Objectives: Glioma is the most common intracranial primary tumor in central nervous system. Glioma grading possesses important guiding significance for the selection of clinical treatment and follow-up plan, and the assessment of prognosis. This study aims to explore the feasibility of logistic regression model based on radiomics to predict glioma grading.
Methods: Retrospective analysis was performed on 146 glioma patients with confirmed pathological diagnosis from January, 2012 to December, 2018. A total of 41 radiomics features were extracted from contrast-enhanced T1-weighted imaging (T1WI+C) lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) was used to select the most-predictive radiomics features for pathological grading and to calculate radiomics score (Rad-score) of each patient. A logistic regression model was built to explore the correlation between giloma grading and Rad-score. Receiver operating characteristic (ROC) curve was performed to evaluate the model's predictive ability with area under the curve (AUC) for the evaluation index. Hosmer-Lemeshow test was used to measure the model's predictive accuracy.
Results: A total of 5 imaging features selected by LASSO were used to establish a logistic regression model for predicting glioma grading. The model showed good discrimination with AUC value of 0.919. Hosmer-Lemeshow test showed no significant difference between the calibration curve and the ideal curve (P=0.808), indicating high predictive accuracy of the model.
Conclusions: The logistic regression model using radiomics exhibits a relatively high accuracy for predicting glioma grading, which may serve as a complementary tool for preoperative prediction of giloma grading.
目的: 胶质瘤是最常见的颅内原发中枢神经系统肿瘤，胶质瘤的分级对临床治疗及随访方案的选择、预后的评估有重要指导意义。本研究目的在于探讨基于影像组学的logistic回归模型预测胶质瘤病理分级的可行性。方法: 回顾性收集2012年1月至2018年12月经手术病理切片证实为胶质瘤的146例患者。手动分割患者增强T1加权成像(contrast-enhanced T1-weighted imaging，T1WI+C)图像中的胶质瘤区域，形成3D感兴趣区(region of interest，ROI)；提取41个影像特征；采用最小绝对收缩和选择运算(least absolute shrinkage and selection operator，LASSO)二元logistic回归法筛选与胶质瘤病理分级最相关的特征并计算影像组学得分(radiomics score，Rad-score)；采用单因素logistic回归建模方法建立预测模型；用受试者操作特征(receiver operating characteristic，ROC)曲线评估模型的区分能力，评估指标为曲线下面积(area under the curve，AUC)。利用Hosmer-Lemeshow检验衡量模型预测的准确性。结果: 筛选出5个与胶质瘤病理分级最相关的特征，用这5个特征构建的预测胶质瘤病理分级的logistic回归模型的ROC曲线AUC为0.919，具有很好的区分能力，其校准曲线经Hosmer-Lemeshow检验，与理想曲线的差异无统计学意义(P=0.808)，预测准确性高。结论: 基于影像组学的logistic回归模型可以有效地对胶质瘤病理分级进行预测，有望成为术前预测胶质瘤分级的辅助方法。.
Keywords: glioma; grading; least absolute shrinkage and selection operator; logistic regression; radiomics.