Can Machine Learning Models Based on Radiomic and Clinical Information Improve Radiologists' Diagnostic Performance for Bone Tumors? An MRMC Study

RATIONALE AND OBJECTIVES: To explore whether machine learning models of bone tumors can improve the diagnostic performance of imaging physicians. MATERIALS AND METHODS: Retrospective radiographic and clinical data collection from bone tumor patients to construct multiple machine learning models. Area under the curve (AUC) values were used as the primary assessment metric to select auxiliary models for this study. Seven readers were selected based on pre-experiment results from the Multireader multicase (MRMC) study. Two reading experiments were conducted using an independent test set to validate the value of interpretable models as clinician aids. We used the Obuchowski-Rockette method to compare differences in physician categorization. RESULTS: The extreme gradient boosting (XGBoost) model based on clinical information and radiomics features performed best for classification with an AUC value of 0.905 (95% CI: 0.841, 0.949). The interpretable algorithm suggested that gray level co-occurrence matrix (GLCM) features provided the most crucial predictive information for the classification model. The AUC was significantly higher for senior physicians (with 7-11 years of experience) than for junior physicians (with 2-5 years of experience) in reading musculoskeletal radiographs (0.929-0.956 vs. 0.812-0.906). The mean AUC value of the independent reading by the seven physicians was 0.904, and the mean AUC value of the model-assisted reading result was improved by 0.037 (95% CI: -0.074, -0.001%), which was statistically significant (P=0.047). CONCLUSION: The machine learning model based on the radiomics features and clinical information of knee X-ray images can effectively assist clinicians in completing the preoperative diagnosis of benign and malignant bone tumors.