Deep Learning for Classification of Bone Lesions on Routine MRI

• Main Authors: Feyisope R. Eweje, SB, Bingting Bao, MS, Jing Wu, MD, Deepa Dalal, MBBS, Wei-hua Liao, MD, Yu He, MD, Yongheng Luo, MD, Shaolei Lu, MD, PhD, Paul Zhang, MD, Xianjing Peng, MD, Ronnie Sebro, MD, PhD, Harrison X. Bai, MD, Lisa States, MD
• Format: Article
• Language: English
• Published: Elsevier 2021-06-01
• Series: EBioMedicine
• Subjects: Deep learning; MRI; Bone tumor; Convolutional neural network; Bone lesion
• Online Access: http://www.sciencedirect.com/science/article/pii/S235239642100195X

Background: Radiologists have difficulty distinguishing benign from malignant bone lesions because these lesions may have similar imaging appearances. The purpose of this study was to develop a deep learning algorithm that can differentiate benign and malignant bone lesions using routine magnetic resonance imaging (MRI) and patient demographics. Methods: 1,060 histologically confirmed bone lesions with T1- and T2-weighted pre-operative MRI were retrospectively identified and included, with lesions from 4 institutions used for model development and internal validation, and data from a fifth institution used for external validation. Image-based models were generated using the EfficientNet-B0 architecture and a logistic regression model was trained using patient age, sex, and lesion location. A voting ensemble was created as the final model. The performance of the model was compared to classification performance by radiology experts. Findings: The cohort had a mean age of 30±23 years and was 58.3% male, with 582 benign lesions and 478 malignant. Compared to a contrived expert committee result, the ensemble deep learning model achieved (ensemble vs. experts): similar accuracy (0·76 vs. 0·73, p=0·7), sensitivity (0·79 vs. 0·81, p=1·0) and specificity (0·75 vs. 0·66, p=0·48), with a ROC AUC of 0·82. On external testing, the model achieved ROC AUC of 0·79. Interpretation: Deep learning can be used to distinguish benign and malignant bone lesions on par with experts. These findings could aid in the development of computer-aided diagnostic tools to reduce unnecessary referrals to specialized centers from community clinics and limit unnecessary biopsies. Funding: This work was funded by a Radiological Society of North America Research Medical Student Grant (#RMS2013) and supported by the Amazon Web Services Diagnostic Development Initiative.