Computational analysis of MRIs predicts osteosarcoma chemoresponsiveness

Aim: This study aimed to improve osteosarcoma chemoresponsiveness prediction by optimization of computational analysis of MRIs. Patients & methods: Our retrospective predictive model involved osteosarcoma patients with MRI scans performed before OsteoSa MAP neoadjuvant cytotoxic chemotherapy. Results: We found that several monofractal and multifractal algorithms were able to classify tumors according to their chemoresponsiveness. The predictive clues were defined as morphological complexity, homogeneity and fractality. The monofractal feature CV for ?'(G) provided the best predictive association (area under the ROC curve = 0.88; p <0.001), followed by Y-axis intersection of the regression line for box fractal dimension, r(2) for FDM and tumor circularity. Conclusion: This is the first full-scale study to indicate that computational analysis of pretreatment MRIs could provide imaging biomarkers for the classification of osteosarcoma according to their chemoresponsiveness. Tweetable abstract Fractal analysis of MRI scans was shown to predict the chemosensitivity of osteosarcoma. These findings may eventually lead to improved patient survival by enabling personalized cytotoxic chemotherapy prescription.