Breast cancer is the most common malignant tumor among women and the second
cause of cancer-related death. Early diagnosis in clinical practice is crucial
for timely treatment and prognosis. Dynamic contrast-enhanced magnetic
resonance imaging (DCE-MRI) has revealed great usability in the preoperative
diagnosis and assessing therapy effects thanks to its capability to reflect the
morphology and dynamic characteristics of breast lesions. However, most
existing computer-assisted diagnosis algorithms only consider conventional
radiomic features when classifying benign and malignant lesions in DCE-MRI. In
this study, we propose to fully leverage the dynamic characteristics from the
kinetic curves as well as the radiomic features to boost the classification
accuracy of benign and malignant breast lesions. The proposed method is a fully
automated solution by directly analyzing the 3D features from the DCE-MRI. The
proposed method is evaluated on an in-house dataset including 200 DCE-MRI scans
with 298 breast tumors (172 benign and 126 malignant tumors), achieving
favorable classification accuracy with an area under curve (AUC) of 0.94. By
simultaneously considering the dynamic and radiomic features, it is beneficial
to effectively distinguish between benign and malignant breast lesions.

利用 3D 特征分析动态和放射学特征可以有效区分良性和恶性乳腺病变，提高良性和恶性乳腺病变的分类准确性。

探索 DCE-MRI 中良性和恶性乳腺病变的运动曲线特征分类

Exploring Kinetic Curves Features for the Classification of Benign and  Malignant Breast Lesions in DCE-MRI

Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays
an important role in the screening and prognosis assessment of high-risk breast
cancer. The segmentation of cancerous regions is essential useful for the
subsequent analysis of breast MRI. To alleviate the annotation effort to train
the segmentation networks, we propose a weakly-supervised strategy using
extreme points as annotations for breast cancer segmentation. Without using any
bells and whistles, our strategy focuses on fully exploiting the learning
capability of the routine training procedure, i.e., the train - fine-tune -
retrain process. The network first utilizes the pseudo-masks generated using
the extreme points to train itself, by minimizing a contrastive loss, which
encourages the network to learn more representative features for cancerous
voxels. Then the trained network fine-tunes itself by using a similarity-aware
propagation learning (SimPLe) strategy, which leverages feature similarity
between unlabeled and positive voxels to propagate labels. Finally the network
retrains itself by employing the pseudo-masks generated using previous
fine-tuned network. The proposed method is evaluated on our collected DCE-MRI
dataset containing 206 patients with biopsy-proven breast cancers. Experimental
results demonstrate our method effectively fine-tunes the network by using the
SimPLe strategy, and achieves a mean Dice value of 81%.

本文提出一种利用极点标注对乳腺癌 DCE-MRI 图像进行弱监督学习分割的方法，使用多阶段 fine-tune 和 SimPLe 等策略，取得了 81% 均值 Dice 值。

SimPLe: 基于相似性感知传播学习的 DCE-MRI 弱监督乳腺癌分割

SimPLe: Similarity-Aware Propagation Learning for Weakly-Supervised  Breast Cancer Segmentation in DCE-MRI

Characterization of breast parenchyma on dynamic contrast-enhanced magnetic
resonance imaging (DCE-MRI) is a challenging task owing to the complexity of
underlying tissue structures. Current quantitative approaches, including
radiomics and deep learning models, do not explicitly capture the complex and
subtle parenchymal structures, such as fibroglandular tissue. In this paper, we
propose a novel method to direct a neural network's attention to a dedicated
set of voxels surrounding biologically relevant tissue structures. By
extracting multi-dimensional topological structures with high saliency, we
build a topology-derived biomarker, TopoTxR. We demonstrate the efficacy of
TopoTxR in predicting response to neoadjuvant chemotherapy in breast cancer.
Our qualitative and quantitative results suggest differential topological
behavior of breast tissue on treatment-na\"ive imaging, in patients who respond
favorably to therapy versus those who do not.

本文提出了一种新颖的方法，通过提取具有高显著性的多维拓扑结构，将神经网络的注意力引导到专用的一组体素，从而构建了一种拓扑导出的生物标志物，TopoTxR。实验结果表明，在乳腺癌新辅助化疗反应预测方面，TopoTxR 的有效性优于当前定量方法，且提示治疗前的影像学信息在预测疗效方面具有潜在的作用。