Abstract P2-02-20: Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Intraoperative Fluorescence Multi-modal Imaging

Field: Medicine (Health Sciences)

Abstract Axillary lymph node (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). Currently, sentinel lymph node biopsy (SLNB) and axillary lymph node dissection (ALND) are the standard methods for predicting and evaluating ALN status; however, intraoperative frozen section and pathological analysis are both time-consuming and laborious, and there is a certain false-positive rate. Therefore, there is a need for a method that can rapidly and accurately assess the status of ALN metastasis (ALNM) during surgery. To address the above clinical needs, this study combined near-infrared fluorescence imaging technology during BC surgery with advanced deep learning strategies to build a predictive model called "Multi-modal Fluorescence Imaging Feature Fusion Prediction" (MFI-FFP). For the multi-modal imaging data obtained during BC surgery, including white light imaging (WLI), near-infrared fluorescence imaging (FI), and pseudo-color imaging (PCI), we selectively customized feature extraction networks for each imaging modality to fully explore and utilized the complementary information between different modalities. We designed a multi-modal feature fusion module that effectively integrates features extracted from each modality by combining global and local information. Additionally, due to the common issues of class imbalance and classification challenges in real-world clinical datasets, we also designed a novel loss function to enhance the recognition of minority class instances. Through the above design, MFI-FFP can quickly and accurately determine the positive or negative of ALNM in surgery, reducing diagnostic time and costs, and ultimately improving patient outcomes. The BC lymph node image database used in this study was derived from intraoperative fluorescence videos of 93 patients. During the surgical procedure, 312 excised lymph nodes were obtained from these 93 patients. with 35 lymph nodes testing positive for metastasis. The ratio of negative samples to positive samples was approximately 8:1. During data preprocessing, key frames of each lymph node in the videos were captured and extracted as intraoperative fluorescence images. Each captured frame contained the three modes of data which include WLI, FI, and PCI. A total of 936 imaging data were captured, with 312 images corresponding to each mode. Experimental evaluations demonstrated that the MFI-FFP model achieved remarkable performance in predicting ALNM, with an area under the ROC curve (AUC) of 0.7512 and an accuracy (ACC) of 0.8617, significantly outperforming single-modality models (WLI: AUC 0.5810; FI: AUC 0.5226; PCI: AUC 0.5601) and dual-modality models (WLI+FI: AUC 0.6786; FI+PCI: AUC 0.6089; WLI+PCI: AUC 0.6571). Given the absence of dedicated multimodal models for intraoperative fluorescence data from BC lymph nodes, we concatenated multi-modal features and input them into models that excel in natural image classification tasks, including resnet18, swin_transformer, convNeXt, efficientnet_v2, mobilenet_v3. Experimental results indicated that our model significantly outperforms the best-performing model in natural image classification, MobileNet_v3, with an AUC value of 0.6595. This study not only validated the precision and reliability of the MFI-FFP model in predicting the status of ALNM in BC but also highlighted its substantial potential in assisting clinical decision-making and enabling real-time diagnosis of ALNM. Citation Format: He Sun, Xiaobo Zhu, Jiaqian Li, Zhenyu Liu, Yu An, Jie Tian. Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Intraoperative Fluorescence Multi-modal Imaging [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P2-02-20.