DECT-based Space-Squeeze Method for Multi-Class Classification of Metastatic Lymph Nodes in Breast Cancer

TL;DR

This study introduces a novel DECT-based space-squeeze method with attention and virtual class injection to classify metastatic lymph nodes in breast cancer, achieving high accuracy and outperforming traditional CNNs.

Contribution

The paper presents a new spectral-spatial feature compression and class boundary sharpening technique for multi-class lymph node classification using DECT.

Findings

Achieved an average test AUC of 0.86 in classifying lymph node metastasis.

Channel-wise attention and virtual class injection improved AUC by over 5%.

Outperformed established CNN models in multi-class classification accuracy.

Abstract

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.