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Background <p>Triple-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.</p> Methods <p>A 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.</p> Results <p>Of 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) (<i>P</i> = 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.</p> Conclusion <p>This 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.</p>

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A multimodal deep learning classifier for prediction of HER2-low expression in triple-negative breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Junfei Fang,
  • Tianle Hong,
  • Yulan Tong,
  • Zedong Ren,
  • Cunke Miao,
  • Sijia Wen,
  • Meimei Du,
  • Meihao Wang,
  • Yezhi Lin,
  • Jiejie Zhou

摘要

Background

Triple-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.

Methods

A 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.

Results

Of 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.

Conclusion

This 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.