用负向控制发现影像特征常受肿瘤体积干扰，影响生物标志物可靠性
Negative controls reveal volume-driven confounding in radiomics and imaging foundation model features
- 通过扰动体素生成对照图像，检验特征是否独立于空间结构
- 3552个肿瘤体积中，多个模型在结构破坏后仍保持预测性能
- 适合开发可解释、可复现的影像生物标志物的研究者使用
放射组学和影像基础模型有望提供肿瘤生物学的无创生物标志物，但其预测特征可能反映的是肿瘤体积或采集伪影，而非有意义的图像结构。我们提出READII-2-ROQC——一个开源框架，利用体积保持的负向控制评估放射组学与深度影像特征是否捕捉到独立的空间信号。该框架采用可配置的随机化策略，在肿瘤、背景及全图区域生成体素扰动图像，比较原始图像与对照图像的特征表现和模型性能。应用于三个公开癌症影像队列，共处理3552个肿瘤体积，从原始图像及九组匹配对照中提取PyRadiomics和基础模型特征。复现了已发表的生存率与HPV状态预测签名，结果显示多个模型在空间结构被破坏后仍保持性能，揭示存在体积驱动或上下文混淆；而另一些模型则对扰动敏感。READII-2-ROQC为开发可解释、生物学基础坚实的影像生物标志物和可复现的放射组学流程提供了可扩展的质量控制策略。
原文摘要 · Abstract (English)
Radiomics and imaging foundation models promise non-invasive biomarkers of tumour biology, yet predictive signatures may reflect tumour volume or acquisition artifacts rather than meaningful image structure. We introduce READII-2-ROQC, an open-source framework that uses volume-preserving negative controls to assess whether radiomic and deep imaging features capture independent spatial signals. READII-2-ROQC generates voxel-perturbed images across tumour, background and whole-image regions using configurable randomization strategies, then compares feature behaviour and model performance between original and control images. Applied to three public cancer imaging cohorts, the framework processed 3,552 tumour volumes and extracted PyRadiomics and foundation-model features from original images and nine matched controls. Reproducing published survival and HPV-status signatures, we show that multiple models retain performance after spatial structure is destroyed, revealing volume-driven or contextual confounding, whereas others show perturbation-sensitive signal. READII-2-ROQC provides a scalable quality-control strategy for developing interpretable, biologically grounded imaging biomarkers and reproducible radiomics workflows.
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