Prognostic Value and Quantitative CT Analysis in RANKL Expression of Spinal GCTB in the Denosumab Era: A Machine Learning Approach

SIMPLE SUMMARY: Prognostic assessment of giant cell tumor of bone (GCTB) is an ongoing challenge in the treatment and management of bone tumors. Recurrence rates of spinal GCTB are higher compared to GCTB in other bone sites, presumably due to a more aggressive pathology and/or the conservative surgery performed to spare the spinal cord nerve function and decrease postoperative complications. A more accurate prognosis of GCTB will help to inform the choice of treatment methods. This retrospective study investigated prognosis-related molecular markers in spinal GCTB, including RANKL (target of denosumab), focusing on using machine learning analysis based on pre-operative CT to evaluate RANKL status, which may facilitate the selection of better disease management strategies. ABSTRACT: The receptor activator of the nuclear factor kappa B ligand (RANKL) is the therapeutic target of denosumab. In this study, we evaluated whether radiomics signature and machine learning analysis can predict RANKL status in spinal giant cell tumors of bone (GCTB). This retrospective study consisted of 107 patients, including a training set (n = 82) and a validation set (n = 25). Kaplan-Meier survival analysis was used to validate the prognostic value of RANKL status. Radiomic feature extraction of three heterogeneous regions (VOI(entire), VOI(edge), and VOI(core)) from pretreatment CT were performed. Followed by feature selection using Selected K Best and least absolute shrinkage and selection operator (LASSO) analysis, three classifiers (random forest (RF), support vector machine, and logistic regression) were used to build models. The area under the curve (AUC), accuracy, F1 score, recall, precision, sensitivity, and specificity were used to evaluate the models’ performance. Classification of 75 patients with eligible follow-up based on RANKL status resulted in a significant difference in progression-free survival (p = 0.035). VOI(core)-based RF classifier performs best. Using this model, the AUCs for the training and validation cohorts were 0.880 and 0.766, respectively. In conclusion, a machine learning approach based on CT radiomic features could discriminate prognostically significant RANKL status in spinal GCTB, which may ultimately aid clinical decision-making.