Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China.
Eur Radiol. 2022 Dec;32(12):8659-8669. doi: 10.1007/s00330-022-08911-3. Epub 2022 Jun 24.
To develop and evaluate a radiomics signature based on magnetic resonance imaging (MRI) from multicenter datasets for identification of invisible basal cisterns changes in tuberculous meningitis (TBM) patients.
Our retrospective study enrolled 184 TBM patients and 187 non-TBM controls from 3 Chinese hospitals (training dataset, 158 TBM patients and 159 non-TBM controls; testing dataset, 26 TBM patients and 28 non-TBM controls). nnU-Net was used to segment basal cisterns in fluid-attenuated inversion recovery (FLAIR) images. Subsequently, radiomics features were extracted from segmented basal cisterns in FLAIR and T2-weighted (T2W) images. Feature selection was carried out in three steps. Support vector machine (SVM) and logistic regression (LR) classifiers were applied to construct the radiomics signature to directly identify basal cisterns changes in TBM patients. Finally, the diagnostic performance was evaluated by the receiver operating characteristic (ROC) curve analysis, calibration curve, and decision curve analysis (DCA).
The segmentation model achieved the mean Dice coefficients of 0.920 and 0.727 in the training and testing datasets, respectively. The SVM model with 7 T2WI-based radiomics features achieved best discrimination capability for basal cisterns changes with an AUC of 0.796 (95% CI, 0.744-0.847) in the training dataset, and an AUC of 0.751 (95% CI, 0.617-0.886) with good calibration in the testing dataset. DCA confirmed its clinical usefulness.
The T2WI-based radiomics signature combined with deep learning segmentation could provide a fully automatic, non-invasive tool to identify invisible changes of basal cisterns, which has the potential to assist in the diagnosis of TBM.
• The T2WI-based radiomics signature was useful for identifying invisible basal cistern changes in TBM. • The nnU-Net model achieved acceptable results for the auto-segmentation of basal cisterns. • Combining radiomics and deep learning segmentation provided an automatic, non-invasive approach to assist in the diagnosis of TBM.
开发并评估基于磁共振成像（MRI）的放射组学特征，以识别结核性脑膜炎（TBM）患者不可见基底池变化。
本回顾性研究纳入了来自中国 3 家医院的 184 例 TBM 患者和 187 例非 TBM 对照（训练数据集，158 例 TBM 患者和 159 例非 TBM 对照；测试数据集，26 例 TBM 患者和 28 例非 TBM 对照）。使用 nnU-Net 对液体衰减反转恢复（FLAIR）图像中的基底池进行分割。随后，从 FLAIR 和 T2 加权（T2W）图像中提取分割后的基底池的放射组学特征。特征选择分 3 步进行。支持向量机（SVM）和逻辑回归（LR）分类器用于构建放射组学特征，以直接识别 TBM 患者的基底池变化。最后，通过受试者工作特征（ROC）曲线分析、校准曲线和决策曲线分析（DCA）评估诊断性能。
分割模型在训练和测试数据集上的平均 Dice 系数分别为 0.920 和 0.727。基于 7 个 T2WI 放射组学特征的 SVM 模型在训练数据集上对基底池变化具有最佳鉴别能力，AUC 为 0.796（95%CI，0.744-0.847），在测试数据集上具有良好的校准，AUC 为 0.751（95%CI，0.617-0.886）。DCA 证实了其临床实用性。
基于 T2WI 的放射组学特征与深度学习分割相结合，可提供一种全自动、非侵入性的工具来识别不可见的基底池变化，有望辅助 TBM 的诊断。
基于 T2WI 的放射组学特征有助于识别 TBM 患者不可见的基底池变化。
nnU-Net 模型在基底池自动分割方面取得了较好的效果。
放射组学与深度学习分割相结合，为辅助 TBM 诊断提供了一种自动、非侵入性的方法。
