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

Publication/Presentation Date

2-1-2019

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, T

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.

Volume

49

Issue

2

First Page

518

Last Page

524

ISSN

1522-2586

Disciplines

Diagnosis | Medicine and Health Sciences | Other Analytical, Diagnostic and Therapeutic Techniques and Equipment | Radiology

PubMedID

30129697

Department(s)

Department of Radiology and Diagnostic Medical Imaging

Document Type

Article

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