Computational analysis of MRIs predicts osteosarcoma chemoresponsiveness.
| Autor: | Djuričić GJ; Department of Radiology, University Children's Hospital, School of Medicine, University of Belgrade, Belgrade, 11000, Serbia., Rajković N; Department of Biophysics, School of Medicine, University of Belgrade, Belgrade, 11000, Serbia., Milošević N; Department of Biophysics, School of Medicine, University of Belgrade, Belgrade, 11000, Serbia., Sopta JP; Institute of Pathology, School of Medicine, University of Belgrade, Belgrade, 11000, Serbia., Borić I; St. Catherine Specialty Hospital, Zagreb, 10000, Croatia., Dučić S; Department of Radiology, University Children's Hospital, School of Medicine, University of Belgrade, Belgrade, 11000, Serbia., Apostolović M; Department of Orthopaedic, Institute for Orthopaedic Surgery, 'Banjica', Belgrade, 11040, Serbia., Radulovic M; Department of Experimental Oncology, Institute for Oncology & Radiology of Serbia, Belgrade, 11000, Serbia. |
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| Jazyk: | angličtina |
| Zdroj: | Biomarkers in medicine [Biomark Med] 2021 Aug; Vol. 15 (12), pp. 929-940. Date of Electronic Publication: 2021 Jul 08. |
| DOI: | 10.2217/bmm-2020-0876 |
| Abstrakt: | 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 Λ'(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, r² for FD |
| Databáze: | MEDLINE |
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