Radiomics-Based Artificial Intelligence Model to Distinguish Between Spinal Tuberculosis and Spinal Metastasis

This work aimed to develop a computer-aided diagnostic system that utilizes radiomics and artificial intelligence (AI) to differentiate between spinal tuberculosis and spinal metastasis, which are difficult to distinguish due to their similar visual characteristics. A total of 50 cases, comprising 25 rare cases of spinal tuberculosis and 25 cases of spinal metastasis, were included in the study cohort. Expert radiologists and orthopedic specialists manually segmented the regions of interest from the corresponding MRI images. PyRadiomics was used to extract 788 radiomic features from each segmented image. XGBoost was then applied for feature selection and SHAP values confirmed their importance. The subset of features from the previous machine learning stage were used to train a deep neural network, resulting in a radiomics-powered AI classification model. Due to the visual challenges in classifying these two diseases, human doctors achieve an average accuracy of 64%, while our well-trained model demonstrates an impressive accuracy of 93%. Along with its strong performance in external validation, this study suggests that this AI model can offer valuable assistance in achieving early and accurate differential diagnoses of spinal tuberculosis and spinal metastasis.