Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong, Hong Kong.
Department of Obstetrics & Gynaecology, Li Ka Shing Faculty of Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong, Hong Kong.
Eur Radiol. 2020 Oct;30(10):5384-5391. doi: 10.1007/s00330-020-06913-7. Epub 2020 May 7.
To evaluate MRI texture analysis in differentiating clinicopathological characteristics of cervical carcinoma (CC).
Patients with newly diagnosed CC who underwent pre-treatment MRI were retrospectively reviewed. Texture analysis was performed using commercial software (TexRAD). Largest single-slice ROIs were manually drawn around the tumour on T2-weighted (T2W) images, apparent diffusion coefficient (ADC) maps and contrast-enhanced T1-weighted (T1c) images. First-order texture features were calculated and compared among histological subtypes, tumour grades, FIGO stages and nodal status using the Mann-Whitney U test. Feature selection was achieved by elastic net. Selected features from different sequences were used to build the multivariable support vector machine (SVM) models and the performances were assessed by ROC curves and AUC.
Ninety-five patients with FIGO stage IB~IVB were evaluated. A number of texture features from multiple sequences were significantly different among all the clinicopathological subgroups (p < 0.05). Texture features from different sequences were selected to build the SVM models. The AUCs of SVM models for discriminating histological subtypes, tumour grades, FIGO stages and nodal status were 0.841, 0.850, 0.898 and 0.879, respectively.
Texture features derived from multiple sequences were helpful in differentiating the clinicopathological signatures of CC. The SVM models with selected features from different sequences offered excellent diagnostic discrimination of the tumour characteristics in CC.
• First-order texture features are able to differentiate clinicopathological signatures of cervical carcinoma. • Combined texture features from different sequences can offer excellent diagnostic discrimination of the tumour characteristics in cervical carcinoma.
评估 MRI 纹理分析在鉴别宫颈癌（CC）临床病理特征中的作用。
回顾性分析了经术前 MRI 检查的新诊断为 CC 的患者。使用商业软件（TexRAD）对纹理分析进行分析。在 T2 加权（T2W）图像、表观扩散系数（ADC）图和对比增强 T1 加权（T1c）图像上手动绘制肿瘤的最大单层面 ROI。使用 Mann-Whitney U 检验比较不同组织学亚型、肿瘤分级、FIGO 分期和淋巴结状态之间的一阶纹理特征。通过弹性网进行特征选择。来自不同序列的选定特征用于构建多变量支持向量机（SVM）模型，并通过 ROC 曲线和 AUC 评估性能。
共评估了 95 例 FIGO 分期为 IB-IVB 的患者。在所有临床病理亚组中，来自多个序列的许多纹理特征在统计学上存在显著差异（p<0.05）。来自不同序列的纹理特征被选择来构建 SVM 模型。用于鉴别组织学亚型、肿瘤分级、FIGO 分期和淋巴结状态的 SVM 模型的 AUC 分别为 0.841、0.850、0.898 和 0.879。
来自多个序列的纹理特征有助于鉴别 CC 的临床病理特征。来自不同序列的选定特征的 SVM 模型可对 CC 中肿瘤特征进行出色的诊断鉴别。
一阶纹理特征可区分宫颈癌的临床病理特征。
来自不同序列的组合纹理特征可对宫颈癌的肿瘤特征进行出色的诊断鉴别。
