| Autor: |
Batur, Halitcan, Mendi, Bokebatur Ahmet Rasit, Cay, Nurdan |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Polish Journal of Radiology. 88:194-202 |
| ISSN: |
1899-0967 |
| Popis: |
Purpose: Contrary to the self-limiting nature of reversible bone marrow lesions, irreversible bone marrow lesions require early surgical intervention to prevent further morbidity. Thus, early discrimination of irreversible pathology is necessitated. The purpose of this study is to evaluate the efficacy of radiomics and machine learning regarding this topic. Material and methods: A database was scanned for patients who had undergone MRI of the hip for differential diagnosis of bone marrow lesions and had had follow-up images acquired within 8 weeks after the first imaging. Images that showed resolution of oedema were included in the reversible group. The remainders that showed progression into characteristic signs of osteonecrosis were included in the irreversible group. Radiomics was performed on the first MR images, calculating first- and second-order parameters. Support vector machine and random forest classifiers were performed using these parameters. Results: Thirty-seven patients (seventeen osteonecrosis) were included. A total of 185 ROIs were segmented. Fortyseven parameters were accepted as classifiers with an area under the curve value ranging from 0.586 to 0.718. Support vector machine yielded a sensitivity of 91.3% and a specificity of 85.1%. Random forest classifier yielded a sensitivity of 84.8% and a specificity of 76.7%. Area under curves were 0.921 for support vector machine and 0.892 for random forest classifier. Conclusions: Radiomics analysis could prove useful for discrimination of reversible and irreversible bone marrow lesions before the irreversible changes occur, which could prevent morbidities of osteonecrosis by guiding the decisionmaking process for management. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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