ArticleBMC medical imaging2025
Predicting axillary lymph node metastasis in breast cancer patients using CNN-GCN on DCE-MRI: a multicenter study.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Deep Learning for Detecting Axillary Lymph Node Metastasis in Breast Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Reimagining Nodal Staging in Colorectal Cancer: Toward a Novel Non-Invasive Imaging Approach.Cancers · 2026Review
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11 authors.
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Abstract
backgroundTo develop a deep learning model combining a convolution neural network (CNN) and a graph convolution network (GCN) based on dynamic contrast-enhanced (DCE) MRI for predicting axillary lymph node (ALN) metastasis in breast cancer patients while also aiming to explore the underlying biological mechanism by using RNA sequencing (RNA-seq) data.
methodsWe retrospectively collected DCE-MRI and clinical data from 1002 patients across four centers and one public dataset. Various CNN-GCN models were trained on tumor and ALN images and compared to radiomics models, the MSKCC model, and radiologists. RNA-seq data from 11 patients were used to explore associated biological pathways. Model performance was evaluated by accuracy, sensitivity, specificity, AUC, and DeLong test.
resultsParticipants were divided into a training set (n = 742, mean age: 53 years ±10 [SD]), an internal test set (n = 83, 53 years ±10), external test set 1 (n = 110, 50 years ±9) and external test set 2 (n = 67, 54 years ±11). The optimal CNN-GCN model, HRNet-GCN_tumor+ALN, achieved an AUC of 0.873 in the internal test set outperforming the LR_tumor+ALN (AUC: 0.790) and MSKCC models (AUC: 0.726) (DeLong test, p < 0.05). Radiologists’ performance improved with HRNet-GCN_tumor+ALN (in both the external test set 1 and 2, p < 0.05). High-risk group associated with pathways such as ribosome, synapse organization, and muscle contraction.
conclusionsThe proposed CNN-GCN fusion deep learning model showed good performance for preoperatively predicting ALN status in breast cancer patients.
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