Yu Liheng, Yu Zekuan, Sun Linlin, Zhu Li, Geng Daoying
Academy for Engineering and Technology, Fudan University, Shanghai, China.
Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Research, Huashan Hospital, Fudan University, Shanghai, China.
Front Med (Lausanne). 2023 Sep 29;10:1232496. doi: 10.3389/fmed.2023.1232496. eCollection 2023.
Gliomas and brain metastases (Mets) are the most common brain malignancies. The treatment strategy and clinical prognosis of patients are different, requiring accurate diagnosis of tumor types. However, the traditional radiomics diagnostic pipeline requires manual annotation and lacks integrated methods for segmentation and classification. To improve the diagnosis process, a gliomas and Mets computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy on multi-center datasets was proposed.
Overall, 1,022 high-grade gliomas and 775 Mets patients' preoperative MR images were adopted in the study, including contrast-enhanced T1-weighted (T1-CE) and T2-fluid attenuated inversion recovery (T2-flair) sequences from three hospitals. Two segmentation models trained on the gliomas and Mets datasets, respectively, were used to automatically segment tumors. Multiple radiomics features were extracted after automatic segmentation. Several machine learning classifiers were used to measure the impact of feature selection methods. A weight soft voting (RSV) model and ensemble decision strategy based on prior knowledge (EDPK) were introduced in the radiomics pipeline. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the classification performance.
The proposed pipeline improved the diagnosis of gliomas and Mets with ACC reaching 0.8950 and AUC reaching 0.9585 after automatic lesion segmentation, which was higher than those of the traditional radiomics pipeline (ACC:0.8850, AUC:0.9450).
The proposed model accurately classified gliomas and Mets patients using MRI radiomics. The novel pipeline showed great potential in diagnosing gliomas and Mets with high generalizability and interpretability.
胶质瘤和脑转移瘤是最常见的脑恶性肿瘤。患者的治疗策略和临床预后不同，需要准确诊断肿瘤类型。然而，传统的放射组学诊断流程需要人工标注，且缺乏分割和分类的集成方法。为了改进诊断过程，提出了一种在多中心数据集上具有自动病变分割和集成决策策略的胶质瘤和脑转移瘤计算机辅助诊断方法。
本研究共采用了1022例高级别胶质瘤和775例脑转移瘤患者的术前磁共振图像，包括来自三家医院的对比增强T1加权（T1-CE）和T2液体衰减反转恢复（T2-flair）序列。分别在胶质瘤和脑转移瘤数据集上训练的两个分割模型用于自动分割肿瘤。自动分割后提取多个放射组学特征。使用几种机器学习分类器来衡量特征选择方法的影响。在放射组学流程中引入了权重软投票（RSV）模型和基于先验知识的集成决策策略（EDPK）。使用准确率、灵敏度、特异性和受试者操作特征曲线下面积（AUC）来评估分类性能。
所提出的流程改进了胶质瘤和脑转移瘤的诊断，自动病变分割后ACC达到0.8950，AUC达到0.9585，高于传统放射组学流程（ACC：0.8850，AUC：0.9450）。
所提出的模型使用MRI放射组学对胶质瘤和脑转移瘤患者进行了准确分类。该新型流程在诊断胶质瘤和脑转移瘤方面具有很大的潜力，具有很高的通用性和可解释性。
