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Diagnostics
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Frontline of Breast Imaging
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Frontline of Breast Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 7019

Special Issue Editor

Prof. Dr. Gary J. Whitman

E-Mail Website
Guest Editor
Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Interests: digital mammography (screening, diagnosis, tomosynthesis, and intervention); breast ultrasound; breast MRI; high-risk screening; high-risk lesions; unusual breast lesions; monitoring response to therapy; inflammatory breast cancer

Special Issue Information

Dear Colleagues,

This issue will explore recent developments in breast imaging, including detection and diagnosis of breast cancer, interventional procedures, and monitoring response to breast cancer treatment. The focus will be on practical, multimodality modern breast imaging, including anatomical and functional modalities. This issue aims to provide an update on mammography, breast ultrasound, and breast MRI, as well as other modalities, in an effort to provide information on efficacy and efficiency in breast imaging. Emphasis will be placed on reviewing developments in the last several years, including the time of the COVID-19 pandemic.

We look forward to receiving your contributions.

Prof. Dr. Gary J. Whitman
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 short 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. 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

  • breast imaging
  • breast ultrasound
  • mammography
  • digital breast tomosynthesis
  • digital mammography
  • contrast-enhanced mammography
  • breast MRI
  • molecular breast imaging
  • imaging-guided interventional breast procedures

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

Published Papers (4 papers)

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Research

12 pages, 2523 KiB  
Article
Stepwise Implementation of 2D Synthesized Screening Mammography and Its Effect on Stereotactic Biopsy of Microcalcifications
by Karen E. Gerlach, Kanchan Ashok Phalak, Ethan O. Cohen, Kiran N. Chang, Roland Bassett and Gary J. Whitman
Diagnostics 2023, 13(13), 2232; https://doi.org/10.3390/diagnostics13132232 - 30 Jun 2023
Cited by 1 | Viewed by 1159
Abstract
Rationale and Objectives: Information evaluating the efficacy of 2D synthesized mammography (2Ds) reconstructions in microcalcification detection is limited. This study used stereotactic biopsy data for microcalcifications to evaluate the stepwise implementation of 2Ds in screening mammography. The study aim was to identify whether [...] Read more.
Rationale and Objectives: Information evaluating the efficacy of 2D synthesized mammography (2Ds) reconstructions in microcalcification detection is limited. This study used stereotactic biopsy data for microcalcifications to evaluate the stepwise implementation of 2Ds in screening mammography. The study aim was to identify whether 2Ds + digital breast tomosynthesis (DBT) is non-inferior to 2D digital mammography (2DM) + 2Ds + DBT, 2DM + DBT, and 2DM in identifying microcalcifications undergoing further diagnostic imaging and stereotactic biopsy. Materials and Methods: Retrospective stereotactic biopsy data were extracted following 151,736 screening mammograms of healthy women (average age, 56.3 years; range, 30–89 years), performed between 2012 and 2019. The stereotactic biopsy data were separated into 2DM, 2DM + DBT, 2DM + 2Ds + DBT, and 2Ds + DBT arms and examined using Fisher’s exact test to compare the detection rates of all cancers, invasive cancers, DCIS, and ADH between modalities for patients undergoing stereotactic biopsy of microcalcifications. Results: No statistical significance in cancer detection was seen for 2Ds + DBT among those calcifications that underwent stereotactic biopsy when comparing the 2Ds + DBT to 2DM, 2DM + DBT, and 2DM + 2Ds + DBT imaging combinations. Conclusion: These data suggest that 2Ds + DBT is non-inferior to 2DM + DBT in detecting microcalcifications that will undergo stereotactic biopsy. Full article
(This article belongs to the Special Issue Frontline of Breast Imaging)
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20 pages, 4940 KiB  
Article
Correlated-Weighted Statistically Modeled Contourlet and Curvelet Coefficient Image-Based Breast Tumor Classification Using Deep Learning
by Shahriar M. Kabir and Mohammed I. H. Bhuiyan
Diagnostics 2023, 13(1), 69; https://doi.org/10.3390/diagnostics13010069 - 26 Dec 2022
Cited by 5 | Viewed by 1822
Abstract
Deep learning-based automatic classification of breast tumors using parametric imaging techniques from ultrasound (US) B-mode images is still an exciting research area. The Rician inverse Gaussian (RiIG) distribution is currently emerging as an appropriate example of statistical modeling. This study presents a new [...] Read more.
Deep learning-based automatic classification of breast tumors using parametric imaging techniques from ultrasound (US) B-mode images is still an exciting research area. The Rician inverse Gaussian (RiIG) distribution is currently emerging as an appropriate example of statistical modeling. This study presents a new approach of correlated-weighted contourlet-transformed RiIG (CWCtr-RiIG) and curvelet-transformed RiIG (CWCrv-RiIG) image-based deep convolutional neural network (CNN) architecture for breast tumor classification from B-mode ultrasound images. A comparative study with other statistical models, such as Nakagami and normal inverse Gaussian (NIG) distributions, is also experienced here. The weighted entitled here is for weighting the contourlet and curvelet sub-band coefficient images by correlation with their corresponding RiIG statistically modeled images. By taking into account three freely accessible datasets (Mendeley, UDIAT, and BUSI), it is demonstrated that the proposed approach can provide more than 98 percent accuracy, sensitivity, specificity, NPV, and PPV values using the CWCtr-RiIG images. On the same datasets, the suggested method offers superior classification performance to several other existing strategies. Full article
(This article belongs to the Special Issue Frontline of Breast Imaging)
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10 pages, 4716 KiB  
Article
Prospective Evaluation of Ultrasound in a Novel Position with MRI Virtual Navigation for MRI-Detected Only Breast Lesions: A Pilot Study of a More Efficient and Economical Method
by Ruixiang Qi, Jianhua Fang, Luoxi Zhu, Yanna Shan, Wei Wang, Chenke Xu and Lingyun Bao
Diagnostics 2023, 13(1), 29; https://doi.org/10.3390/diagnostics13010029 - 22 Dec 2022
Viewed by 1467
Abstract
The aim of this study was to evaluate the clinical utility of ultrasound (US) with magnetic resonance imaging (MRI) virtual navigation in a novel prone position for MRI-detected incidental breast lesions. Between June 2016 and June 2020, 30 consecutive patients with 33 additional [...] Read more.
The aim of this study was to evaluate the clinical utility of ultrasound (US) with magnetic resonance imaging (MRI) virtual navigation in a novel prone position for MRI-detected incidental breast lesions. Between June 2016 and June 2020, 30 consecutive patients with 33 additional Breast Imaging Reporting and Data System (BI-RADS) category 4 or 5 lesions that were detected on MRI but occult on second-look US were enrolled in the study. All suspicious lesions were located in real-time US using MRI virtual navigation in the prone position and then followed by US-guided biopsy or surgical excision. Pathological results were taken as the standard of reference. The detection rate of US with MRI virtual navigation was calculated. The MRI features and pathological types of these lesions were analyzed. A total of 31 lesions were successfully located with real-time US with MRI virtual navigation and then US-guided biopsy or localization, and the detection rate was 93.9% (31/33). Twenty-seven (87.1%, 27/31) proved to be benign lesions and four (12.9%, 4/31) were malignant lesions at pathology. Of the 33 MRI-detected lesions, 31 (93.9%, 31/33) were non-mass enhancements and two (6.1%, 2/33) were masses. This study showed that real-time US with prone MRI virtual navigation is a novel efficient and economical method to improve the detection and US-guided biopsy rate of breast lesions that are detected solely on MRI. Full article
(This article belongs to the Special Issue Frontline of Breast Imaging)
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13 pages, 1944 KiB  
Article
Evaluation of 68Ga-Radiolabeled Peptides for HER2 PET Imaging
by Maxwell Ducharme, Hailey A. Houson, Solana R. Fernandez and Suzanne E. Lapi
Diagnostics 2022, 12(11), 2710; https://doi.org/10.3390/diagnostics12112710 - 5 Nov 2022
Cited by 1 | Viewed by 1683
Abstract
One in eight women will be diagnosed with breast cancer in their lifetime and approximately 25% of those cases will be HER2-positive. Current methods for diagnosing HER2-positive breast cancer involve using IHC and FISH from suspected cancer biopsies to quantify HER2 expression. HER2 [...] Read more.
One in eight women will be diagnosed with breast cancer in their lifetime and approximately 25% of those cases will be HER2-positive. Current methods for diagnosing HER2-positive breast cancer involve using IHC and FISH from suspected cancer biopsies to quantify HER2 expression. HER2 PET imaging could potentially increase accuracy and improve the diagnosis of lesions that are not available for biopsies. Using two previously discovered HER2-targeting peptides, we modified each peptide with the chelator DOTA and a PEG2 linker resulting in DOTA-PEG2-GSGKCCYSL (P5) and DOTA-PEG2-DTFPYLGWWNPNEYRY (P6). Each peptide was labeled with 68Ga and was evaluated for HER2 binding using in vitro cell studies and in vivo tumor xenograft models. Both [68Ga]P5 and [68Ga]P6 showed significant binding to HER2-positive BT474 cells versus HER2-negative MDA-MB-231 cells ([68Ga]P5; 0.68 ± 0.20 versus 0.47 ± 0.05 p < 0.05 and [68Ga]P6; 0.55 ± 0.21 versus 0.34 ± 0.12 p < 0.01). [68Ga]P5 showed a higher percent injected dose per gram (%ID/g) binding to HER2-positive tumors two hours post-injection compared to HER2-negative tumors (0.24 ± 0.04 versus 0.12 ± 0.06; p < 0.05), while the [68Ga]P6 peptide showed significant binding (0.98 ± 0.22 versus 0.51 ± 0.08; p < 0.05) one hour post-injection. These results lay the groundwork for the use of peptides to image HER2-positive breast cancer. Full article
(This article belongs to the Special Issue Frontline of Breast Imaging)
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