AI and Cancer Research ── Breast ── 2026-10-11
Breast cancer: treatment, trials and AI
- Abstract
Preoperative evaluation of sentinel lymph node (SLN) metastasis facilitates individualized axillary management for patients with invasive breast cancer (IBC). This study aimed to develop and externally validate a deep learning-derived ultrasound radiomics model to predict SLN metastasis. In this retrospective two-center study, 246 pathologically confirmed IBC patients were enrolled. 194 patients from Center A were split into training ( n = 155) and internal test ( n = 39) cohorts, while 52 patients from Center B served as the external validation cohort. Deep features were extracted from preoperative ultrasound images via ResNet50. After feature selection within the training cohort, a gradient boosting decision tree classifier was constructed. Model discrimination was assessed using receiver-operating characteristic analysis, and decision-curve analysis was applied for exploratory net-benefit evaluation. Twenty-two deep learning-derived features were retained. The model achieved areas under the receiver operating characteristic curve (AUC) of 0.734 (95% CI, 0.655–0.813), 0.761 (95% CI, 0.601–0.920), and 0.742 (95% CI, 0.598–0.886) in the training, internal test, and external validation cohorts, respectively. In the external cohort, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.769, 0.650, 0.844, 0.722, and 0.794, respectively. Decision curves were exploratory and do not establish clinical utility. The model showed moderate discrimination for SLN metastasis, with similar AUC point estimates but wide confidence intervals across cohorts; it provides preliminary discrimination and risk ranking only and is not currently clinically actionable. Prospective validation in larger cohorts is required before any clinical use.
Journal IF-equivalent: 3.8 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: He K, Qiu Y, Zeng B, Huang Z, Lin J, Chen J, et al. Ultrasound-based deep learning-derived radiomics for preoperative prediction of sentinel lymph node metastasis in invasive breast cancer: a two-center retrospective study. BMC Med Imaging. 2026 Oct 9 [Epub ahead of print]. doi:10.1186/s12880-026-02919-7.Checked: Abstract only - Abstract
Accurate assessment of HER2 status in breast cancer has been critical for guiding therapy and has become even more important with the emergence of antibody–drug conjugates, now also indicated in HER2-low tumors. However, inter- and intraobserver variability limits the reproducibility of HER2 IHC scoring among pathologists. Artificial intelligence (AI) models offer potential to standardize and improve diagnostic accuracy and bring new insights into current practices shortcomings. We conducted a study recruiting generalist and specialist pathologists from Rede D’Or centers across Brazil to assess digitized HER2 IHC whole slide images. The same images were presented for the pathologists with an interval of one month and to the AIM-HER2 (PathAI ®, Boston, MA) AI model. Intra- and interobserver agreement, as well as agreement with AI, were measured across 126 breast cancer samples. The association between sample features and agreement metrics was also analyzed using AI spatial breakdown data. Among pathologists, the median intraobserver agreement was 66.67%, and the median agreement with AI was 60.8%. Median interobserver agreement was 67.65%, with high agreement (> 85%) in 25.4% of samples. Significant positive correlations were observed among all agreement metrics. Samples with lower intra-sample heterogeneity, as determined by AI spatial breakdown scores, were associated with higher agreement levels. Our findings highlight significant variability in HER2 IHC scoring among pathologists. We found that AI-assessed intra-sample heterogeneity is correlated with a lower agreement rate among pathologists. We believe this shows a potential limitation of current scoring practices and that AI models such as AIM-HER2 can have a role in increasing reproducibility of analysis by indicating the samples that are more likely to result in diagnostic discordance between evaluators.
Journal IF-equivalent: 3.9 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Ferrari P, Petaccia de Macedo M, Werneck da Cunha I, Soares-Souza GB, Soares FA, Breast Cancer Cooperative Study Group, et al. Artificial Intelligence Model’s assessment of intra-sample heterogeneity of HER2 IHC in breast cancer is related to interobserver and intraobserver agreement among pathologists. BMC Cancer. 2026 Oct 9 [Epub ahead of print]. doi:10.1186/s12885-026-17049-0.Checked: Abstract only