AI and Cancer Research ── Breast ── 2026-10-06

A diagram of one breast cancer paper in three steps: cohort, method, test results. It classifies HER2 status in 239 women from two centers. A hybrid CNN-Transformer model takes three kinds of DWI images. On the external test set AUC is 0.91 vs. 0.87 for the best benchmark, and higher than two clinical experts. — Image abstract — the whole article on one page (click to enlarge)

1 Breast papers. Lead: Deep learning-based prediction of HER2 status from breast diffusion-weighted MRI

Journals covered and how papers are chosen: see the index

Breast cancer: treatment, trials and AI

Deep learning-based prediction of HER2 status from breast diffusion-weighted MRI
Frontiers in Oncology · 2026-10-05 · Evidence: Retrospective observational study (two centers, with internal and external test sets)
Abstract
Background and objective The accurate classification of human epidermal growth factor receptor 2 (HER2) status is very important for the diagnosis and treatment of breast cancer. However, the biopsy, the current gold standard for differentiating HER2 status, is invasive and time-consuming. To overcome these drawbacks, a novel deep learning model was developed to differentiate HER2-negative and HER2-positive status in breast cancer solely based on diffusion-weighted imaging (DWI). Materials and methods This retrospective study included 239 women patients confirmed with breast cancer from two local medical centers. A hybrid CNN-Transformer DL model was proposed, which took DWI images (the ADC maps, DWI images with b = 0 s/mm² and b = 800 s/mm²) as inputs and output the classification of HER2-negative and HER2-positive status. Classification by the proposed DL model was quantitatively compared to the classification by the other benchmark DL models and two clinical experts.
Results: Data of the 239 patients (mean age, 49.4 ± 10.0 years) were separated into a training set (n = 156), an internal test set (n = 39), and an external test set (n = 44). On the internal test set, the proposed DL model performed numerically better than the best benchmark DL model (area under the curve [AUC]: 0.93 vs. 0.89; accuracy: 0.90 vs. 0.85). On the external test set, the proposed model also performed numerically better than the best benchmark model (AUC: 0.91 vs. 0.87; accuracy: 0.84 vs. 0.82), and significantly better than the two clinical experts (AUC: 0.91 vs. 0.65 vs. 0.63; accuracy: 0.84 vs. 0.61 vs. 0.57).
Conclusion: This study demonstrates the promise of combining DWI and DL for the classification of HER2 status in breast cancer, and it may potentially serve as a non-invasive adjunct or decision-support tool.
Journal IF-equivalent: 2.7 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)
Reference: Zhang Y, Kan X, Wang J, Peng J, Cai J, Ma Y, et al. Deep learning-based prediction of HER2 status from breast diffusion-weighted MRI. Front Oncol. 2026 Oct 5;16:1863720. doi:10.3389/fonc.2026.1863720.
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