发表机构
Nankai University; Zhongguancun Academy; The First Medical Center of Chinese PLA General Hospital; Beijing University of Posts and Telecommunications; The Six Medical Center of Chinese PLA General Hospital; Tsinghua University（南开大学; 中关村学院; 中国人民解放军总医院第一医学中心; 北京邮电大学; 中国人民解放军总医院第六医学中心; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对乳腺DCE-MRI分类中患者特异性背景变异性问题，提出PRISM-Net无配准双侧框架，利用对侧乳腺特征建模跨乳腺对应，在多数据集上优于基线，提升了乳腺病灶分类性能。
AI 中文摘要
乳腺DCE-MRI人工智能正被越来越多地探索用于乳腺水平分类，涵盖无病灶、良性和恶性结果，超出了传统以病灶为中心的诊断范围。然而，在这一更广泛的诊断范围内，患者特异性背景变异性仍是各类分类任务中成像混淆的主要来源。现有方法主要聚焦单侧或以病灶为中心的分析，而双侧方法对空间自适应跨乳腺对应关系的显式建模有限。我们提出PRISM-Net，这是一种无配准的双侧框架，利用对侧乳腺特征作为患者特异性参考进行背景感知表征学习。PRISM-Net整合双侧特征匹配与不对称感知注意力，以建立自适应跨乳腺对应关系并增强判别性不对称模式的表征。在ODELIA数据集的分布内测试集上，Macro AUC、Micro AUC和二次加权kappa分别为84.11±2.33、90.64±1.61和60.94±5.64；在保留机构测试集上，上述指标分别为68.51±4.54、80.74±2.68和43.45±7.10，在主要评估指标上优于所评估的基线方法。PRISM-Net在独立机构评估和背景复杂度评估中也展现出性能。消融实验表明，双侧关系建模和不对称感知重加权均对分类性能提升有贡献。这些发现凸显患者特异性双侧参考建模是基于临床依据的DCE-MRI解读策略，通过显式建模背景复杂度提升了对不对称模式的判别能力。
英文摘要
Breast DCE-MRI AI is increasingly being explored for breast-level classification of no-lesion, benign, and malignant findings, beyond conventional lesion-centered diagnosis. Within this broader diagnostic scope, however, patient-specific background variability remains a major source of imaging confounding across classification tasks. Existing approaches predominantly focus on unilateral or lesion-centric analysis, whereas bilateral methods offer limited explicit modeling of spatially adaptive cross-breast correspondence. We propose PRISM-Net, a registration-free bilateral framework that leverages contralateral breast features as patient-specific references for background-aware representation learning. PRISM-Net integrates bilateral feature matching and asymmetry-aware attention to establish adaptive inter-breast correspondence and enhance representations of discriminative asymmetric patterns. On ODELIA, Macro AUC, Micro AUC, and quadratic weighted kappa were $84.11 \pm 2.33$, $90.64 \pm 1.61$, and $60.94 \pm 5.64$ on the in-distribution test set, and $68.51 \pm 4.54$, $80.74 \pm 2.68$, and $43.45 \pm 7.10$ on the held-out institution, respectively, outperforming the evaluated baseline methods across the primary evaluation metrics. PRISM-Net further demonstrated performance on independent institutional and background-complexity evaluations. Ablation experiments revealed that both bilateral relation modeling and asymmetry-aware reweighting contributed to improved classification performance. These findings highlight patient-specific bilateral reference modeling as a clinically grounded strategy for DCE-MRI interpretation, improving asymmetric pattern discrimination through explicit modeling of background complexity.
Comments15 pages, 7 figures, 5 tables
