Multiple deep learning models based on MRI images in discriminating glioblastoma from solitary brain metastases: a multicentre study.
Authors
Affiliations (8)
Affiliations (8)
- 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.
- School of Control Science and Engineering, Shandong University, Jinan, China.
- Department of Head and Neck Comprehensive Radiotherapy, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, China.
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China. [email protected].
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong Province, 250117, China. [email protected].
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China. [email protected].
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong Province, 250117, China. [email protected].
Abstract
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.