American Association for Cancer Research
10780432ccr173445-sup-192625_2_supp_4745298_p8gqsm.pdf (933.83 kB)

Supplemental Tables from Machine Learning–Based Radiomics for Molecular Subtyping of Gliomas

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posted on 2023-03-31, 20:41 authored by Chia-Feng Lu, Fei-Ting Hsu, Kevin Li-Chun Hsieh, Yu-Chieh Jill Kao, Sho-Jen Cheng, Justin Bo-Kai Hsu, Ping-Huei Tsai, Ray-Jade Chen, Chao-Ching Huang, Yun Yen, Cheng-Yu Chen

Table S1 Full list of included TCIA glioma subjects for the training of the machine-learning models (KPS = Karnofsky Performance Scale) Table S2 MRI data integrity of the training dataset Table S3 Full list of included subjects as an independent validation dataset Table S4 The formulae for the calculation of primary radiomic features.


Ministry of Science and Technology, Taiwan

Taipei Medical University

National Health Research Institutes



Purpose: The new classification announced by the World Health Organization in 2016 recognized five molecular subtypes of diffuse gliomas based on isocitrate dehydrogenase (IDH) and 1p/19q genotypes in addition to histologic phenotypes. We aim to determine whether clinical MRI can stratify these molecular subtypes to benefit the diagnosis and monitoring of gliomas.Experimental Design: The data from 456 subjects with gliomas were obtained from The Cancer Imaging Archive. Overall, 214 subjects, including 106 cases of glioblastomas and 108 cases of lower grade gliomas with preoperative MRI, survival data, histology, IDH, and 1p/19q status were included. We proposed a three-level machine-learning model based on multimodal MR radiomics to classify glioma subtypes. An independent dataset with 70 glioma subjects was further collected to verify the model performance.Results: The IDH and 1p/19q status of gliomas can be classified by radiomics and machine-learning approaches, with areas under ROC curves between 0.922 and 0.975 and accuracies between 87.7% and 96.1% estimated on the training dataset. The test on the validation dataset showed a comparable model performance with that on the training dataset, suggesting the efficacy of the trained classifiers. The classification of 5 molecular subtypes solely based on the MR phenotypes achieved an 81.8% accuracy, and a higher accuracy of 89.2% could be achieved if the histology diagnosis is available.Conclusions: The MR radiomics-based method provides a reliable alternative to determine the histology and molecular subtypes of gliomas. Clin Cancer Res; 24(18); 4429–36. ©2018 AACR.

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