Computational analysis of MRIs predicts osteosarcoma chemoresponsiveness

This study aimed to improve osteosarcoma chemoresponsiveness prediction by optimization of computational analysis of MRIs. Our retrospective predictive model involved osteosarcoma patients with MRI scans performed before OsteoSa MAP neoadjuvant cytotoxic chemotherapy. 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 Λ′(G) provided the best predictive association (area under the ROC curve = 0.88; p