ArticleBMC medical imaging2026
A multimodal deep learning classifier for prediction of HER2-low expression in triple-negative breast cancer.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTriple-negative breast cancer (TNBC) is an aggressive molecular subtype. With the new definition of HER2-low status and the availability of anti-HER2 drug conjugates, the drug regimens for HER2-low patients in TNBC could be individually tailored.
methodsA total of 122 patients with 122 pathologically confirmed TNBCs was retrospectively investigated from 2017 to 2022. Breast US and multiparametric MRI (T2WI, DWI and DCE-MRI) were collected and analyzed. To address data scarcity, a few-shot deep learning framework (FADNET) was developed and evaluated under simulated data-constrained conditions. The model used a frozen ResNet34 backbone trained via contrastive learning and soft k-means for classification. We compared pre-treatment imaging-only models (single modalities and multimodal fusion) against a hybrid model (CLI-FADNET) that incorporated imaging and a postoperative clinical variable for post-surgical assessment.
resultsOf 122 TNBC cases, 13 (11%) patients experienced postoperative recurrence. Most clinical characteristics showed no significant differences between HER2-zero and HER2-low groups except for postoperative lymph node metastasis (≥ 4 positive nodes) (P = 0.004). Pre-treatment models based on a single imaging modality achieved AUCs ranging from 0.764 to 0.806, while the multimodal imaging fusion model improved the AUC to 0.877. The CLI-FADNET model, integrated with the postoperative clinical variable, further increased the AUC to 0.884.
conclusionThis study validates the FADNET few-shot learning framework for classifying HER2 status under data-constrained conditions. While the imaging-only models offer pre-treatment evaluation, the CLI-FADNET hybrid model serves as a comprehensive post-surgical assessment tool. Rather than presenting a fully deployable clinical model, this methodological validation demonstrates the potential of few-shot learning in rare tumor subtypes, establishing a foundation for future large-scale external validation.
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