Chen J M, Wan Q, Zhu H Y, Ge Y Q, Wu L L, Zhai J, Ding Z M
Medical Imaging Central, Yijishan Hospital of Wannan Medical College, Wuhu 241001, China.
GE health care, Shanghai 200000, China.
Zhonghua Yi Xue Za Zhi. 2020 Dec 8;100(45):3626-3631. doi: 10.3760/cma.j.cn112137-20200511-01511.
To investigate the value of conventional magnetic resonance imaging (MRI) based radiomic model in predicting the texture of pituitary macroadenoma. The complete data of 101 patients with pituitary macroadenoma confirmed by surgery and pathology in Yijishan Hospital of Wannan Medical College from December 2014 to December 2019 were retrospectively analyzed. According to the texture of the intraoperative pituitary tumor, patients were divided into soft group (58) and hard group (43). They were randomly divided into training group (72) and validation group (29) at a ratio of 7∶3. All patients underwent conventional MRI scan of the pituitary gland. Itk-snap software was used to manually outline the T(1)-weighted image (T(1)WI), T(2)-weighted image (T(2)WI) and enhanced T(1)WI image section by section on tumor area of interest (ROI) and perform three-dimensional fusion. Then AK software was imported to extract texture features. The regression analysis methods of minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were used for feature selection and radiomic signature establishment. The reliability of the model was verified by 100 leave-group-out cross validation (LGOCV), and the predictive ability of the model was evaluated by drawing the receiver operating characteristic (ROC) curve. The decision curve analysis (DCA) was used to evaluate the clinical application value of the model. The AUC (Area Under the ROC Curve) (95) values of T1WI, T2WI, enhanced T1WI, and the combined sequence model to predict the texture of pituitary macroadenomas in the training and validation groups were 0.91 (0.84-0.98) and 0.90 (0.78-1.00), 0.86 (0.78-0.95) and 0.83 (0.64-1.00), 0.90 (0.83-0.97) and 0.89 (0.77-1.00),0.92 (0.85-0.98) and 0.91 (0.79-1.00), respectively. DCA demonstrated that T(1)WI, T(2)WI, enhanced T(1)WI, and combined sequence model all had good net benefits in clinical practice. T(1)WI, T(2)WI, enhanced T(1)WI, and combined sequence model of conventional MRI all had high efficacy in predicting the texture of pituitary macroadenoma, which provided a new quantitative method for predicting the texture of pituitary macroadenoma.
探讨基于传统磁共振成像(MRI)的放射组学模型在预测垂体大腺瘤质地方面的价值。回顾性分析2014年12月至2019年12月在皖南医学院弋矶山医院经手术及病理证实的101例垂体大腺瘤患者的完整资料。根据术中垂体肿瘤的质地,将患者分为软质组(58例)和硬质组(43例)。按7∶3的比例随机分为训练组(72例)和验证组(29例)。所有患者均接受垂体常规MRI扫描。使用Itk-snap软件在感兴趣的肿瘤区域(ROI)逐节手动勾勒出T1加权图像(T1WI)、T2加权图像(T2WI)和增强T1WI图像,并进行三维融合。然后导入AK软件提取纹理特征。采用最小冗余最大相关性(mRMR)和最小绝对收缩选择算子(LASSO)回归分析方法进行特征选择并建立放射组学特征。通过100次留一法交叉验证(LGOCV)验证模型的可靠性,并通过绘制受试者操作特征(ROC)曲线评估模型的预测能力。采用决策曲线分析(DCA)评估模型的临床应用价值。训练组和验证组中T1WI、T2WI、增强T1WI及联合序列模型预测垂体大腺瘤质地的AUC(ROC曲线下面积)(95)值分别为0.91(0.84 - 0.98)和0.90(0.78 - 1.00)、0.86(0.78 - 0.95)和0.83(0.64 - 1.00)、0.90(0.83 - 0.97)和0.89(0.77 - 1.00)、0.92(0.85 - 0.98)和0.91(0.79 - 1.00)。DCA表明,T1WI、T2WI、增强T1WI及联合序列模型在临床实践中均具有良好的净效益。传统MRI的T1WI、T2WI、增强T1WI及联合序列模型在预测垂体大腺瘤质地方面均具有较高的效能,为预测垂体大腺瘤质地提供了一种新的定量方法。