Application of Artificial Intelligence in Breast Cancer Diagnosis, Prognosis, and Assessing Responses to Treatment
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: closed (15 February 2025) | Viewed by 3790
Special Issue Editors
Interests: machine learning; nuclear imaging; cancer
2. Center for Innovative Biomedicine and Biotechnology (CIBB), Coimbra Institute for Clinical and Biomedical Research (iCBR), Coimbra, Portugal
Interests: clinical and translational research on gynecology
Special Issue Information
Dear Colleagues,
Breast cancer continues to present significant challenges due to its multifaceted nature, which is shaped by various factors such as clinical parameters, genetic predispositions, and environmental influences. Despite advancements in screening modalities and therapeutic interventions, early detection remains pivotal in enhancing patients’ prognoses. In this regard, the integration of genetic, histological, and imaging-based biomarkers into artificial intelligence (AI) models has shown great potential to enhance breast cancer diagnosis, patient stratification, predictions of responses to treatment, and assessments of early treatment response.
We are pleased to invite you to contribute to this Special Issue. We aim to delve into the intricate interplay between different AI models and biomarkers from diverse data sources.
This Special Issue has the following aims:
- To shed light on the utility of AI algorithms in harnessing varied datasets—encompassing clinical records, genetic markers, and imaging biomarkers, among others—in order to refine risk prediction models for breast cancer. Such endeavors may facilitate initiatives with the ultimate goal of early detection.
- To explore the application of AI-driven analyses in deciphering the complexities of tumor microenvironments and molecular signatures, thereby enabling tailored treatment selection and prognostication of a given treatment’s efficacy.
- To address persistent challenges presented by factors like breast tissue density and false positives in screening programs; this will be achieved by exploring how AI methodologies can mitigate these challenges, thereby enhancing screening accuracy and resource optimization.
- To assess responses to therapy using AI as soon as possible, enabling faster and more effective personalization of treatment.
We encourage the submission of original research articles and reviews related to breast cancer to this Special Issue. Research areas may include (but are not limited to) the following:
- Pioneering and validating novel AI algorithms for breast cancer risk assessment;
- Harmonizing AI with multi-source data ecosystems;
- Unveiling the enigmatic tumor microenvironment through AI-driven analyses;
- Predicting responses to tailored therapies through AI;
- Embracing the dawn of explainable AI (XAI) in breast cancer diagnosis;
- Quantitative medical imaging and radiomics;
- AI-based image enhancement technologies for breast cancer imaging.
We look forward to receiving your contributions.
Dr. Francisco Caramelo
Dr. Maria João Carvalho
Dr. Francisco P. M. Oliveira
Guest Editors
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
- artificial intelligence
- image processing
- diagnosis
- risk stratification
- prognosis
- treatment response assessment
- personalized medicine
- early detection
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