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Cancers
Special Issues
Updates on Breast Cancer Interventional and Diagnostic Imaging
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Updates on Breast Cancer Interventional and Diagnostic Imaging

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 563

Special Issue Editor

Dr. Jacopo Nori

E-Mail Website
Guest Editor
Diagnostic Senology Unit, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
Interests: breast ultrasound; mammography; breast MRI; contrast-enhanced mammography; breast tomosynthesis; percutaneous ablation techniques of breast lesions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue highlights the latest advancements and research in breast cancer interventional and diagnostic imaging. It includes cutting-edge studies on the development and application of new imaging techniques, innovations in minimally invasive procedures, and enhanced diagnostic tools aimed at improving early detection and treatment of breast cancer.

This Special Issue also explores emerging technologies in breast cancer imaging, offering insights into their potential to revolutionize clinical practice. Each article contributes to a deeper understanding of how these advancements can lead to more accurate diagnoses, personalized treatment plans, and ultimately, better patient outcomes.

Dr. Jacopo Nori
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 screening
  • digital breast tomosynthesis (DBT)
  • MRI
  • genetic screening
  • risk assessment models
  • artificial intelligence (AI)
  • contrast-enhanced mammography
  • dense breast tissue
  • screening guidelines
  • overdiagnosis and overtreatment

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

24 pages, 25306 KiB  
Article
Understanding the Impact of Deep Learning Model Parameters on Breast Cancer Histopathological Classification Using ANOVA
by Nerea Hernandez, Francisco Carrillo-Perez, Francisco M. Ortuño, Ignacio Rojas and Olga Valenzuela
Cancers 2025, 17(9), 1425; https://doi.org/10.3390/cancers17091425 - 24 Apr 2025
Viewed by 214
Abstract
Artificial intelligence (AI) has the potential to enhance clinical practice, particularly in the early and accurate diagnosis of diseases like breast cancer. However, for AI models to be effective in medical settings, they must not only be accurate but also interpretable and reliable. [...] Read more.
Artificial intelligence (AI) has the potential to enhance clinical practice, particularly in the early and accurate diagnosis of diseases like breast cancer. However, for AI models to be effective in medical settings, they must not only be accurate but also interpretable and reliable. This study aims to analyze how variations in different model parameters affect the performance of a weakly supervised deep learning model used for breast cancer detection. Methods: In this work, we apply Analysis of Variance (ANOVA) to investigate how changes in different parameters impact the performance of the deep learning model. The model is built using attention mechanisms, which both perform classification and identify the most relevant regions in medical images, improving the interpretability of the model. ANOVA is used to determine the significance of each parameter in influencing the model’s outcome, offering insights into the specific factors that drive its decision-making. Results: Our analysis reveals that certain parameters significantly affect the model’s performance, with some configurations showing higher sensitivity and specificity than others. By using ANOVA, we identify the key factors that influence the model’s ability to classify images correctly. This approach allows for a deeper understanding of how the model works and highlights areas where improvements can be made to enhance its reliability in clinical practice. Conclusions: The study demonstrates that applying ANOVA to deep learning models in medical applications provides valuable insights into the parameters that influence performance. This analysis helps make AI models more interpretable and trustworthy, which is crucial for their adoption in real-world medical environments like breast cancer detection. Understanding these factors enables the development of more transparent and efficient AI tools for clinical use. Full article
(This article belongs to the Special Issue Updates on Breast Cancer Interventional and Diagnostic Imaging)
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