Recent Advances in Artificial Intelligence-Based Approaches for Breast Cancer

A special issue of Current Oncology (ISSN 1718-7729). This special issue belongs to the section "Breast Cancer".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 1067

Editor


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Guest Editor
Department of Oncology, McMaster University, Hamilton, ON L8V 5C2, Canada
Interests: Artificial Intelligence (AI); healthcare; breast cancer

Special Issue Information

Dear Colleagues,

In recent years, many advancements have been made in artificial intelligence (AI)-based research and applications for breast cancer prevention, screening, diagnosis, treatment, survival, and prognosis. This Special Issue of Current Oncology focuses on the diverse applications of AI in breast cancer, covering developments in methodology, evaluation, and research appraisal instigated by breakthroughs in the field.

We welcome submissions (original research articles, reviews, and innovative methodologies) that demonstrate how recent advances in AI are revolutionizing breast cancer research and management, with the potential to improve care for breast cancer patients. Topics of interest include, but are not limited to, the following:

  1. Designing and validating novel multimodal diagnostic, predictive, or prognostic AI models;
  2. The generation and curation of research-ready datasets for breast cancer using AI;
  3. Improving the performance of AI models in external validations;
  4. Appraising the breast cancer research literature utilizing AI;
  5. The benchmarking and comparison of AI models for assistive tasks pertaining to breast cancer care and management in retrospective or prospective studies;
  6. Generative AI applications in breast cancer;
  7. Explainable AI (XAI) methodologies for breast cancer AI models;
  8. Literature reviews (e.g., systematic reviews, scoping reviews, surveys) at the intersection of AI and breast cancer.

We look forward to receiving your contributions.

Dr. Ashirbani Saha
Guest Editor

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Keywords

  • breast cancer
  • artificial intelligence
  • multimodal data
  • breast cancer datasets
  • AI model validation
  • benchmarking
  • literature appraisal
  • explainable AI for breast cancer
  • personalized medicine
  • generative AI for breast cancer

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Published Papers (1 paper)

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Research

16 pages, 2714 KB  
Article
A Parsimonious Ultrasound Radiomics and Ki-67 Model for Estimating MammaPrint Risk Categorization in HR+/HER2− Early Breast Cancer
by Yuanjing Gao, Yanwen Luo, Zihan Niu, Mengyuan Zhou, Mengsu Xiao, Tianjiao Chen, Jia Lu, Yuxin Jiang, Bo Pan and Qingli Zhu
Curr. Oncol. 2026, 33(8), 464; https://doi.org/10.3390/curroncol33080464 - 4 Aug 2026
Viewed by 190
Abstract
The 70-gene signature (70-GS; MammaPrint) assay is useful for prognosis assessment in HR+/HER2− early breast cancer, but limited accessibility motivates development of noninvasive alternatives. We retrospectively enrolled 219 women with preoperative grayscale ultrasound and 70-GS results, including a development cohort (n = [...] Read more.
The 70-gene signature (70-GS; MammaPrint) assay is useful for prognosis assessment in HR+/HER2− early breast cancer, but limited accessibility motivates development of noninvasive alternatives. We retrospectively enrolled 219 women with preoperative grayscale ultrasound and 70-GS results, including a development cohort (n = 125), an internal validation cohort (n = 53), and a temporally independent validation cohort (n = 41). Radiomic features were extracted from manually delineated ROIs using PyRadiomics, and a radiomics score was derived after LASSO selection. Candidate radiomics-only, clinicopathologic-only, and full clinicoradiomic models were explored. To reduce overfitting, we selected a parsimonious model combining the radiomics score and Ki67 as the primary model. The simplified model achieved AUCs of 0.878, 0.816, and 0.831 in the development, internal validation, and temporally independent validation cohorts, respectively. In 1000 bootstrap resamples, the optimism-corrected AUC was 0.872 and the corrected calibration slope was 0.953. Adding the radiomics score to a Ki67-only model significantly improved model fit (likelihood-ratio chi-square = 17.14, df = 1, p < 0.001). An ultrasound radiomics and Ki67 model may provide a noninvasive reference for estimating MammaPrint risk categorization, but it should be considered only as a supportive adjunct and not as a replacement for genomic testing. Full article
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