Kong Chao, Yan Ding, Liu Kai, Yin Yong, Ma Changsheng
Department of Graduate, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
BMC Med Imaging. 2025 May 19;25(1):171. doi: 10.1186/s12880-025-01703-3.
Development of a deep learning model for accurate preoperative identification of glioblastoma and solitary brain metastases by combining multi-centre and multi-sequence magnetic resonance images and comparison of the performance of different deep learning models.
Clinical data and MR images of a total of 236 patients with pathologically confirmed glioblastoma and single brain metastases were retrospectively collected from January 2019 to May 2024 at Provincial Hospital of Shandong First Medical University, and the data were randomly divided into a training set and a test set according to the ratio of 8:2, in which the training set contained 197 cases and the test set contained 39 cases; the images were preprocessed and labeled with the tumor regions. The images were pre-processed and labeled with tumor regions, and different MRI sequences were input individually or in combination to train the deep learning model 3D ResNet-18, and the optimal sequence combinations were obtained by five-fold cross-validation enhancement of the data inputs and training of the deep learning models 3D Vision Transformer (3D Vit), 3D DenseNet, and 3D VGG; the working characteristic curves (ROCs) of subjects were plotted, and the area under the curve (AUC) was calculated. The area under the curve (AUC), accuracy, precision, recall and F1 score were used to evaluate the discriminative performance of the models. In addition, 48 patients with glioblastoma and single brain metastases from January 2020 to December 2022 were collected from the Affiliated Cancer Hospital of Shandong First Medical University as an external test set to compare the discriminative performance, robustness and generalization ability of the four deep learning models.
In the comparison of the discriminative effect of different MRI sequences, the three sequence combinations of T1-CE, T2, and T2-Flair gained discriminative effect, with the accuracy and AUC values of 0.8718 and 0.9305, respectively; after the four deep learning models were inputted into the aforementioned sequence combinations, the accuracy and AUC of the external validation of the 3D ResNet-18 model were 0.8125, respectively, 0.8899, all of which are the highest among all models.
A combination of multi-sequence MR images and a deep learning model can efficiently identify glioblastoma and solitary brain metastases preoperatively, and the deep learning model 3D ResNet-18 has the highest efficacy in identifying the two types of tumours.
通过结合多中心和多序列磁共振图像，开发一种深度学习模型，用于术前准确识别胶质母细胞瘤和孤立性脑转移瘤，并比较不同深度学习模型的性能。
回顾性收集2019年1月至2024年5月山东第一医科大学附属省立医院236例经病理证实的胶质母细胞瘤和单发脑转移瘤患者的临床资料和磁共振图像，并按照8:2的比例随机分为训练集和测试集，其中训练集包含197例，测试集包含39例；对图像进行预处理，并标记肿瘤区域。对图像进行预处理并标记肿瘤区域，将不同的磁共振成像序列单独或组合输入，训练深度学习模型3D ResNet-18，并通过对数据输入进行五折交叉验证增强以及训练深度学习模型3D视觉Transformer（3D Vit）、3D DenseNet和3D VGG获得最佳序列组合；绘制受试者工作特征曲线（ROC），并计算曲线下面积（AUC）。使用曲线下面积（AUC）、准确率、精确率、召回率和F1分数来评估模型的判别性能。此外，收集2020年1月至2022年12月山东第一医科大学附属肿瘤医院48例胶质母细胞瘤和单发脑转移瘤患者作为外部测试集，比较四种深度学习模型的判别性能、稳健性和泛化能力。
在不同磁共振成像序列判别效果的比较中，T1-CE、T2和T2-Flair这三种序列组合获得了判别效果，准确率和AUC值分别为0.8718和0.9305；将四种深度学习模型输入上述序列组合后，3D ResNet-18模型外部验证的准确率和AUC分别为0.8125、0.8899，均为所有模型中最高。
多序列磁共振图像与深度学习模型相结合能够有效术前识别胶质母细胞瘤和孤立性脑转移瘤，且深度学习模型3D ResNet-18在识别这两种肿瘤方面疗效最高。
