用对抗方法检测前列腺癌影像模型中临床变量的有用与干扰信号。
Causal-Adversarial Probing of Clinical Covariates for Prostate MRI Grading
- 构建因果框架，通过对抗抑制单个临床变量来探测其对影像诊断的影响。
- 抑制年龄、体重等变量可提升模型性能，表明它们是干扰信号；抑制PSA等则降低性能，说明其为有效信息。
- 适用于医学影像模型可解释性研究，尤其关注临床变量与影像间的潜在依赖关系。
基于多参数MRI的前列腺癌分级深度学习模型可能编码了反映疾病真实信号或非泛化性捷径信息的临床协变量，但其作用常被默认。本文提出一种基于因果推理的探针框架，用于分析在国际泌尿病理学会（ISUP）分级预测中临床协变量的依赖关系。不将mpMRI视为分级的直接原因，而是将影像表现与分级视为潜在肿瘤病理的观测结果，检验候选临床变量是否为干扰相关项、疾病相关代理变量或无关协变量。通过对抗框架，在保持影像分级预测能力的同时，逐个抑制特定临床变量的可解码性。该方法在2,903例前列腺MRI检查上开发并评估，并在576名患者上进行外部验证。结果显示，在二分类ISUP Grade Group ≥2任务中，抑制年龄、BMI和饮酒史分别使AUC提升1.23%、0.84%和1.42%（均p<0.05），表明这些变量为非泛化性干扰；而抑制PSA和前列腺体积则使AUC下降1.91%和7.61%（均p<0.001），说明其携带任务相关信号。结果表明，对抗协变量抑制能有效区分模型中潜在有害依赖与有价值信号。
原文摘要 · Abstract (English)
Deep learning models for prostate MRI-based cancer grading may encode clinical covariates that either reflect useful disease-related signal or non-generalising shortcut information, but their role is usually assumed. We propose a causal-reasoning framework for probing covariate dependence in MRI-based International Society of Urological Pathology (ISUP) Grade Group prediction. Rather than treating mpMRI as a direct cause of grade, we model MRI appearance and ISUP grade as observations of latent tumour pathology, and test whether candidate clinical variables act as nuisance correlates, disease-related proxies, or irrelevant covariates in the learned representation. We implement this using an adversarial framework that suppresses the decodability of individual clinical covariate at a time while preserving MRI-based grade prediction. The approach is developed and evaluated on 2,903 prostate MRI examinations, with external validation on 576 patients. We report a set of interesting and previously under-explored imaging-to-clinical-variable interactions in the context of deep learning generalisation. For examples, in binary ISUP Grade Group $\geq2$ classification, suppressing age, BMI, and alcohol use improved AUC by 1.23%, 0.84%, and 1.42%, respectively (all p < 0.05), suggesting reduced non-generalising covariate information; In contrast, suppressing PSA and prostate volume degraded AUC by 1.91% and 7.61% (all p < 0.001), indicating that these variables carried task-relevant signal. These findings show that adversarial covariate suppression can provide a practical representation-level analysis for distinguishing potentially harmful dependence from informative signal in prostate MRI grading models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。