Multimodal radiomics in glioma: predicting recurrence in the peritumoural brain zone using integrated MRI.
Authors
Affiliations (11)
Affiliations (11)
- Department of Radiology, The 958th Army Hospital of the Chinese People's Liberation Army, Chongqing, 400000, China.
- Department of Radiology, Southwest Hospital Army Medical University (Third Military Medical University), Chongqing, China.
- School of Medicine, Chongqing University, Chongqing, China.
- Department of Nuclear Medicine, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
- 7T Magnetic Resonance Imaging Translational Medical Center, Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Chongqing, China.
- Department of Urology, The Third Affliated Hospital of Chongging Medical University, Chongging, China.
- Department of Radiology, The 958th Army Hospital of the Chinese People's Liberation Army, Chongqing, 400000, China. [email protected].
- Department of Radiology, Southwest Hospital Army Medical University (Third Military Medical University), Chongqing, China. [email protected].
- Department of Nuclear Medicine, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China. [email protected].
- Glioma Medicine Research Center, The First Affiliated Hospital of Army Medical University, Chongqing, China. [email protected].
Abstract
Gliomas exhibit a high recurrence rate, particularly in the peritumoural brain zone after surgery. This study aims to develop and validate a radiomics-based model using preoperative fluid-attenuated inversion recovery (FLAIR) and T1-weighted contrast-enhanced (T1-CE) magnetic resonance imaging (MRI) sequences to predict glioma recurrence within specific quadrants of the surgical margin. In this retrospective study, 149 patients with confirmed glioma recurrence were included. 23 cases of data from Guizhou Medical University were used as a test set, and the remaining data were randomly used as a training set (70%) and a validation set (30%). Two radiologists from the research group established a Cartesian coordinate system centred on the tumour, based on FLAIR and T1-CE MRI sequences, dividing the tumour into four quadrants. Recurrence in each quadrant after surgery was assessed, categorising preoperative tumour quadrants as recurrent and non-recurrent. Following the division of tumours into quadrants and the removal of outliers, These quadrants were assigned to a training set (105 non-recurrence quadrants and 226 recurrence quadrants), a verification set (45 non-recurrence quadrants and 97 recurrence quadrants) and a test set (16 non-recurrence quadrants and 68 recurrence quadrants). Imaging features were extracted from preoperative sequences, and feature selection was performed using least absolute shrinkage and selection operator. Machine learning models included support vector machine, random forest, extra trees, and XGBoost. Clinical efficacy was evaluated through model calibration and decision curve analysis. The fusion model, which combines features from FLAIR and T1-CE sequences, exhibited higher predictive accuracy than single-modality models. Among the models, the LightGBM model demonstrated the highest predictive accuracy, with an area under the curve of 0.906 in the training set, 0.832 in the validation set and 0.805 in the test set. The study highlights the potential of a multimodal radiomics approach for predicting glioma recurrence, with the fusion model serving as a robust tool for clinical decision-making.