文章：

可复现且可解释的机器学习放射组学分析用于多形性胶质母细胞瘤总生存期预测

Reproducible and Interpretable Machine Learning-Based Radiomic Analysis for Overall Survival Prediction in Glioblastoma Multiforme

原文发布日期：30 September 2024

DOI: 10.3390/cancers16193351

类型: Article

开放获取: 是

英文摘要：

Purpose: To develop and validate an MRI-based radiomic model for predicting overall survival (OS) in patients diagnosed with glioblastoma multiforme (GBM), utilizing a retrospective dataset from multiple institutions. Materials and Methods: Pre-treatment MRI images of 289 GBM patients were collected. From each patient’s tumor volume, 660 radiomic features (RFs) were extracted and subjected to robustness analysis. The initial prognostic model with minimum RFs was subsequently enhanced by including clinical variables. The final clinical–radiomic model was derived through repeated three-fold cross-validation on the training dataset. Performance evaluation included assessment of concordance index (C-Index), integrated area under curve (iAUC) alongside patient stratification into low and high-risk groups for overall survival (OS). Results: The final prognostic model, which has the highest level of interpretability, utilized primary gross tumor volume (GTV) and one MRI modality (T2-FLAIR) as a predictor and integrated the age variable with two independent, robust RFs, achieving moderately good discriminatory performance (C-Index [95% confidence interval]: 0.69 [0.62–0.75]) with significant patient stratification (p= 7 × 10−5) on the validation cohort. Furthermore, the trained model exhibited the highest iAUC at 11 months (0.81) in the literature. Conclusion: We identified and validated a clinical–radiomic model for stratification of patients into low and high-risk groups based on OS in patients with GBM using a multicenter retrospective dataset. Future work will focus on the use of deep learning-based features, with recently standardized convolutional filters on OS tasks.

摘要翻译： 

目的：基于多机构回顾性数据集，开发并验证一种基于磁共振成像（MRI）的影像组学模型，用于预测多形性胶质母细胞瘤（GBM）患者的总生存期（OS）。材料与方法：收集289例GBM患者的治疗前MRI图像。从每位患者的肿瘤体积中提取660个影像组学特征（RFs），并进行稳健性分析。通过纳入临床变量，对包含最少RFs的初始预后模型进行优化。最终临床-影像组学模型通过在训练数据集上进行重复三折交叉验证得出。性能评估包括一致性指数（C-Index）、综合曲线下面积（iAUC）的评估，以及将患者按总生存期（OS）分为低风险和高风险组。结果：最终预后模型具有最高的可解释性，以原发大体肿瘤体积（GTV）和一种MRI序列（T2-FLAIR）作为预测因子，并结合年龄变量与两个独立且稳健的RFs，在验证队列中表现出中等良好的区分性能（C-Index [95%置信区间]：0.69 [0.62–0.75]），且患者分层显著（p= 7 × 10−5）。此外，训练模型在文献中显示出11个月时最高的iAUC（0.81）。结论：我们基于多中心回顾性数据集，识别并验证了一种临床-影像组学模型，可根据GBM患者的总生存期（OS）将其分为低风险和高风险组。未来工作将侧重于使用基于深度学习的特征，并结合近期标准化的卷积滤波器应用于OS任务。

原文链接：

Reproducible and Interpretable Machine Learning-Based Radiomic Analysis for Overall Survival Prediction in Glioblastoma Multiforme