发表机构
School of Computer Science, The University of Sydney; Department of Molecular Imaging, Royal Prince Alfred Hospital（悉尼大学计算机科学学院; 皇家阿尔弗雷德王子医院分子影像科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出HA-DLC框架，通过HSG、CPSA、DSFE模块联合优化，在两个公开mp-MRI肿瘤数据集上，实现优于现有方法的肿瘤分类性能
AI 中文摘要
肿瘤内异质性（ITH）反映肿瘤生物学的空间变异，是决定肿瘤行为、预后及治疗响应的重要因素。放射组学与深度学习在基于多参数MRI（mp-MRI）的肿瘤分类中展现潜力，但放射组学依赖手工特征，多数深度学习方法采用全肿瘤表示或手动定义的亚区，限制了对肿瘤异质性的可扩展建模。本文提出异质性感知深度学习分类（HA-DLC）框架，其明确建模影像衍生的肿瘤亚区以实现病变类型诊断与分子状态预测。HA-DLC包含：（1）异质性亚区生成（HSG）模块，通过无监督聚类生成初始伪标签亚区，随后的跨患者亚区对齐（CPSA）利用软分配将聚类衍生区域映射至共享标签空间；（2）双流特征提取（DSFE）模块，将局部异质性感知特征与全局肿瘤表示相融合。给定初始聚类掩码，通过软目标分割与分类目标对CPSA、分割、特征提取及分类进行端到端联合优化。在LLD-MMRI2023肝脏病变数据集与RSNA-ASNR-MICCAI 2021放射基因组脑肿瘤数据集上评估HA-DLC，结果显示其始终优于当前最优的放射组学与深度学习基线，证明跨患者亚区对齐及双流异质性建模对mp-MRI肿瘤分类的价值。
英文摘要
Intra-tumoural heterogeneity (ITH) reflects spatial variation in tumour biology and is an important determinant of tumour behaviour, prognosis, and treatment response. Radiomics and deep learning have shown promise for tumour classification from multiparametric MRI (mp-MRI), but radiomics relies on handcrafted features, while most deep learning methods use whole-tumour representations or manually defined sub-regions, limiting scalable modelling of tumour heterogeneity. We propose a Heterogeneity-Aware Deep Learning Classification (HA-DLC) framework that explicitly models imaging-derived tumour sub-regions for lesion-type diagnosis and molecular-status prediction. HA-DLC consists of: (1) a Heterogeneous Sub-region Generation (HSG) module that produces initial pseudo-labelled sub-regions via unsupervised clustering, followed by Cross-Patient Sub-region Alignment (CPSA), which maps cluster-derived regions to a shared label space using soft assignments; and (2) a Dual-Stream Feature Extraction (DSFE) module that integrates local heterogeneity-aware features with global tumour representations. Given the initial clustering masks, CPSA, segmentation, feature extraction, and classification are jointly optimized end-to-end using soft-target segmentation and classification objectives. We evaluate HA-DLC on the LLD-MMRI2023 liver lesion dataset and the RSNA-ASNR-MICCAI 2021 Radiogenomic Brain Tumour dataset. HA-DLC consistently outperforms state-of-the-art radiomics and deep learning baselines, demonstrating the value of cross-patient sub-region alignment and dual-stream heterogeneity modelling for tumour classification from mp-MRI.
