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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. Early prediction of pathological complete response (pCR) is important for deciding appropriate treatment strategies for patients. In this study, we aimed to quantify the dynamic characteristics of dynamic cont...

    Authors: Xinyu Zhang, Xinzhi Teng, Jiang Zhang, Qingpei Lai and Jing Cai
    Citation: Breast Cancer Research 2024 26:77
  2. Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screenin...

    Authors: Mi-ri Kwon, Yoosoo Chang, Soo-Youn Ham, Yoosun Cho, Eun Young Kim, Jeonggyu Kang, Eun Kyung Park, Ki Hwan Kim, Minjeong Kim, Tae Soo Kim, Hyeonsoo Lee, Ria Kwon, Ga-Young Lim, Hye Rin Choi, JunHyeok Choi, Shin Ho Kook…
    Citation: Breast Cancer Research 2024 26:68
  3. Accurate classification of breast cancer molecular subtypes is crucial in determining treatment strategies and predicting clinical outcomes. This classification largely depends on the assessment of human epide...

    Authors: Minsun Jung, Seung Geun Song, Soo Ick Cho, Sangwon Shin, Taebum Lee, Wonkyung Jung, Hajin Lee, Jiyoung Park, Sanghoon Song, Gahee Park, Heon Song, Seonwook Park, Jinhee Lee, Mingu Kang, Jongchan Park, Sergio Pereira…
    Citation: Breast Cancer Research 2024 26:31
  4. There is increasing evidence that artificial intelligence (AI) breast cancer risk evaluation tools using digital mammograms are highly informative for 1–6 years following a negative screening examination. We h...

    Authors: Ruggiero Santeramo, Celeste Damiani, Jiefei Wei, Giovanni Montana and Adam R. Brentnall
    Citation: Breast Cancer Research 2024 26:25
  5. Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30–40% of TNBC patients treated with neoadjuvant chemotherapy (N...

    Authors: Timothy B. Fisher, Geetanjali Saini, T. S. Rekha, Jayashree Krishnamurthy, Shristi Bhattarai, Grace Callagy, Mark Webber, Emiel A. M. Janssen, Jun Kong and Ritu Aneja
    Citation: Breast Cancer Research 2024 26:12
  6. Generalizability of predictive models for pathological complete response (pCR) and overall survival (OS) in breast cancer patients requires diverse datasets. This study employed four machine learning models to...

    Authors: Kevin Dell’Aquila, Abhinav Vadlamani, Takouhie Maldjian, Susan Fineberg, Anna Eligulashvili, Julie Chung, Richard Adam, Laura Hodges, Wei Hou, Della Makower and Tim Q. Duong
    Citation: Breast Cancer Research 2024 26:7
  7. Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent nee...

    Authors: Witali Aswolinskiy, Enrico Munari, Hugo M. Horlings, Lennart Mulder, Giuseppe Bogina, Joyce Sanders, Yat-Hee Liu, Alexandra W. van den Belt-Dusebout, Leslie Tessier, Maschenka Balkenhol, Michelle Stegeman, Jeffrey Hoven, Jelle Wesseling, Jeroen van der Laak, Esther H. Lips and Francesco Ciompi
    Citation: Breast Cancer Research 2023 25:142
  8. Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to dev...

    Authors: Yunfang Yu, Wei Ren, Zifan He, Yongjian Chen, Yujie Tan, Luhui Mao, Wenhao Ouyang, Nian Lu, Jie Ouyang, Kai Chen, Chenchen Li, Rong Zhang, Zhuo Wu, Fengxi Su, Zehua Wang, Qiugen Hu…
    Citation: Breast Cancer Research 2023 25:132
  9. Breast density is strongly associated with breast cancer risk. Fully automated quantitative density assessment methods have recently been developed that could facilitate large-scale studies, although data on a...

    Authors: Laurel A. Habel, Stacey E. Alexeeff, Ninah Achacoso, Vignesh A. Arasu, Aimilia Gastounioti, Lawrence Gerstley, Robert J. Klein, Rhea Y. Liang, Jafi A. Lipson, Walter Mankowski, Laurie R. Margolies, Joseph H. Rothstein, Daniel L. Rubin, Li Shen, Adriana Sistig, Xiaoyu Song…
    Citation: Breast Cancer Research 2023 25:92
  10. Deep learning analysis of radiological images has the potential to improve diagnostic accuracy of breast cancer, ultimately leading to better patient outcomes. This paper systematically reviewed the current li...

    Authors: Richard Adam, Kevin Dell’Aquila, Laura Hodges, Takouhie Maldjian and Tim Q. Duong
    Citation: Breast Cancer Research 2023 25:87
  11. Generalizable population-based studies are unable to account for individual tumor heterogeneity that contributes to variability in a patient’s response to physician-chosen therapy. Although molecular character...

    Authors: Joseph R. Peterson, John A. Cole, John R. Pfeiffer, Gregory H. Norris, Yuhan Zhang, Dorys Lopez-Ramos, Tushar Pandey, Matthew Biancalana, Hope R. Esslinger, Anuja K. Antony and Vinita Takiar
    Citation: Breast Cancer Research 2023 25:54
  12. Breast cancer (BC) grading plays a critical role in patient management despite the considerable inter- and intra-observer variability, highlighting the need for decision support tools to improve reproducibilit...

    Authors: Gerardo Fernandez, Marcel Prastawa, Abishek Sainath Madduri, Richard Scott, Bahram Marami, Nina Shpalensky, Krystal Cascetta, Mary Sawyer, Monica Chan, Giovanni Koll, Alexander Shtabsky, Aaron Feliz, Thomas Hansen, Brandon Veremis, Carlos Cordon-Cardo, Jack Zeineh…
    Citation: Breast Cancer Research 2022 24:93
  13. 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
  14. Breast terminal duct lobular units (TDLUs), the source of most breast cancer (BC) precursors, are shaped by age-related involution, a gradual process, and postpartum involution (PPI), a dramatic inflammatory p...

    Authors: Joshua Ogony, Thomas de Bel, Derek C. Radisky, Jennifer Kachergus, E. Aubrey Thompson, Amy C. Degnim, Kathryn J. Ruddy, Tracy Hilton, Melody Stallings-Mann, Celine Vachon, Tanya L. Hoskin, Michael G. Heckman, Robert A. Vierkant, Launia J. White, Raymond M. Moore, Jodi Carter…
    Citation: Breast Cancer Research 2022 24:45
  15. 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
  16. 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