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Prevention and Early Diagnosis of Breast Cancer

Edited by:

Monica Pernia Marin, MD, Columbia University Irving Medical Center, United States of America
Caren Greenstein, MD, MBA, White Plains Hospital, United States of America
Mary Salvatore, MD, MBA, Columbia University Irving Medical Center, United States of America
 

Submission Status: Open   |   Submission Deadline: 31 August 2026
 

Journal of Translational Medicine is calling for submissions to our Collection on Prevention and Early Diagnosis of Breast Cancer.



Image credit: © anttoniart / stock.adobe.com

New Content ItemThis Collection supports and amplifies research related to SDG 3: Good Health & Well-Being.

About the Collection

The American Cancer Society has shared its breast cancer expectations for 2024 and predicts over 310,000 patients will be diagnosed with invasive breast cancer and over 42,000 women will die because of the disease. The rate of breast cancer continues to increase in the United States with younger women experiencing a greater increase. Sixty-two is the average age for breast cancer diagnosis. 1 in 8 women will suffer from breast cancer at some time in their life1. To improve outcomes early diagnosis and treatment are key and new imaging modalities and blood biomarkers provide hope for increased sensitivity for detection with decreased false positives. The role of artificial intelligence has yet to be fully actualized but there is great hope that it could improve the diagnostic accuracy of available imaging modalities. Cutting-edge research could allow improved risk stratification leading ultimately to cancer prevention. This collection of articles will consider where we are now on the path to achieving these goals and what we will have to do to make them a reality. So many strides have been made and so much more work is yet to be done.

Possible topics for manuscripts submission:  

•AI’s role in increasing the sensitivity and specificity of current imaging techniques.

•The role of serologic biomarkers “liquid biopsy” in the fight against breast cancer.

•Risk reduction strategies for breast cancer.

•Novel imaging techniques (i.e. Fast MRI, elastography, scintimammography).

•Risk stratification and chemoprevention.

Meet the Guest Editors

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Monica Pernia Marin, MD, Columbia University Irving Medical Center, United States of America

Monica Pernia Marin, MD is a triple board-certified physician in the fields of Internal Medicine, Geriatrics, and Hospice & Palliative Medicine. In the summer of 2024, she will begin fellowship training in Neuro-Oncology at Columbia/Cornell University in New York City. She has a passion for education and cancer care in older adults. Her research interests include geriatric oncology, neuro-oncology, and the relationship between aging, fibrosis, and cancer development. 
 

Caren Greenstein MD, MBA, White Plains Hospital, United States of America

Dr. Greenstein is the director of breast intervention at White Plains Hospital (NY), and oversees the breast interventions at the outpatient imaging center in White Plains Center for Ambulatory Medicine. She is locally known as a leader in the field of high-risk screening for breast cancer. Her fields of expertise include breast MRI and of performance all minimally invasive breast procedure types, including vacuum-assisted ultrasound-guided, stereotactic, and MRI guided breast biopsies. Dr. Greenstein has co-authored a textbook on breast MRI—Breast MRI: A Case-Based Approach—released by Lippincott, Williams & Wilkins in 2011.
 

Mary M. Salvatore MD, MBA, Columbia University Irving Medical Center, United States of America

Mary Salvatore MD, MBA is a professor of radiology at Columbia University Medical Center specializing in thoracic radiology. She has previously finished a mammography fellowship and worked as a breast imager for five years. This combination of experiences has provided her with a unique perspective on early diagnosis and risk stratification for breast cancer. She has published multiple articles on the topic of breast cancer on chest CT.

  1. To develop explainable machine learning models that integrate multimodal imaging and pathological biomarkers to predict axillary lymph node metastasis (ALNM) in breast cancer patients and assess their clinical...

    Authors: Yuxi liu, Yunfeng Wu, Qing Xia, Hao He, Haining Yu and Ying Che
    Citation: Journal of Translational Medicine 2025 23:892
    Content type: Research Published on:

  2. Accurate prediction of pathological complete response (pCR) to neoadjuvant chemotherapy has significant clinical utility in the management of breast cancer treatment. Although multimodal deep learning models h...

    Authors: Taishi Nishizawa, Takouhie Maldjian, Zhicheng Jiao and Tim Q. Duong
    Citation: Journal of Translational Medicine 2025 23:774
    Content type: Research Published on:

  3. Breast cancer progression and metastasis involve the action of multiple transcription factors in tumors and in the cells of the tumor microenvironment (TME) and understanding how these transcription factors ar...

    Authors: Stephanie M. Wilk, Kihak Lee, Caitlyn C. Castillo, Mohamed Haloul, Alexa M. Gajda, Virgilia Macias, Elizabeth L. Wiley, Zhengjia Chen, Xinyi Liu, Xiaowei Wang, Maria Sverdlov, Kent F. Hoskins and Ekrem Emrah Er
    Citation: Journal of Translational Medicine 2025 23:599
    Content type: Research Published on:

  4. Biennial mammography screening is well-established for women aged 50 and above, but guidelines for younger women are less clear. Risk-based screening may provide women with key information to make informed dec...

    Authors: Peh Joo Ho, Su-Ann Goh, Serene Si Ning Goh, Jenny Liu, Ying Jia Chew, Nur Khaliesah Mohamed Riza, Han Boon Oh, Chi Hui Chin, Sing Cheer Kwek, Zhi Peng Zhang, Desmond Luan Seng Ong, Swee Tian Quek, Sujith Wijerathne, Jingmei Li, Philip Tsau Choong Iau and Mikael Hartman
    Citation: Journal of Translational Medicine 2025 23:517
    Content type: Research Published on:

  7. Risk-based breast cancer screening offers a more targeted and potentially cost-effective approach in cancer detection compared to age-based screening. This study aims to understand women’s preferences and will...

    Authors: Yi Wang, Peh Joo Ho, Langming Mou and Jingmei Li
    Citation: Journal of Translational Medicine 2025 23:96
    Content type: Research Published on:

Submission Guidelines

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This Collection welcomes submission of original research articles as well as review articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. 

Articles for this Collection should be submitted via our submission system, Editorial Manager. Please select the appropriate Collection title “Prevention and Early Diagnosis of Breast Cancer" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer-review process. The peer-review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.