南方医科大学学报

南方医科大学学报 ›› 2023, Vol. 43 ›› Issue (6): 1023-1028.doi: 10.12122/j.issn.1673-4254.2023.06.19

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多模态MRI影像组学可预测弥漫性较低级别胶质瘤的1p/19q共缺失状态

卢明君，屈耀铭，马安东，朱建彬，邹 霞，林耕耘，李榆欣，刘昕孜，温志波   

  1. 南方医科大学珠江医院影像诊断科，广东 广州 510282
  • 出版日期:2023-06-20 发布日期:2023-07-06

Prediction of 1p/19q codeletion status in diffuse lower-grade glioma using multimodal MRI radiomics

LU Mingjun, QU Yaoming, MA Andong, ZHU Jianbin, ZOU Xia, LIN Gengyun, LI Yuxin, LIU Xinzi, WEN Zhibo   

  1. Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
  • Online:2023-06-20 Published:2023-07-06

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摘要： 目的 应用多模态MRI影像组学特征无创性预测弥漫性较低级别胶质瘤1p/19q共缺失状态。方法 收集2015年10月~2022年9月经病理证实为弥漫性较低级别胶质瘤的104例患者的MRI数据，并从T2WI、T1WI、FLAIR、对比增强T1WI和DWI序列提取535个组学特征，包括70个形态学特征，90个一阶统计学特征以及375个纹理特征。构建逻辑回归（LR）、基于逻辑回归的最小绝对收缩和选择算子（LRlasso）、支持向量机（SVM）和线性判别分析（LDA）模型，经十折交叉验证后，比较4组模型的预测效能。两位影像医师根据MRI图像来预测弥漫性较低级别胶质瘤1p/19q共缺失状态。采用受试者工作特性曲线（ROC）来评估影像组学模型和影像医师预测效能。结果 LR、LRlasso、SVM、LDA模型在验证集的AUC值分别为0.833、0.819、0.824、0.819，差异不具有统计学意义（P>0.1）。4 组影像组学模型预测效能均高于住院医师（AUC=0.645，P=0.011、0.022、0.016、0.030），并与主治医师相仿（AUC=0.838，P>0.05）。结论 多模态MRI影像组学模型可以非侵入性地预测弥漫性较低级别胶质瘤1p/19q共缺失状态。

关键词: 弥漫性较低级别胶质瘤；1p/19q共缺失；影像组学；MRI

Abstract: Objective To develop a noninvasive method for prediction of 1p/19q codeletion in diffuse lower-grade glioma (DLGG) based on multimodal magnetic resonance imaging (MRI) radiomics. Methods We collected MRI data from 104 patients with pathologically confirmed DLGG between October, 2015 and September, 2022. A total of 535 radiomics features were extracted from T2WI, T1WI, FLAIR, CE-T1WI and DWI, including 70 morphological features, 90 first order features, and 375 texture features. We constructed logistic regression (LR), logistic regression least absolute shrinkage and selection operator (LRlasso), support vector machine (SVM) and Linear Discriminant Analysis (LDA) radiomics models and compared their predictive performance after 10- fold cross validation. The MRI images were reviewed by two radiologists independently for predicting the 1p/19q status. Receiver operating characteristic curves were used to evaluate classification performance of the radiomics models and the radiologists. Results The 4 radiomics models (LR, LRlasso, SVM and LDA) achieved similar area under the curve (AUC) in the validation dataset (0.833, 0.819, 0.824 and 0.819, respectively; P>0.1), and their predictive performance was all superior to that of resident physicians of radiology (AUC=0.645, P=0.011, 0.022, 0.016, 0.030, respectively) and similar to that of attending physicians of radiology (AUC=0.838, P>0.05). Conclusion Multiparametric MRI radiomics models show good performance for noninvasive prediction of 1p/19q codeletion status in patients with in diffuse lower-grade glioma.

Key words: diffuse lower-grade glioma; 1p/19q codeletion; radiomics; magnetic resonance imaging