دورية أكاديمية
Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors
| العنوان: | Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors |
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| المساهمون: | Yae Won Park, Yoon Seong Choi, Sung Soo Ahn, Jong Hee Chang, Se Hoon Kim, Seung-Koo Lee, Kim, Se Hoon |
| بيانات النشر: | Korean Society of Radiology |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Grade, Lower-grade glioma, Magnetic resonance imaging, Radiomics, The Cancer Genome Atlas |
| الوصف: | OBJECTIVE: To assess whether radiomics features derived from multiparametric MRI can predict the tumor grade of lower-grade gliomas (LGGs; World Health Organization grade II and grade III) and the nonenhancing LGG subgroup. MATERIALS AND METHODS: Two-hundred four patients with LGGs from our institutional cohort were allocated to training (n = 136) and test (n = 68) sets. Postcontrast T1-weighted images, T2-weighted images, and fluid-attenuated inversion recovery images were analyzed to extract 250 radiomics features. Various machine learning classifiers were trained using the radiomics features to predict the glioma grade. The trained classifiers were internally validated on the institutional test set and externally validated on a separate cohort (n = 99) from The Cancer Genome Atlas (TCGA). Classifier performance was assessed by determining the area under the curve (AUC) from receiver operating characteristic curve analysis. An identical process was performed in the nonenhancing LGG subgroup (institutional training set, n = 73; institutional test set, n = 37; and TCGA cohort, n = 37) to predict the glioma grade. RESULTS: The performance of the best classifier was good in the internal validation set (AUC, 0.85) and fair in the external validation set (AUC, 0.72) to predict the LGG grade. For the nonenhancing LGG subgroup, the performance of the best classifier was good in the internal validation set (AUC, 0.82), but poor in the external validation set (AUC, 0.68). CONCLUSION: Radiomics feature-based classifiers may be useful to predict LGG grades. However, radiomics classifiers may have a limited value when applied to the nonenhancing LGG subgroup in a TCGA cohort. ; open |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1229-6929 2005-8330 |
| العلاقة: | Korean Journal of Radiology; J02884; OAK-2019-04420; https://ir.ymlib.yonsei.ac.kr/handle/22282913/171416Test; T201903489; Korean Journal of Radiology, Vol.20(9) : 1381-1389, 2019; 63825 |
| DOI: | 10.3348/kjr.2018.0814 |
| الإتاحة: | https://doi.org/10.3348/kjr.2018.0814Test https://ir.ymlib.yonsei.ac.kr/handle/22282913/171416Test |
| حقوق: | CC BY-NC-ND 2.0 KR |
| رقم الانضمام: | edsbas.578AAF5 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 12296929 20058330 |
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| DOI: | 10.3348/kjr.2018.0814 |