التفاصيل البيبلوغرافية
| العنوان: |
Magnetic Resonance Imaging Texture Analysis Based on Intraosseous and Extraosseous Lesions to Predict Prognosis in Patients with Osteosarcoma. |
| المؤلفون: |
Mori, Yu1 (AUTHOR) yu.mori.c4@tohoku.ac.jp, Ren, Hainan2 (AUTHOR) merylren1994@163.com, Mori, Naoko2,3 (AUTHOR) naokomori7127@gmail.com, Watanuki, Munenori1 (AUTHOR) mwata@ortho.med.tohoku.ac.jp, Hitachi, Shin2 (AUTHOR) mugi844@gmail.com, Watanabe, Mika4 (AUTHOR) mkawatan@patholo2.med.tohoku.ac.jp, Mugikura, Shunji2,5 (AUTHOR) ktakase@rad.med.tohoku.ac.jp, Takase, Kei2 (AUTHOR) |
| المصدر: |
Diagnostics (2075-4418). Nov2024, Vol. 14 Issue 22, p2562. 16p. |
| مصطلحات موضوعية: |
*TEXTURE analysis (Image processing), *RECEIVER operating characteristic curves, *MAGNETIC resonance imaging, *PRINCIPAL components analysis, *SUPPORT vector machines |
| مستخلص: |
Objectives: To construct an optimal magnetic resonance imaging (MRI) texture model to evaluate histological patterns and predict prognosis in patients with osteosarcoma (OS). Methods: Thirty-four patients underwent pretreatment MRI and were diagnosed as having OS by surgical resection or biopsy between September 2008 and June 2018. Histological patterns and 3-year survival were recorded. Manual segmentation was performed in intraosseous, extraosseous, and entire lesions on T1-weighted, T2-weighted, and contrast-enhanced T1-weighted images to extract texture features and perform principal component analysis. A support vector machine algorithm with 3-fold cross-validation was used to construct and validate the models. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate diagnostic performance in evaluating histological patterns and 3-year survival. Results: Eight patients were chondroblastic and the remaining twenty-six patients were non-chondroblastic patterns. Twenty-seven patients were 3-year survivors, and the remaining seven patients were non-survivors. In discriminating chondroblastic from non-chondroblastic patterns, the model from extraosseous lesions on the T2-weighted images showed the highest diagnostic performance (AUCs of 0.94 and 0.89 in the training and validation sets). The model from intraosseous lesions on the T1-weighted images showed the highest diagnostic performance in discriminating 3-year non-survivors from survivors (AUCs of 0.99 and 0.88 in the training and validation sets) with a sensitivity, specificity, positive predictive value, and negative predictive value of 85.7%, 92.6%, 75.0%, and 96.2%, respectively. Conclusions: The texture models of extraosseous lesions on T2-weighted images can discriminate the chondroblastic pattern from non-chondroblastic patterns, while the texture models of intraosseous lesions on T1-weighted images can discriminate 3-year non-survivors from survivors. [ABSTRACT FROM AUTHOR] |
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