TopoTxR：一种用于DCE-MRI乳腺实质学习的拓扑引导深度卷积网络
TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs
AI总结：
针对DCE-MRI乳腺实质表征难题，提出拓扑引导深度网络TopoTxR，显式提取多尺度拓扑结构并融入注意力机制，在预测新辅助化疗反应中较最先进方法准确率提升2.6%、AUC提升4.6%。
摘要：
在动态对比增强磁共振成像（DCE-MRI）中表征乳腺实质是一项具有挑战性的任务，因为潜在的组织结构具有复杂性。现有的定量方法，如放射组学和深度学习模型，缺乏对复杂且微妙的实质结构（包括纤维腺体组织）的显式量化。为解决这一问题，我们提出了一种新颖的拓扑方法，该方法显式提取多尺度拓扑结构以更好地逼近乳腺实质结构，然后通过注意力机制将这些结构整合到基于深度学习的预测模型中。我们的拓扑信息深度学习模型TopoTxR利用拓扑结构为疾病病理生理学和治疗反应中的关键组织提供增强的洞察。我们使用VICTRE仿真乳腺数据集对TopoTxR进行了实证验证，结果表明我们模型提取的拓扑结构能有效逼近乳腺实质结构。我们进一步证明了TopoTxR在预测新辅助化疗反应方面的有效性。我们的定性和定量分析表明，在治疗前影像中，对治疗有良好反应（达到病理完全缓解pCR）的患者与未达到的患者相比，乳腺组织表现出不同的拓扑行为。在与公开可用的I-SPY 1数据集（N=161，包括47例pCR患者和114例非pCR患者）和Rutgers专有数据集（N=120，其中69例患者达到pCR，51例未达到）上的多个基线的比较分析中，TopoTxR表现出显著改进，与最先进方法相比，准确率提高了2.6%，AUC提升了4.6%。
英文摘要：
Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Existing quantitative approaches, like radiomics and deep learning models, lack explicit quantification of intricate and subtle parenchymal structures, including fibroglandular tissue. To address this, we propose a novel topological approach that explicitly extracts multi-scale topological structures to better approximate breast parenchymal structures, and then incorporates these structures into a deep-learning-based prediction model via an attention mechanism. Our topology-informed deep learning model, \emph{TopoTxR}, leverages topology to provide enhanced insights into tissues critical for disease pathophysiology and treatment response. We empirically validate \emph{TopoTxR} using the VICTRE phantom breast dataset, showing that the topological structures extracted by our model effectively approximate the breast parenchymal structures. We further demonstrate \emph{TopoTxR}'s efficacy in predicting response to neoadjuvant chemotherapy. Our qualitative and quantitative analyses suggest differential topological behavior of breast tissue in treatment-naïve imaging, in patients who respond favorably to therapy as achieving pathological complete response (pCR) versus those who do not. In a comparative analysis with several baselines on the publicly available I-SPY 1 dataset (N=161, including 47 patients with pCR and 114 without) and the Rutgers proprietary dataset (N=120, with 69 patients achieving pCR and 51 not), \emph{TopoTxR} demonstrates a notable improvement, achieving a 2.6\% increase in accuracy and a 4.6\% enhancement in AUC compared to the state-of-the-art method.
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