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Computational analysis of MRIs predicts osteosarcoma chemoresponsiveness
Goran J Djuričić1, Nemanja Rajković2, Nebojša Milošević2
1Department of Radiology, University Children's Hospital, School of Medicine, University of Belgrade, Belgrade, 11000, Serbia.
Biomarkers in Medicine
|July 8, 2021
Summary
Computational analysis of pretreatment MRIs can predict osteosarcoma chemoresponsiveness. Morphological complexity and fractality identified using monofractal and multifractal algorithms offer promising imaging biomarkers for treatment classification.
Area of Science:
- Oncology
- Radiology
- Medical Imaging Analysis
Background:
- Osteosarcoma treatment response prediction is crucial for patient outcomes.
- Accurate prediction of chemoresponsiveness guides neoadjuvant chemotherapy selection.
- Current methods for predicting treatment response have limitations.
Purpose of the Study:
- To enhance the prediction of osteosarcoma chemoresponsiveness.
- To optimize computational analysis of pre-treatment Magnetic Resonance Imaging (MRI) for predictive insights.
- To identify novel imaging biomarkers from MRI data.
Main Methods:
- Retrospective analysis of osteosarcoma patient MRI scans.
- Application of monofractal and multifractal algorithms for image analysis.
- Development of a predictive model using computational MRI features.
Main Results:
- Monofractal and multifractal algorithms successfully classified tumors based on chemoresponsiveness.
- Predictive features included morphological complexity, homogeneity, and fractality.
- The monofractal feature CV for Λ'(G) showed the highest predictive accuracy (AUC = 0.88, p < 0.001).
Conclusions:
- Computational analysis of pre-treatment MRIs can serve as a basis for imaging biomarkers.
- This study is the first to demonstrate the potential of MRI-based computational analysis for classifying osteosarcoma chemoresponsiveness.
- These findings may lead to improved personalized treatment strategies for osteosarcoma.
