发表机构
Princess Margaret Cancer Centre; University Health Network（玛格丽特公主癌症中心; 大学网络医疗保健系统）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究推出开源框架READII-2-ROQC，通过体积保持阴性对照分析放射组学和影像基础模型特征，揭示体积驱动的混杂效应，为可解释影像生物标志物开发提供质控策略。
AI 中文摘要
放射组学和影像基础模型有望成为肿瘤生物学的非侵入性生物标志物，但预测特征可能反映肿瘤体积或采集伪影，而非有意义的图像结构。我们推出READII-2-ROQC，这是一个开源框架，利用体积保持的阴性对照评估放射组学和深度影像特征是否捕获独立空间信号。READII-2-ROQC采用可配置随机策略，生成肿瘤、背景及全图像区域的体素扰动图像，再比较原始图像与对照图像间的特征行为和模型性能。将其应用于三个公开癌症影像队列，该框架处理了3552个肿瘤体积，从原始图像和9个匹配对照中提取PyRadiomics及基础模型特征。重现已发表的生存和HPV状态特征后，我们发现多个模型在空间结构被破坏后仍保留性能，揭示体积驱动或上下文混杂，而其他模型则表现出对扰动敏感的信号。READII-2-ROQC为开发可解释、基于生物学的影像生物标志物及可重复的放射组学工作流提供了可扩展的质量控制策略。
英文摘要
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.
Comments22 pages (including supplementary), 6 figures, 2 supplementary tables, 5 supplementary figures
