Machine-learning approach to predict molecular subgroups of medulloblastoma using multiparametric MRI-based tumor radiomics

人工智能 接收机工作特性 支持向量机 无线电技术 特征选择 Lasso(编程语言) 模式识别(心理学) 交叉验证 髓母细胞瘤 医学 随机森林 计算机科学 特征(语言学) 核医学 机器学习 病理 语言学 哲学 万维网
作者
Ann Christy Saju,Abhishek Chatterjee,Arpita Sahu,Tejpal Gupta,Rahul Krishnatry,Smruti Mokal,Ayushi Sahay,Sridhar Epari,Maya Prasad,Girish Chinnaswamy,Jai Prakash Agarwal,Jayant Sastri Goda
出处
期刊:British Journal of Radiology [Wiley]
卷期号:95 (1134) 被引量:8
标识
DOI:10.1259/bjr.20211359
摘要

Image-based prediction of molecular subgroups of Medulloblastoma (MB) has the potential to optimize and personalize therapy. The objective of the study is to distinguish between broad molecular subgroups of MB using MR-Texture analysis.Thirty-eight MB patients treated between 2007 and 2020 were retrospectively analyzed. Texture analysis was performed on contrast enhanced T1(T1C) and T2 weighted (T2W) MR images. Manual segmentation was performed on all slices and radiomic features were extracted which included first order, second order (GLCM - Grey level co-occurrence matrix) and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression and thereafter Support Vector Machine (SVM) and a 10-fold cross-validation strategy was used for model development. The area under Receiver Operator Characteristic (ROC) curve was used to evaluate the model.A total of 174 and 170 images were obtained for analysis from the Axial T1C and T2W image datasets. One hundred and sixty-four MR based texture features were extracted. The best model was arrived at by using a combination of 30 GLCM and six shape features on T1C MR sequence. A 10-fold cross-validation demonstrated an AUC of 0.93, 0.9, 0.93, and 0.93 in predicting WNT, SHH, Group 3, and Group 4 MB subgroups, respectively.Radiomic analysis of MR images in MB can predict molecular subgroups with acceptable degree of accuracy. The strategy needs further validation in an external dataset for its potential use in ab initio management paradigms of MBs.Medulloblastoma can be classified into four distinct molecular subgroups using radiomic feature classifier from non-invasive Multiparametric Magnetic resonance imaging. This can have future ramifications in the extent of surgical resection of Medulloblastoma which can ultimately result in reduction of morbidity.
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