Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score

• Published in: Journal of magnetic resonance imaging Vol. 49; no. 2; pp. 518 - 524
• Main Authors: Ha, Richard, Chang, Peter, Mutasa, Simukayi, Karcich, Jenika, Goodman, Sarah, Blum, Elyse, Kalinsky, Kevin, Liu, Michael Z., Jambawalikar, Sachin
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.02.2019
• Subjects: Accuracy; Adult; Aged; Algorithms; Area Under Curve; Artificial neural networks; Breast; Breast cancer; Breast carcinoma; Breast Neoplasms - diagnostic imaging; Carcinoma, Ductal, Breast - diagnostic imaging; Confidence intervals; Diagnostic systems; Epidermal growth factor; ErbB-2 protein; Estrogen Receptor alpha - metabolism; Estrogen receptors; Estrogens; Female; Field strength; Genetic analysis; Growth factors; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Imaging, Three-Dimensional; Invasiveness; Magnetic Resonance Imaging; Mathematical models; Middle Aged; Neoplasm Recurrence, Local; Neural networks; Neural Networks, Computer; Patients; Population (statistical); Radiomics; Receptor, ErbB-2 - metabolism; Reproducibility of Results; Retrospective Studies; Risk; ROC Curve; Sensitivity; Statistical analysis; Statistical tests; Treatment Outcome; Tumors
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.26244

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.