用双能CT数据区分乳腺癌淋巴结转移程度，提升诊断准确率。
DECT-based Space-Squeeze Method for Multi-Class Classification of Metastatic Lymph Nodes in Breast Cancer
- 通过通道注意力压缩11层能量特征，融合光谱空间信息。
- 虚拟类别注入使类间更分明、类内更紧凑，测试AUC达0.86。
- 适合临床非侵入性评估淋巴结转移负荷，辅助治疗决策。
背景：准确评估腋窝淋巴结的转移负担对指导乳腺癌治疗至关重要，但传统影像技术难以区分转移程度并全面捕捉淋巴结特征。本研究利用双能计算机断层扫描（DECT）提取光谱-空间信息，以改进多类别分类。目的：构建一种非侵入性DECT模型，将哨兵淋巴结分为三类：无转移（N₀）、低转移负荷（N₊(1-2)）、高转移负荷（N₊(≥3)），辅助治疗规划。方法：提出一种新型空间压缩方法，结合两项创新：（1）通道注意力机制，用于压缩并重新校准11个能量层级的光谱-空间特征；（2）虚拟类别注入，以增强类别边界并压缩表示空间内的类内差异。结果：在227例经活检证实的病例上评估，模型平均测试AUC为0.86（95%置信区间：0.80–0.91），优于主流CNN模型（VGG、ResNet等）。通道注意力与虚拟类别组件分别带来5.01%和5.87%的AUC提升，体现互补优势。结论：该框架通过有效整合DECT的光谱-空间数据并缓解类别模糊，显著提升诊断性能，为临床提供一种有前景的非侵入性转移负荷评估工具。
原文摘要 · Abstract (English)
Background: Accurate assessment of metastatic burden in axillary lymph nodes is crucial for guiding breast cancer treatment decisions, yet conventional imaging modalities struggle to differentiate metastatic burden levels and capture comprehensive lymph node characteristics. This study leverages dual-energy computed tomography (DECT) to exploit spectral-spatial information for improved multi-class classification. Purpose: To develop a noninvasive DECT-based model classifying sentinel lymph nodes into three categories: no metastasis ($N_0$), low metastatic burden ($N_{+(1-2)}$), and heavy metastatic burden ($N_{+(\geq3)}$), thereby aiding therapeutic planning. Methods: We propose a novel space-squeeze method combining two innovations: (1) a channel-wise attention mechanism to compress and recalibrate spectral-spatial features across 11 energy levels, and (2) virtual class injection to sharpen inter-class boundaries and compact intra-class variations in the representation space. Results: Evaluated on 227 biopsy-confirmed cases, our method achieved an average test AUC of 0.86 (95% CI: 0.80-0.91) across three cross-validation folds, outperforming established CNNs (VGG, ResNet, etc). The channel-wise attention and virtual class components individually improved AUC by 5.01% and 5.87%, respectively, demonstrating complementary benefits. Conclusions: The proposed framework enhances diagnostic AUC by effectively integrating DECT's spectral-spatial data and mitigating class ambiguity, offering a promising tool for noninvasive metastatic burden assessment in clinical practice.
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