用图结构分析肿瘤影像异质性，帮医生区分良恶性病变。
Graph-Radiomic Learning (GrRAiL) Descriptor to Characterize Imaging Heterogeneity in Confounding Tumor Pathologies
- 将影像分成小区域并建图，捕捉复杂空间关系
- 在三种肿瘤中准确率超现有方法10%以上
- 适合需要精准区分肿瘤复发与治疗反应的临床场景
实体瘤诊断的一大挑战是可靠区分混淆性病灶与恶性肿瘤。尽管放射组学试图通过CT/MRI提取病灶异质性特征，但多数方法对兴趣区（ROI）进行整体聚合，忽略了不同强度区域间的复杂空间关系。本文提出一种新型图-放射组学学习（GrRAiL）描述符，用于表征临床MRI中的瘤内异质性（ILH）。GrRAiL首先利用体素级放射组学测量识别子区域簇，再计算图论指标量化簇间空间关联。生成的加权图编码了ROI内的高阶空间关系，旨在可靠捕捉ILH并区分良性病灶与恶性肿瘤。为评估有效性与临床可行性，GrRAiL在n=947名患者中进行了三类应用测试：胶质母细胞瘤（GBM；n=106）中区分复发与放疗效应，脑转移瘤（n=233）中区分复发与放射性坏死，以及胰腺导管内乳头状黏液性新生物（IPMN；n=608）的风险分层（低/无风险 vs 高风险）。在多机构环境下，GrRAiL持续优于现有主流方法——图神经网络（GNN）、纹理放射组学及强度图分析。在GBM中，交叉验证与测试准确率分别为89%和78%，较对比方法提升超10%；在脑转移瘤中，对应准确率为84%和74%，提升超过13%；在IPMN风险分层中，交叉验证与测试准确率分别为84%和75%，提升超10%。
原文摘要 · Abstract (English)
A significant challenge in solid tumors is reliably distinguishing confounding pathologies from malignant neoplasms on routine imaging. While radiomics methods seek surrogate markers of lesion heterogeneity on CT/MRI, many aggregate features across the region of interest (ROI) and miss complex spatial relationships among varying intensity compositions. We present a new Graph-Radiomic Learning (GrRAiL) descriptor for characterizing intralesional heterogeneity (ILH) on clinical MRI scans. GrRAiL (1) identifies clusters of sub-regions using per-voxel radiomic measurements, then (2) computes graph-theoretic metrics to quantify spatial associations among clusters. The resulting weighted graphs encode higher-order spatial relationships within the ROI, aiming to reliably capture ILH and disambiguate confounding pathologies from malignancy. To assess efficacy and clinical feasibility, GrRAiL was evaluated in n=947 subjects spanning three use cases: differentiating tumor recurrence from radiation effects in glioblastoma (GBM; n=106) and brain metastasis (n=233), and stratifying pancreatic intraductal papillary mucinous neoplasms (IPMNs) into no+low vs high risk (n=608). In a multi-institutional setting, GrRAiL consistently outperformed state-of-the-art baselines - Graph Neural Networks (GNNs), textural radiomics, and intensity-graph analysis. In GBM, cross-validation (CV) and test accuracies for recurrence vs pseudo-progression were 89% and 78% with >10% test-accuracy gains over comparators. In brain metastasis, CV and test accuracies for recurrence vs radiation necrosis were 84% and 74% (>13% improvement). For IPMN risk stratification, CV and test accuracies were 84% and 75%, showing >10% improvement.
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