OMT and tensor SVD-based deep learning model for segmentation and predicting genetic markers of glioma: A multicenter study.
Authors
Affiliations (12)
Affiliations (12)
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
- School of Mathematics and Shing-Tung Yau Center, Southeast University, Nanjing 210096, China.
- Nanjing Center for Applied Mathematics, Nanjing 211135, China.
- Shanghai Institute for Mathematics and Interdisciplinary Sciences, Shanghai 200433, China.
- Department of Mathematics, National Taiwan Normal University, Taipei 116, Taiwan.
- Department of Neurosurgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China.
- Department of Neurology, LuAn People's Hospital, LuAn 237005, China.
- Yau Mathematical Sciences Center, Jingzhai, Tsinghua University, Beijing 100084, China.
- Medical Imaging Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
- Nanjing Key Laboratory for Cardiovascular Information and Health Engineering Medicine, Nanjing 210002, China.
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing 210008, China.
Abstract
Glioma is the most common primary malignant brain tumor and preoperative genetic profiling is essential for the management of glioma patients. Our study focused on tumor regions segmentation and predicting the World Health Organization (WHO) grade, isocitrate dehydrogenase (IDH) mutation, and 1p/19q codeletion status using deep learning models on preoperative MRI. To achieve accurate tumor segmentation, we developed an optimal mass transport (OMT) approach to transform irregular MRI brain images into tensors. In addition, we proposed an algebraic preclassification (APC) model utilizing multimode OMT tensor singular value decomposition (SVD) to estimate preclassification probabilities. The fully automated deep learning model named OMT-APC was used for multitask classification. Our study incorporated preoperative brain MRI data from 3,565 glioma patients across 16 datasets spanning Asia, Europe, and America. Among these, 2,551 patients from 5 datasets were used for training and internal validation. In comparison, 1,014 patients from 11 datasets, including 242 patients from The Cancer Genome Atlas (TCGA), were used as independent external test. The OMT segmentation model achieved mean lesion-wise Dice scores of 0.880. The OMT-APC model was evaluated on the TCGA dataset, achieving accuracies of 0.855, 0.917, and 0.809, with AUC scores of 0.845, 0.908, and 0.769 for WHO grade, IDH mutation, and 1p/19q codeletion, respectively, which outperformed the four radiologists in all tasks. These results highlighted the effectiveness of our OMT and tensor SVD-based methods in brain tumor genetic profiling, suggesting promising applications for algebraic and geometric methods in medical image analysis.