Rad-EfficientNet: Improving Breast MRI Diagnosis Through Integration of Radiomics and Deep Learning.

Abstract

Breast cancer stands as the most prevalent cancer in women globally, with its worldwide escalating incidence and mortality rates underscoring the necessity of improving upon current non-invasive diagnostic methodologies for early-stage detection. This study introduces Rad-EfficientNet, a convolutional neural network (CNN) that incorporates radiomic features in its training pipeline to differentiate benign from malignant breast tumors in multiparametric 3 T breast magnetic resonance imaging (MRI). To this end, a dataset of 104 cases, including 45 benign and 59 malignant instances, was collected, and radiomic features were extracted from the 3D bounding boxes of each of the tumors. The Pearson's correlation coefficient and the Variance Inflation Factor were employed to reduce the radiomic features to a subset of 25. Rad-EfficientNet was then trained on both image and radiomics data. Based on the EfficientNet network family, the proposed Rad-EfficientNet architecture builds upon it by introducing a radiomics fusion layer consisting of a feature reduction operation, radiomic feature concatenation with the learned features, and finally a dropout layer. Rad-EfficientNet achieved an accuracy score of 82%, outperforming conventional classifiers trained solely on radiomic features, as well as hybrid models that combine learned and radiomic features post-training. These results indicate that by incorporating radiomics directly into the CNN training pipeline, complementary features are learned, thereby offering a way to improve current diagnostic deep learning techniques for breast lesion diagnosis.