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, TopoTxR, leverages topology to provide enhanced insights into tissues critical for disease pathophysiology and treatment response. We empirically validate 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 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), 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.

中文翻译：

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TopoTxR：用于 DCE-MRI 上乳腺实质学习的拓扑引导深度卷积网络


由于底层组织结构的复杂性，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。现有的定量方法，如放射组学和深度学习模型，缺乏对复杂和微妙的实质结构（包括纤维腺组织）的明确量化。为了解决这个问题，我们提出了一种新的拓扑方法，该方法显式提取多尺度拓扑结构以更好地接近乳腺实质结构，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 利用拓扑学来增强对疾病病理生理学和治疗反应至关重要的组织。我们使用 VICTRE 幻乳数据集对 TopoTxR 进行了实证验证，表明我们的模型提取的拓扑结构有效地接近了乳腺实质结构。我们进一步证明了 TopoTxR 在预测对新辅助化疗反应方面的疗效。我们的定性和定量分析表明，在初治成像中，对治疗反应良好的患者与未达到病理完全缓解 （pCR） 的患者乳房组织拓扑行为存在差异。在对公开可用的 I-SPY 1 数据集（N = 161，包括 47 名 pCR 患者和 114 名无 pCR 患者）和罗格斯专有数据集（N = 120,69 名患者达到 pCR，51 名未达到 pCR）上的几个基线进行比较分析中，TopoTxR 显示出显着的改进，与最先进的方法相比，准确性提高了 2.6%，AUC 提高了 4.6%。