Editorial for “Deep Learning Assisted Diagnosis of Musculoskeletal Tumors Based Upon Contrast‐Enhanced Magnetic Resonance Imaging”

Editorial for “Deep Learning Assisted Diagnosis of Musculoskeletal Tumors Based Upon Contrast-Enhanced Magnetic Resonance Imaging” Although radiographs are the first choice for the initial characterization of bone lesions, MRI is the primary imaging modality for further evaluation of suspicious musculoskeletal lesions. However, the spectrum of both malignant and benign musculoskeletal tumors is extremely broad, individual entities may be exceptionally rare, and the overall frequency of such tumors is rather low. Thus, reading musculoskeletal tumor MR images may pose challenges even for specialized radiologists, and several years of intensive training are required to correctly interpret MRI examinations and potentially malignant findings. Beyond the reporting by radiologists, specialists from various fields such as oncologists, radiation therapists, pathologists, or orthopedic surgeons are also involved in patient management and treatment. As a consequence of this multidisciplinarity, numerous physicians not primarily specialized in MRI are confronted with the interpretation of complex MR images. Machine learning approaches have been a source of hope for several years, as they could help more distant disciplines improving their ability to assess and read radiological examinations. These techniques have made significant progress in recent years and have attracted much attention in medicine, since their application has the potential to increase interobserver agreement in reporting, decrease the rate of misdiagnosis, and support lesion classification with high accuracy. Over the past decade, artificial neural networks have increasingly outperformed other established models in several key benchmarks. In medical imaging, interest in deep learning—a subcategory of machine learning—was fueled primarily by convolutional neural networks (CNNs), a powerful method for analyzing medical images. In this issue of JMRI, an article by Zhao et al aimed to improve MRI diagnosis of musculoskeletal tumors using a deep learning algorithm based on MRNet, an adaptation of the AlexNet CNN. For this purpose, the authors retrospectively included 304 patients with potentially suspicious musculoskeletal lesions that were examined by contrast-enhanced MRI. The results of histological examinations from either surgeries or biopsies served as a standard of reference. The study then compared the diagnostic performance of the algorithm with that of seven experts in musculoskeletal tumor disease. These were four orthopedic surgeons, two oncologists, and one radiologist. The authors’ results suggest that the use of the presented deep learning algorithm mainly increases the sensitivity for the diagnosis of malignant lesions but has no significant impact on the specificity. Thus, from a clinical perspective, the use of this algorithm could help to rule out malignancy and avoid unnecessary biopsies by increasing the rate of true positive findings. A comparison between the involved experts’ disciplines revealed that the algorithm was able to increase the performance of the oncologists and orthopedists to a greater extent than that of the radiologist. Interestingly, a recent study by Eweje et al found that deep learning can be used to distinguish benign and malignant bone lesions on par with expert radiologists. In an earlier and extensively debated article on the use of an artificial intelligence system for breast cancer screening, the system was reported to outperform radiologists. However, despite all the successes of machine learning, sheer outperforming human radiologists should not become the primary goal of applying such algorithms, as this would merely emerge into a “man versus machine” competition in terms of image interpretation. Rather, the paramount ambition should be similar to that of the present manuscript by Zhao et al: To improve the diagnostic accuracy of physicians in a “man and machine” sense, which might be particularly helpful in parts of the world where radiologists are not abundantly available. In this way, the machine learning algorithm presented here could be beneficial especially for physicians of medical fields, whose main activity does not involve the assessment of radiological images by providing support for MR image interpretation of musculoskeletal tumors. In conclusion, algorithms that use deep learning to analyze radiological images can help to make radiological exams more interpretable to physicians with less radiological expertise, reduce needless referrals from smaller hospitals to