Kavli Affiliate: Xiang Zhang
| First 5 Authors: Hai Jiang, Chushan Zheng, Jiawei Pan, Yuanpin Zhou, Qiongting Liu
| Summary:
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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