MRI影像特征对胶质母细胞瘤预后预测的增益有限，主要依赖年龄性别等临床因素。
The added value for MRI radiomics and deep-learning for glioblastoma prognostication compared to clinical and molecular information
- 对比临床与分子信息，传统影像组学和深度学习模型仅小幅提升预测性能
- 联合模型在外部验证中AUC达0.75，优于仅用临床或影像数据的模型
- 对无分子数据患者群体，影像特征价值更显著，但整体增益仍不明显
背景：影像组学在胶质母细胞瘤表征中展现潜力，但其相对于临床与分子预测因子的额外价值尚未证实。本研究基于大型多中心数据集，评估了常规影像组学（CR）与深度学习（DL）MRI影像组学在胶质母细胞瘤预后（≤6月与>6月生存）中的附加价值。方法：经筛选后，数据集包含来自瑞士五个中心及一个公开来源的1152例胶质母细胞瘤（WHO 2016）患者，涵盖临床（年龄、性别）、分子（MGMT、IDH）及基线MRI数据（T1、T1增强、FLAIR、T2）及肿瘤区域。采用标准方法构建CR与DL模型，并在内部与外部队列中评估。亚分析比较不同特征组合（影像仅、临床/分子仅、联合）及患者子集（S-1：所有患者；S-2：有分子数据者；S-3：IDH野生型）。结果：最优性能出现在全队列（S-1）。外部验证中，联合特征的CR模型达到AUC 0.75，略高于仅临床（0.74）和仅影像（0.68）模型，差异显著；DL模型趋势相似但无统计意义。在S-2与S-3中，联合模型未超越仅临床模型。探索性分析显示，影像数据在总生存预测中更具相关性：各子集中，联合模型显著优于仅临床模型，但优势仅2-4个C-index点。结论：尽管确认解剖性MRI序列对胶质母细胞瘤预后的预测价值，该多中心研究发现标准CR与DL影像组学方法对年龄、性别等人口学因素的预测能力提升微乎其微。
原文摘要 · Abstract (English)
Background: Radiomics shows promise in characterizing glioblastoma, but its added value over clinical and molecular predictors has yet to be proven. This study assessed the added value of conventional radiomics (CR) and deep learning (DL) MRI radiomics for glioblastoma prognosis (<= 6 vs > 6 months survival) on a large multi-center dataset. Methods: After patient selection, our curated dataset gathers 1152 glioblastoma (WHO 2016) patients from five Swiss centers and one public source. It included clinical (age, gender), molecular (MGMT, IDH), and baseline MRI data (T1, T1 contrast, FLAIR, T2) with tumor regions. CR and DL models were developed using standard methods and evaluated on internal and external cohorts. Sub-analyses assessed models with different feature sets (imaging-only, clinical/molecular-only, combined-features) and patient subsets (S-1: all patients, S-2: with molecular data, S-3: IDH wildtype). Results: The best performance was observed in the full cohort (S-1). In external validation, the combined-feature CR model achieved an AUC of 0.75, slightly, but significantly outperforming clinical-only (0.74) and imaging-only (0.68) models. DL models showed similar trends, though without statistical significance. In S-2 and S-3, combined models did not outperform clinical-only models. Exploratory analysis of CR models for overall survival prediction suggested greater relevance of imaging data: across all subsets, combined-feature models significantly outperformed clinical-only models, though with a modest advantage of 2-4 C-index points. Conclusions: While confirming the predictive value of anatomical MRI sequences for glioblastoma prognosis, this multi-center study found standard CR and DL radiomics approaches offer minimal added value over demographic predictors such as age and gender.
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