用于子宫内膜异位症分型的盆腔MRI多尺度放射组学：强调数据异质性约束
Multi-scale radiomics in pelvic MRI for endometriosis subtyping: highlighting data heterogeneity constraints
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中文总结 AI 辅助
本研究基于UT-EndoMRI数据集，探索盆腔MRI多尺度放射组学用于子宫内膜异位症分型，发现原始小波特征结合梯度提升分类器AUC达0.80，但小型多中心数据集中放射组学分型存在脆弱性。
中文摘要 AI 辅助
分析女性盆腔MRI颇具挑战性，尤其在评估子宫内膜异位症时，视觉特征受多种因素影响，包括解剖结构复杂性、技术变异性及阅片者间变异性。本研究使用公开可用的UT-EndoMRI数据集，评估基于放射组学的患者层面子宫内膜异位症分型流程。我们从手动分割的子宫和卵巢区域提取放射组学特征，对比多种多尺度特征表示及特征选择策略。训练有监督分类器以区分至少患有1个子宫内膜异位囊肿的患者与未患病患者，并进行无监督扰动分析，以评估放射组学特征是否能揭示可重复的患者亚组。采用原始小波衍生特征与梯度提升分类器实现最佳有监督性能，AUC达0.80，但该模型产生多个假阳性，导致特异性较低。ComBat标准化并未持续提升性能，表明在小型多中心队列中，采集组患者数量极少，事后标准化效果不足。通过无监督聚类分析，我们识别出可重复但分离度较差的分区，且仍与采集变量相关。总体而言，这些结果提示盆腔MRI放射组学包含子宫内膜异位症分型的初步信号，同时凸显了小型多中心数据集中放射组学分型的脆弱性。
英文摘要
Analyzing female pelvic MRIs is challenging, especially for evaluating endometriosis, where visual features are influenced by several factors, including anatomical complexity, technical variability, and inter-reader variability. Here, we evaluate a radiomics-based pipeline for patient-level endometriosis subtyping using the publicly available UT-EndoMRI dataset. We extract radiomics features from manually segmented uterine and ovarian regions and compare several multi-scale feature representations and feature-selection strategies. We train supervised classifiers to distinguish patients with at least one endometrioma from those without, and perform an unsupervised perturbation analysis to assess whether radiomics profiles reveal reproducible patient subgroups. The best supervised performance is achieved using raw Wavelet-derived features and a Gradient Boosting classifier, yielding an AUC of 0.80. However, this model produces several false positives, resulting in low specificity. ComBat harmonization does not consistently improve performance, suggesting that post hoc harmonization is insufficient in a small, multi-site cohort in which acquisition groups contained very few patients. Using an unsupervised clustering analysis, we identify reproducible but poorly separated partitions that remain associated with acquisition variables. Overall, these results suggest that pelvic MRI radiomics contain a preliminary signal for endometriosis subtyping, while highlighting the fragility of radiomics-based subtyping in small, multi-site datasets.
