Department of Computer Science, State University of New York at Stony Brook, NY, USA.
Department of Computer Science, State University of New York at Stony Brook, NY, USA.
Med Image Anal. 2025 Jan;99:103373. doi: 10.1016/j.media.2024.103373. Epub 2024 Oct 16.
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.
在动态对比增强磁共振成像（DCE-MRI）中，对乳腺实质进行特征描述是一项具有挑战性的任务，这主要是由于其组织结构的复杂性所致。现有的定量方法，如放射组学和深度学习模型，缺乏对复杂和细微的实质结构的明确量化，包括纤维腺体组织。为了解决这个问题，我们提出了一种新的拓扑方法，该方法可以明确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纳入基于深度学习的预测模型中。我们的拓扑感知深度学习模型 TopoTxR 利用拓扑学为疾病病理生理学和治疗反应关键的组织提供了更深入的见解。我们使用 VICTRE 乳腺体模数据集对 TopoTxR 进行了实证验证，结果表明我们的模型提取的拓扑结构可以有效地逼近乳腺实质结构。我们进一步证明了 TopoTxR 在预测新辅助化疗反应方面的功效。我们的定性和定量分析表明，在治疗前成像中，在对治疗反应良好的患者（即实现病理完全缓解（pCR）的患者）和反应不佳的患者（即未实现 pCR 的患者）中，乳腺组织的拓扑行为存在差异。在与 I-SPY1 数据集（N=161，包括 47 例 pCR 患者和 114 例无 pCR 患者）和 Rutgers 专有数据集（N=120，包括 69 例 pCR 患者和 51 例无 pCR 患者）上的几个基线进行的比较分析中，TopoTxR 表现出显著的改进，与最先进的方法相比，准确性提高了 2.6%，AUC 提高了 4.6%。
