Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jun 3.
doi: 10.1007/s11517-025-03383-1. Online ahead of print.

Self-knowledge distillation for prediction of breast cancer molecular subtypes based on digital breast tomosynthesis

Wei Guo  1 , Jiayi Bo  1 , Shilin Chen  1 , Zhaoxuan Gong  1 , Guodong Zhang  1 , Hanxun Zhou  2 , Zekun Wang  3 , Peng Zhao  4 , Wenyan Jiang  5
Affiliations

Self-knowledge distillation for prediction of breast cancer molecular subtypes based on digital breast tomosynthesis

Wei Guo et al. Med Biol Eng Comput. .

Abstract

This study aims to investigate the effectiveness of self-knowledge distillation (self-KD) with progressive refinement in the early prediction of molecular subtypes of breast cancer (BC) using digital breast tomosynthesis (DBT) images. This study conducted a retrospective analysis of 368 patients who underwent breast DBT and/or magnetic resonance imaging (MRI) scans at our hospital. Among these patients, 303 underwent DBT scans and 119 underwent MRI scans. Of the DBT patients, 137 had images with molecular subtypes labels, while the remaining 166 did not have molecular subtype annotations. None of the MRI patients had the corresponding molecular subtype labels. To address the issue of insufficient labeled DBT images, we proposed a self-knowledge distillation (self-KD) framework with progressive refinement to more effectively utilize the unlabeled MRI and DBT image. Initially, the teacher network was pre-trained using unlabeled MRI images to capture the essential characteristics of BC. Subsequently, the teacher network was progressively refined to generate more accurate soft labels for the unlabeled DBT images, which improved the performance of the student network through KD. Additionally, a noise-adaptive layer was integrated to adjust the soft labels for more accurate learning. The performance of our method was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) values. The proposed self-KD method achieved an AUC of 0.834, ACC of 0.732, SEN of 0.930, and SPE of 0.734, which surpassed the existing methods for BC molecular subtype prediction. Specifically, compared to the baseline KD, our self-KD improved AUC by 9%, ACC by 6%, SEN by 26%, and SPE by 9%. The proposed self-KD framework effectively refines the network using both labeled and unlabeled images, which enables more accurate BC molecular subtype prediction.

Keywords: Breast cancer; DBT; MRI; Molecular subtype; Self-knowledge distillation.

PubMed Disclaimer

Conflict of interest statement

Declarations. Conflict of interest: The authors declare no competing interests.

Similar articles

See all similar articles

Cited by

References

    1. Chhikara BS, Parang K (2022) Global Cancer Statistics 2022: the trends projection analysis. Chem Biol Lett 10(1):451–451
    1. Siegel RL, Miller KD, Wagle NS et al (2023) Cancer statistics, 2023. CA Cancer J Clin 73(1):17–48 - DOI - PubMed
    1. Zhu S (2000) Molecular portraits of human breast tumours. Nature 490(7418):61
    1. Haynes B, Sarma A, Nangia-Makker P et al (2017) Breast cancer complexity: implications of intratumoral heterogeneity in clinical management. Cancer Metastasis Rev 36(3):547–555 - DOI - PubMed - PMC
    1. Ades F, Zardavas D, Bozovic-Spasojevic I et al (2014) Luminal B breast cancer: molecular characterization, clinical management, and future perspectives. J Clin Onco 32:2794–2803 - DOI

LinkOut - more resources