underwent 3D segmentation. In all, 1649 volumetric slices in 134 tumors (mean 12.3 slices/tumor) were evaluated. A CNN consisted of four convolutional layers and max‐pooling layers. Dropout at 50% was applied to the second to last fully connected layer to prevent overfitting. Three‐class prediction (group 1 vs. group 2 vs. group 3) and two‐class prediction (group 1 vs. group 2/3) models were performed. Statistical Tests: A 5‐fold crossvalidation test was performed using 80% training and 20% testing. Diagnostic accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) area under the curve (AUC) were evaluated. Results: The CNN achieved an overall accuracy of 81% (95% confidence interval [CI] ± 4%) in three‐class prediction with specificity 90% (95% CI ± 5%), sensitivity 60% (95% CI ± 6%), and the area under the ROC curve was 0.92 (SD, 0.01). The CNN achieved an overall accuracy of 84% (95% CI ± 5%) in two‐class prediction with specificity 81% (95% CI ± 4%), sensitivity 87% (95% CI ± 5%), and the area under the ROC curve was 0.92 (SD, 0.01). Data Conclusion: It is feasible for current deep CNN architecture to be trained to predict Oncotype DX RS. Level of Evidence: 4 Technical Efficacy : Stage 2 J. Magn. Reson. Imaging 2019;49:518–524.