DataSheet_1_An automated approach for predicting glioma grade and survival of LGG patients using CNN and radiomics.pdf

ObjectivesTo develop and validate an efficient and automatically computational approach for stratifying glioma grades and predicting survival of lower-grade glioma (LGG) patients using an integration of state-of-the-art convolutional neural network (CNN) and radiomics.
MethodThis retrospective study reviewed 470 preoperative MR images of glioma from BraTs public dataset (n=269) and Jinling hospital (n=201). A fully automated pipeline incorporating tumor segmentation and grading was developed, which can avoid variability and subjectivity of manual segmentations. First, an integrated approach by fusing CNN features and radiomics features was employed to stratify glioma grades. Then, a deep-radiomics signature based on the integrated approach for predicting survival of LGG patients was developed and subsequently validated in an independent cohort.
ResultsThe performance of tumor segmentation achieved a Dice coefficient of 0.81. The intraclass correlation coefficients (ICCs) of the radiomics features between the segmentation network and physicians were all over 0.75. The performance of glioma grading based on integrated approach achieved the area under the curve (AUC) of 0.958, showing the effectiveness of the integrated approach. The multivariable Cox regression results demonstrated that the deep-radiomics signature remained an independent prognostic factor and the integrated nomogram showed significantly better performance than the clinical nomogram in predicting overall survival of LGG patients (C-index: 0.865 vs. 0.796, P=0.005).
ConclusionThe proposed integrated approach can be noninvasively and efficiently applied in prediction of gliomas grade and survival. Moreover, our fully automated pipeline successfully achieved computerized segmentation instead of manual segmentation, which shows the potential to be a reproducible approach in clinical practice.

研究目的 本研究旨在结合当前主流的卷积神经网络（convolutional neural network, CNN）与放射组学（radiomics）技术，开发并验证一种高效的自动化计算方法，用于胶质瘤分级以及预测低级别胶质瘤（lower-grade glioma, LGG）患者的生存情况。研究方法 本回顾性研究共纳入470例胶质瘤患者的术前磁共振图像，其中来自BraTs公共数据集（BraTs public dataset）的样本量为269例，来自金陵医院的样本量为201例。本研究开发了一套集成肿瘤分割与分级的全自动分析流程，可规避手动分割带来的变异性与主观性。首先，采用融合卷积神经网络特征与放射组学特征的整合方法完成胶质瘤分级；随后，基于该整合方法构建用于预测低级别胶质瘤患者生存情况的深度放射组学特征模型，并在独立队列中进行验证。研究结果 肿瘤分割任务的Dice系数（Dice coefficient）达到0.81。分割网络与医师标注的放射组学特征的组内相关系数（intraclass correlation coefficients, ICCs）均大于0.75。基于整合方法的胶质瘤分级模型的受试者工作特征曲线下面积（area under the curve, AUC）为0.958，证实了该整合方法的有效性。多变量Cox回归分析结果显示，深度放射组学特征模型是独立的预后因素；在预测低级别胶质瘤患者总生存期方面，整合列线图的性能显著优于临床列线图（C指数：0.865 vs 0.796，P=0.005）。研究结论 本研究提出的整合方法可无创且高效地应用于胶质瘤分级与生存预测。此外，本研究开发的全自动分析流程成功实现了肿瘤的计算机化分割，替代了传统手动分割，具备在临床实践中推广应用的潜力。