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Advances in Radiology for the Detection, Diagnosis, and Management of Breast Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1126

Special Issue Editors

Prof. Dr. Takouhie Catherine Maldjian

E-Mail Website
Guest Editor
Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY 10467, USA
Interests: breast imaging; AI/machine learning in prediction of breast cancer prognosis and pathological complete response; multiparametric and longitudinal MRI in multimodal prediction models for the prediction of breast cancer prognosis and pCR; diagnostic radiology
Special Issues, Collections and Topics in MDPI journals
Dr. Mary M. Salvatore

E-Mail Website
Guest Editor
Department of Radiology, Jacobi Medical Center, Albert Einstein College of Medicine, Bronx, NY 10461, USA
Interests: lung fibrosis; lung cancer; pulmonary fibrosis; radiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Breast cancer is the most frequently diagnosed cancer in women globally. Radiology is integral to early detection, accurate diagnosis, and monitoring treatment response. With emerging technologies such as contrast-enhanced mammography, breast MRI, ultrasound elastography, and AI-driven diagnostics, the landscape of breast imaging is rapidly evolving.

This Special Issue invites original research, reviews, and clinical insights focused on advancing radiologic approaches in breast cancer care. We aim to highlight innovations that enhance diagnostic accuracy, personalize screening, assess neoadjuvant therapy response, and support long-term surveillance.

Submissions may include original studies and reviews.

Topics of interest include, but are not limited to the following:

  • AI and radiomics in breast imaging;
  • Contrast-enhanced and tomographic mammography;
  • Breast MRI and elastography;
  • Image-guided biopsy techniques;
  • Radiologic evaluation of treatment response;
  • Imaging in dense breast tissue and male breast cancer.

We are pleased to invite you to submit your work to this Special Issue. We welcome original articles and review articles and look forward to receiving your contributions.

Prof. Dr. Takouhie Catherine Maldjian
Dr. Mary M. Salvatore
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 250 words) can be sent to the Editorial Office for assessment.

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
  • radiology
  • breast imaging
  • mammography
  • MRI
  • ultrasound
  • AI
  • radiomics
  • diagnosis
  • treatment response
  • image-guided biopsy

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

Published Papers (3 papers)

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Research

14 pages, 4302 KB  
Article
Performance of an Artificial Intelligence Support System on Screening Mammography Cases Proceeding to Stereotactic Biopsy
by Anandita Mathur, Colleen McNally, Arielle Sasson, Nicholas Thoreson, Sadaf Sahraian, David S. Mendelson and Laurie R. Margolies
Cancers 2025, 17(23), 3878; https://doi.org/10.3390/cancers17233878 - 4 Dec 2025
Viewed by 195
Abstract
Background/Objective: The objective was to evaluate the standalone performance of an AI system, Transpara 1.7.1 (ScreenPoint Medical), in screening mammography cases proceeding to stereotactic biopsy using histopathological results as ground truth. Methods: This retrospective study included 202 asymptomatic female patients (mean age: 57.8 [...] Read more.
Background/Objective: The objective was to evaluate the standalone performance of an AI system, Transpara 1.7.1 (ScreenPoint Medical), in screening mammography cases proceeding to stereotactic biopsy using histopathological results as ground truth. Methods: This retrospective study included 202 asymptomatic female patients (mean age: 57.8 years) who underwent stereotactic biopsy at a multicenter academic institution between October 2022 and September 2023 with a preceding screening mammogram within 14 months. Transpara AI risk scores were compared to pathology results (benign versus malignant). Performance metrics for AI including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC) were calculated. Results: Transpara AI classified 20 of 39 malignant findings (51%) as elevated risk compared with 50 of 211 total findings (24%). AI score was positively correlated with malignancy (r = 0.29, p < 0.001). Sensitivity for detecting malignancy (classifying as intermediate or elevated risk) was 94.9% (95% CI: 81.4–94.1), specificity was 24.4% (95% CI: 18.3–31.7), PPV was 22.2% (95% CI: 16.3–29.4), and NPV was 95.5% (95% CI: 83.3–99.2). Transpara had fair performance in detecting breast cancer with AUC 0.73 (95% CI: 0.63–0.82). Conclusions: Transpara AI is a useful screening mammography triage tool. Given its high sensitivity and high negative predictive value, AI may be used to guide radiologists in making biopsy or follow up recommendations. However, the high false-positive rate and presence of two false negatives underscore the need for radiologists to use caution and clinical expertise when interpreting AI results. Full article
(This article belongs to the Special Issue Advances in Radiology for the Detection, Diagnosis, and Management of Breast Cancer)
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22 pages, 1949 KB  
Article
Radiomics Analysis of QUS Spectral Parametric Images for Predicting the Risk of Breast Cancer Recurrence
by Laurentius Oscar Osapoetra, Graham Dinniwell, Maria Lourdes Anzola Pena, David Alberico, Lakshmanan Sannachi and Gregory J. Czarnota
Cancers 2025, 17(23), 3810; https://doi.org/10.3390/cancers17233810 - 28 Nov 2025
Viewed by 214
Abstract
Background/Objectives: To evaluate the ability of radiomics analysis of QUS spectral parametric imaging to non-invasively differentiate intermediate-to-high-risk from low-risk Oncotype DXTM Recurrence Score (ODXRS). Methods: This prospective study included 31 participants (21 intermediate-to-high-risk ODXRS (median age, 56 years [IQR: 49–68 years]) and [...] Read more.
Background/Objectives: To evaluate the ability of radiomics analysis of QUS spectral parametric imaging to non-invasively differentiate intermediate-to-high-risk from low-risk Oncotype DXTM Recurrence Score (ODXRS). Methods: This prospective study included 31 participants (21 intermediate-to-high-risk ODXRS (median age, 56 years [IQR: 49–68 years]) and 10 low-risk ODXRS (median age, 52 years [IQR: 48–58 years])) presenting with ER+ HER2− invasive breast masses acquired between September 2015 and August 2024. Quantitative ultrasound (QUS) spectroscopy produced five spectral maps, from which radiomics features (including statistical, texture, and morphological measures) were extracted from the tumor core and a 5 mm margin. The ground truth label was determined from thresholding the ODXRS. A multivariate predictive model was developed to differentiate intermediate-to-high-risk ODXRS from low-risk ODXRS, with performance assessed via nested leave-one-out cross-validation (LOOCV). Results: A nested leave-one-out cross-validation (LOOCV) analysis demonstrated the generalization performance of a four-feature model. The support vector machine (SVM-RBF) classifier achieved 86% recall, 100% specificity, 93% balanced accuracy, and an area under the receiver operating characteristic curve (AUROC) of 0.95 (CI = 0.88–1.00) in identifying intermediate-to-high-risk versus low-risk ODXRS. Conclusions: The preliminary results suggest the potential radiomics-based model of ODXRS in predicting the risks of recurrence. The results warrant further investigation on a larger cohort. This framework can be a useful surrogate for participants for whom ODX testing is neither affordable nor available. Full article
(This article belongs to the Special Issue Advances in Radiology for the Detection, Diagnosis, and Management of Breast Cancer)
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16 pages, 2443 KB  
Article
Suspicion for Sarcoma: Clinical Presentation, Multi-Modality Imaging Evaluation, and Ultrasound Artificial Intelligence-Based Decision Support
by Nikki A. Mehran, Emily Rooney, Harsh Shah, Tamar Gomolin, Nebras Zeizafoun, Dayna Williams, Laurie R. Margolies and Christine Chen
Cancers 2025, 17(22), 3626; https://doi.org/10.3390/cancers17223626 - 11 Nov 2025
Viewed by 361
Abstract
Background/Objective: This study aims to better characterize the clinical presentation, histology, and imaging features of breast sarcomas on mammography, ultrasound, and MRI, in addition to analyzing the effectiveness of AI DS in detecting breast sarcomas. Methods: A retrospective review from 2008–2024 [...] Read more.
Background/Objective: This study aims to better characterize the clinical presentation, histology, and imaging features of breast sarcomas on mammography, ultrasound, and MRI, in addition to analyzing the effectiveness of AI DS in detecting breast sarcomas. Methods: A retrospective review from 2008–2024 yielded 18 patients with histologically proven breast sarcomas with imaging available for review. Mammography was available for 13 lesions, ultrasound for 19 lesions, and MRI for 9 lesions. Imaging features were classified according to the BI-RADS 5th edition lexicon. Images were reviewed by two radiologists, and consensus was obtained regarding imaging features. AI DS was retrospectively applied to the breast masses identified on ultrasound. Data analysis was performed using descriptive statistics. Results: 17 females and 1 male were included in this study. Mammographic findings varied from solitary masses (3/13 [23.1%]), asymmetries (3/13 [23.1%]), architectural distortion (1/13 [7.7%]), skin thickening (3/13 [23.1%]), focal asymmetry with calcifications (1/13 [7.7%]), or no suspicious findings (2/13 [15.4%]). Sonography often revealed masses with an irregular shape (13/16 [81.2%]), non-circumscribed margins (15/16 [93.7%]), hypoechoic echo pattern (10/16 [62.5%]), and vascular flow (12/16 [75%]). MRI showed heterogeneously enhancing masses (6/9 [66.7%]) or isolated skin enhancement (3/9 [33.3%]). AI DS analyzed 16 masses on ultrasound and identified 15 (93.8%) as suspicious. Conclusions: Breast sarcomas had a variable appearance on breast imaging, ranging from a solitary mass to isolated skin findings. Awareness of how breast sarcomas can present across imaging modalities while using AI DS as an aid may help radiologists in making the correct diagnosis of this rare and aggressive disease. Full article
(This article belongs to the Special Issue Advances in Radiology for the Detection, Diagnosis, and Management of Breast Cancer)
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