Chaddad Ahmad, Jia Pingyue, Hu Yan, Katib Yousef, Kateb Reem, Daqqaq Tareef Sahal
Artificial Intelligence for Personalised Medicine, School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China.
Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure (ETS), Montreal, QC H3C 1K3, Canada.
Bioengineering (Basel). 2025 Apr 24;12(5):450. doi: 10.3390/bioengineering12050450.
Brain tumors are among the most common malignant tumors of the central nervous system, with high mortality and recurrence rates. Radiomics extracts quantitative features from medical images, converting them into predictive biomarkers for tumor diagnosis, prognosis, and survival analysis. Despite the invasiveness and heterogeneity of brain tumors, even with timely treatment, the overall survival time or survival probability is not necessarily favorable. Therefore, accurate prediction of brain tumor grade and survival outcomes is important for personalized treatment. In this study, we propose a radiomic model for the non-invasive prediction of brain tumor grade and patient survival outcomes. We used four magnetic resonance imaging (MRI) sequences from 159 patients with glioma. Four classifiers were employed based on whether feature selection was applied. The features were derived from regions of interest identified and corrected either manually or automatically. The extreme gradient boosting (XGB) model with 3860 radiomic features achieved the highest classification performance, with an AUC of 98.20%, in distinguishing LGG from GBM images using manually corrected labels. Similarly, the Random Forest (RF) model exhibits the best discrimination between short-term and long-term survival groups with a -value < 0.0003, a hazard ratio (HR) value of 3.24, and a 95% confidence interval (CI) of 1.63-4.43 based on the ICC features. The experimental findings demonstrate strong classification accuracy and effectively predict survival outcomes in glioma patients.
脑肿瘤是中枢神经系统最常见的恶性肿瘤之一，具有高死亡率和复发率。放射组学从医学图像中提取定量特征，将其转化为用于肿瘤诊断、预后和生存分析的预测生物标志物。尽管脑肿瘤具有侵袭性和异质性，即使及时治疗，总体生存时间或生存概率也不一定理想。因此，准确预测脑肿瘤分级和生存结果对于个性化治疗很重要。在本研究中，我们提出了一种用于非侵入性预测脑肿瘤分级和患者生存结果的放射组学模型。我们使用了159例神经胶质瘤患者的四个磁共振成像（MRI）序列。根据是否应用特征选择采用了四种分类器。这些特征来自手动或自动识别和校正的感兴趣区域。具有3860个放射组学特征的极端梯度提升（XGB）模型在使用手动校正标签区分低级别胶质瘤（LGG）和胶质母细胞瘤（GBM）图像时，实现了最高的分类性能，曲线下面积（AUC）为98.20%。同样，基于组内相关系数（ICC）特征，随机森林（RF）模型在区分短期和长期生存组方面表现最佳，P值<0.0003，风险比（HR）值为3.24，95%置信区间（CI）为1.63 - 4.43。实验结果证明了强大的分类准确性，并有效预测了神经胶质瘤患者的生存结果。
