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Diagnostics
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Advances in Breast Diagnostics
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Advances in Breast Diagnostics

A Special Issue of Diagnostics (ISSN 2075-4418) belonging to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 January 2026) | Viewed by 6844

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
Prof. Dr. Jessica Leung

E-Mail Website
Guest Editor
Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
Interests: multimodality breast imaging; diagnostic imaging
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
Prof. Dr. Judy Yee

E-Mail Website
Guest Editor
Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, USA
Interests: abdominal imaging; colorectal cancer screening

Special Issue Information

Dear Colleagues,

Technological advances such as AI have many potential applications in medicine, with the ability to improve the speed and accuracy of diagnoses and generate prediction models that can help guide treatment and promote precision medicine.  Specifically, machine learning allows the amalgamation and analysis of large amounts of data, enabling more accurate diagnoses and the prediction of outcomes, such as recurrence-free survival, pathologic complete response (pCR), and the prediction of prognosis in breast cancer. This can help to promote more individually tailored treatment strategies with the goal of achieving improved outcomes. Incorporating novel imaging and non-imaging biomarkers in machine learning analyses may further enhance the prediction of outcomes. The goal of this Special Issue is to showcase the latest advancements and emerging trends in the application of AI methods for breast cancer detection and outcome prediction.

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

Prof. Dr. Takouhie Catherine Maldjian
Prof. Dr. Jessica Leung
Dr. Mary M. Salvatore
Prof. Dr. Judy Yee
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 short 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics 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 2600 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

  • artificial intelligence
  • machine learning
  • deep learning
  • breast imaging
  • breast cancer
  • breast cancer prognosis

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Published Papers (4 papers)

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Research

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14 pages, 1758 KB  
Article
Training AI to Improve Distinction of Triple-Negative Invasive Breast Cancer from Cysts and Fibroadenomas on Ultrasound
by Wendie A. Berg, Andriy I. Bandos, Linda H. Larsen, Samantha L. Heller, Regina J. Hooley, Richard S. Ha, Maham Siddique, Jeremy M. Berg, Yuying Cao, R. Chad McClennan and Ajit Jairaj
Diagnostics 2026, 16(9), 1354; https://doi.org/10.3390/diagnostics16091354 - 30 Apr 2026
Viewed by 747
Abstract
Background/Objectives: Circumscribed oval, hypoechoic masses are common on screening breast ultrasound (US), and the vast majority are benign. Triple-receptor negative invasive breast cancer (TNBC) can appear similar, resulting in both human and artificial intelligence (AI) interpretive errors. Purpose: We sought to improve [...] Read more.
Background/Objectives: Circumscribed oval, hypoechoic masses are common on screening breast ultrasound (US), and the vast majority are benign. Triple-receptor negative invasive breast cancer (TNBC) can appear similar, resulting in both human and artificial intelligence (AI) interpretive errors. Purpose: We sought to improve AI performance in distinguishing common benign masses from TNBC through a retrospective model refinement and validation study. Materials and Methods: In an Institutional Review Board-approved HIPAA-compliant protocol, from five academic medical centers, orthogonal ultrasound images of 1771 breast masses 2 cm or smaller were acquired, consisting of cysts, complicated cysts, other benign, and malignancies. Cases were randomized, controlling for lesion class, site, and patient age, with 1446 (including 402, 27.8%, malignancies) used for training and 325 (including 95, 29.2% malignancies) for validation using Koios DS® (decision support, KDS) software version 2.0. A breast imaging radiologist from each center reviewed images and recorded BI-RADS features and assessment. Demographics, symptoms, and pathology or at least one-year follow-up was recorded. The KDS score was evaluated standalone and in combination with BI-RADS using logistic regression and ROC analysis with focus on specificity at sensitivity of 98%. Results: In training, KDS standalone performed comparably to BI-RADS, and significantly improved BI-RADS malignancy risk prediction (p < 0.001). The 98%–sensitivity threshold for combined KDS + BI-RADS was estimated and kept fixed during validation. In validation, KDS standalone performed similar to BI-RADS with AUC = 0.97 (CI: 0.95–0.98) versus 0.95 (p = 0.22), with sensitivity of 98% (93/95, CI: 95–100%) for both and specificity of 70.9% (163/230, CI: 65.0–76.7%) for KDS versus 63.9% for BI-RADS (147/230, p = 0.10). Combining KDS + BIRADS significantly improved overall performance (AUC 0.98, p < 0.001) and specificity (74.4%, 171/230, p < 0.001) while maintaining sensitivity at 98% (93/95). Conclusions: While KDS alone should not replace BI-RADS, when used in combination with BI-RADS, it can significantly improve specificity for highly accurate (98% sensitivity) triaging management of masses representative of those seen on screening US. Full article
(This article belongs to the Special Issue Advances in Breast Diagnostics)
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17 pages, 1287 KB  
Article
Time-Dependent DCE-MRI Radiomics to Predict Response to Neoadjuvant Therapy in Breast Cancer: A Multicenter Study with External Validation
by Giulia Vatteroni, Riccardo Levi, Paola Nardi, Giulia Pruneddu, Elisa Salpietro, Federica Fici, Cinzia Monti, Rubina Manuela Trimboli and Daniela Bernardi
Diagnostics 2026, 16(4), 611; https://doi.org/10.3390/diagnostics16040611 - 19 Feb 2026
Viewed by 1298
Abstract
Background: The accurate prediction of response to neoadjuvant therapy (NAT) is crucial for optimizing breast cancer management. Conventional breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) radiomics typically relies on single post-contrast phases and may not fully capture temporal enhancement patterns related to [...] Read more.
Background: The accurate prediction of response to neoadjuvant therapy (NAT) is crucial for optimizing breast cancer management. Conventional breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) radiomics typically relies on single post-contrast phases and may not fully capture temporal enhancement patterns related to tumor heterogeneity. This study evaluated a machine learning model based on time-dependent radiomic features extracted from pre-treatment DCE-MRI for predicting NAT response in breast cancer patients. Methods: Breast DCE-MRI examinations of women scheduled for NAT, acquired on 1.5 T scanners from three different vendors, were retrospectively collected from two centers. Tumors were automatically segmented on the third post-contrast DCE image using a 3D nnUNet model trained on 30 lesions. All DCE phases were registered to the reference image, and radiomic features were extracted from a consistent tumor region of interest across all phases. Time-dependent radiomic features were computed using linear regression modeling of feature evolution over time. A random forest classifier integrating static and time-dependent radiomic features was developed to predict pathological complete response (pCR), partial response (pPR), and non-response (pNR). Model performance was evaluated using internal validation (Center 1) and an independent external test cohort (Center 2). Results: A total of 212 patients were included (173 from Center 1 and 39 from Center 2), comprising 103 pCR, 103 pPR and 6 pNR cases. Among 759 extracted features, 30 showed significant differences across response groups. Several time-dependent texture features related to intratumoral heterogeneity were significantly associated with pNR. The model achieved AUC values of 0.80, 0.81, and 0.95 in the internal validation cohort and 0.75, 0.74, and 0.86 in the external test cohort for predicting pCR, pPR, and pNR, respectively. Conclusions: Time-dependent radiomic features derived from pre-treatment breast DCE-MRI enable the accurate prediction of response to NAT, with particularly strong performance in identifying non-responders. This approach may support imaging-based risk stratification and contribute to more personalized treatment. Full article
(This article belongs to the Special Issue Advances in Breast Diagnostics)
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Review

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19 pages, 1097 KB  
Review
Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis
by Ziyu Fu, Xiaofei Huo, Andrew B. Jing, Jingfei Ma and Gaiane M. Rauch
Diagnostics 2026, 16(5), 671; https://doi.org/10.3390/diagnostics16050671 - 26 Feb 2026
Viewed by 1367
Abstract
Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype associated with limited targeted treatment options, heterogeneous treatment response, and high risk of early recurrence. Artificial intelligence (AI) has rapidly emerged as a powerful tool to address key clinical challenges in TNBC across [...] Read more.
Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype associated with limited targeted treatment options, heterogeneous treatment response, and high risk of early recurrence. Artificial intelligence (AI) has rapidly emerged as a powerful tool to address key clinical challenges in TNBC across diagnosis, treatment response assessment, and prognosis. Diagnostic and staging challenges persist due to variable imaging features in TNBC and limitations in conventional modalities, increasing the risk of delayed detection. Predicting response to neoadjuvant systemic therapy remains difficult, as patient responses are heterogeneous, and existing clinical markers provide limited early predictive value. Prognostication in TNBC is similarly constrained by the absence of widely used genomic tools and reliance on clinicopathologic factors that incompletely reflect tumor biology. This review summarizes recent advances in AI applications for TNBC across diagnosis, tumor characterization and staging, treatment response prediction, and prognosis, highlighting both emerging opportunities and current limitations in clinical translation. Full article
(This article belongs to the Special Issue Advances in Breast Diagnostics)
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25 pages, 1075 KB  
Review
The Role of Tumor pH in Breast Cancer Imaging: Biology, Diagnostic Applications, and Emerging Techniques
by Dyutika Kantamneni, Saumya Gurbani and Mary Salvatore
Diagnostics 2026, 16(1), 76; https://doi.org/10.3390/diagnostics16010076 - 25 Dec 2025
Cited by 2 | Viewed by 2683
Abstract
Breast cancer screening, while vital for reducing mortality, faces significant limitations in sensitivity and specificity, particularly in dense breasts. Current modalities primarily detect anatomical changes, often missing biologically aggressive tumors at their earliest stages. The altered metabolism of cancer cells establishes a characteristic [...] Read more.
Breast cancer screening, while vital for reducing mortality, faces significant limitations in sensitivity and specificity, particularly in dense breasts. Current modalities primarily detect anatomical changes, often missing biologically aggressive tumors at their earliest stages. The altered metabolism of cancer cells establishes a characteristic inverted pH gradient that drives tumor invasion, metastasis, and treatment resistance. This makes tumor acidity a compelling, functional biomarker for early detection. This review synthesizes the emerging role of pH as a diagnostic biomarker and provides a critical evaluation of advanced imaging techniques for its non-invasive or minimal measurement. We detail the biological underpinnings of tumor acidosis, emphasizing its regulation through glycolytic reprogramming and dysregulated proton transport. Our analysis encompasses a broad spectrum of pH-sensitive imaging modalities, including magnetic resonance methods such as Chemical Exchange Saturation Transfer (CEST) MRI for extracellular pH mapping and multi-nuclear Magnetic Resonance Spectroscopy (MRS) using 1H, 31P, and 19F nuclei to probe various cellular compartments. Furthermore, we examine hyperpolarized 13C MRI for real-time metabolic flux imaging, where metrics such as the lactate-to-pyruvate ratio demonstrate significant predictive value for treatment response. The review also assesses optical and photoacoustic imaging techniques, which offer high sensitivity but are often constrained to superficial tumors. Imaging tumor pH provides a powerful functional window into the earliest metabolic shifts in breast cancer, far preceding macroscopic anatomical changes. The ongoing development and evidence support the role of the pH-sensitive imaging techniques in diagnosis, lesion characterization, and therapy. Additionally, it holds promise for supplementing breast cancer screening by enabling earlier, more specific detection and personalized risk stratification, ultimately aiming to improve patient outcomes. Full article
(This article belongs to the Special Issue Advances in Breast Diagnostics)
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