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Cancers
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Breast Cancer Research and Treatment
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Breast Cancer Research and Treatment

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Tumor Microenvironment".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 688

Special Issue Editor

Dr. Huaitian Liu

E-Mail Website
Guest Editor
Center for Cancer Research (CCR), National Cancer Institute (NCI), Bethesda, MD 20892, USA
Interests: cancer genomics; genetics; epidemiology; bioinformatics

Special Issue Information

Dear Colleagues,

Breast cancer is classified into four molecular subtypes: luminal A, luminal B, triple-negative breast cancer (TNBC), and human epidermal growth factor receptor type 2 (HER2)-positive. The tumor microenvironment plays a critical role in modulating the aggressiveness and differentiation of malignant cells. Advances in single-cell genomics have transformed our ability to analyze the cellular, transcriptional, and epigenetic heterogeneity of human tumors with unprecedented resolution. This Special Issue will highlight recent progress in our understanding of the tumor and immune microenvironments, as well as identifying relevant biomarkers and therapeutic targets, paving the way for breast cancer to become a curable disease.

Dr. Huaitian Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • breast cancer
  • tumor microenvironment
  • immune microenvironment
  • intratumoral heterogeneity
  • immunotherapy

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Published Papers (1 paper)

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Research

14 pages, 1983 KB  
Article
Federated Learning Architecture for 3D Breast Cancer Image Classification
by Amel Ali Alhussan, Wiem Nhidi, Imen Filali, Faten Benhmida and Ridha Ejbali
Cancers 2025, 17(21), 3450; https://doi.org/10.3390/cancers17213450 - 28 Oct 2025
Abstract
Backgrouds: Breast cancer remains a major global health challenge, with early diagnosis playing a crucial role in improving patient survival rates. Among the available diagnostic techniques, mammography is widely employed for early detection. However, its effectiveness is often constrained by the complexity of [...] Read more.
Backgrouds: Breast cancer remains a major global health challenge, with early diagnosis playing a crucial role in improving patient survival rates. Among the available diagnostic techniques, mammography is widely employed for early detection. However, its effectiveness is often constrained by the complexity of image interpretation, which makes automated detection methods increasingly vital. Methods: In this study, we propose an advanced approach that leverages 3D mammographic imaging and integrates Federated Learning (FL) to enable decentralized, privacy-preserving model training across multiple institutions. To evaluate the effectiveness of this approach, we assess various machine learning models, including Convolutional Neural Networks (CNNs), Transfer Learning architectures (VGG16, VGG19, ResNet50), and AutoEncoders (AEs), using 3D mammographic data. Results: Our results indicate that the CNN model achieves an accuracy of 97.30%, which improves slightly to 97.37% when the model is combined with Federated Learning, highlighting both the predictive performance and privacy-preserving advantages of our method. In contrast, Transfer Learning models and AutoEncoders exhibit lower accuracies that range from 48.83% to 89.24%, revealing their limitations in the context of this specific task. Conclusions: These findings underscore the effectiveness of the CNN-FL framework as a robust tool for breast cancer detection, showing that this approach offers a promising balance between diagnostic accuracy and data security—two critical factors in medical imaging. Full article
(This article belongs to the Special Issue Breast Cancer Research and Treatment)
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