Li Qian, Xiang Chaodong, Zeng Xianchun, Liao Ang, Chen Kang, Yang Jing, Li Yong, Jia Min, Song Lingheng, Hu Xiaofei
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.
BMC Med Imaging. 2025 Aug 11;25(1):321. doi: 10.1186/s12880-025-01853-4.
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.
胶质瘤具有较高的复发率，尤其是在手术后的肿瘤周围脑区。本研究旨在开发并验证一种基于影像组学的模型，该模型使用术前液体衰减反转恢复（FLAIR）和T1加权对比增强（T1-CE）磁共振成像（MRI）序列来预测手术切缘特定象限内的胶质瘤复发情况。
在这项回顾性研究中，纳入了149例确诊为胶质瘤复发的患者。将来自贵州医科大学的23例数据用作测试集，其余数据随机用作训练集（70%）和验证集（30%）。研究小组的两名放射科医生基于FLAIR和T1-CE MRI序列，以肿瘤为中心建立了一个笛卡尔坐标系，将肿瘤分为四个象限。评估手术后每个象限的复发情况，将术前肿瘤象限分为复发和未复发。在将肿瘤划分为象限并去除异常值后，将这些象限分配到训练集（105个未复发象限和226个复发象限）、验证集（45个未复发象限和97个复发象限）和测试集（16个未复发象限和68个复发象限）。从术前序列中提取影像特征，并使用最小绝对收缩和选择算子进行特征选择。机器学习模型包括支持向量机、随机森林、极端随机树和XGBoost。通过模型校准和决策曲线分析评估临床疗效。
结合FLAIR和T1-CE序列特征的融合模型比单模态模型表现出更高的预测准确性。在这些模型中，LightGBM模型表现出最高的预测准确性，训练集的曲线下面积为0.906，验证集为0.832，测试集为0.805。
该研究突出了多模态影像组学方法在预测胶质瘤复发方面的潜力，融合模型可作为临床决策的有力工具。
