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Artificial intelligence in breast imaging

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Artificial intelligence (AI) is becoming integrated into many aspects of our day -to -day life, whether its suggestions on movies we should consider, books we may be interested in reading or apparel that may suit our personal taste. In preclinical research, AI provides tools for rapid and robust evaluation of cancer cell and organoid phenotypes or data from small animal imaging. AI is particularly well suited to radiology where it affords opportunities to enhance the speed, accuracy and quality of image interpretation. Rather than eliminating the need for radiologists anytime soon, AI can serve as a valuable adjunct to them allowing resulting in more dependable interpretations of ever more complex technology used in radiology. However, integration of AI to clinical imaging workflows requires careful evaluation of associated ethical, legal, and regulatory challenges.

In this cross-journal collection, we welcome a wide range of articles on AI in breast imaging, including primary research articles, method-based articles, reviews, and perspectives. 

To express your interest to contribute, please contact the Editor-in-Chief of the respective journal:

Breast Cancer Research: Lewis A. Chodosh (chodosh@pennmedicine.upenn.edu)
Journal of Mammary Gland Biology and Neoplasia: Zuzana Koledova (koledova@med.muni.cz)
Breast Cancer Research & Treatment: William J. Gradishar (w-gradishar@northwestern.edu)

Participating journals:
Breast Cancer Research: Submit here

Breast Cancer Research and Treatment: Submit here

Journal of Mammary Gland Biology and Neoplasia: Submit here


  1. The biological phenotype of tumours evolves during neoadjuvant chemotherapy (NAC). Accurate prediction of pathological complete response (pCR) to NAC in the early-stage or posttreatment can optimize treatment ...

    Authors: Lei Wu, Weitao Ye, Yu Liu, Dong Chen, Yuxiang Wang, Yanfen Cui, Zhenhui Li, Pinxiong Li, Zhen Li, Zaiyi Liu, Min Liu, Changhong Liang, Xiaotang Yang, Yu Xie and Ying Wang
    Citation: Breast Cancer Research 2022 24:81
    Content type: Research Published on:

  2. Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than ...

    Authors: Aimilia Gastounioti, Shyam Desai, Vinayak S. Ahluwalia, Emily F. Conant and Despina Kontos
    Citation: Breast Cancer Research 2022 24:14
    Content type: Review Published on:

  3. Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and th...

    Authors: Xiangning Chen, Daniel G. Chen, Zhongming Zhao, Justin M. Balko and Jingchun Chen
    Citation: Breast Cancer Research 2021 23:96
    Content type: Research article Published on: