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Diagnostics
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Advances in Breast Imaging and Analytics
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Advances in Breast Imaging and Analytics

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 15214

Special Issue Editor

Dr. Ernest Usang Ekpo

E-Mail Website
Guest Editor
Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging Sciences, Faculty of Medicine and Health, University of Sydney, Western Ave, Camperdown, Sydney, NSW 2006, Australia
Interests: optimization of radiological diagnosis through technology and education; breast cancer prediction; early detection; and prognosis; breast density; medical image perception; radiological image evaluation; dose optimization; software technologies for cancer prediction; prognosis; breast composition analyses

Special Issue Information

Dear Colleagues,

The last decade has recorded significant improvements in breast cancer survival rates and reductions in deaths from the disease. These achievements have been possible due to improvements in risk prediction; early detection; and treatment. Therefore; optimising risk prediction and early detection are crucial to further reducing breast cancer deaths. Breast imaging tools such as mammography; ultrasound; digital breast tomosynthesis; computed tomography; magnetic resonance imaging; and molecular breast imaging are constantly evolving to optimise breast cancer detection and assessment. Despite these technological advances; about 30% of breast cancer cases are missed; suggesting that strategies to improve the interpretation of breast images are needed. Another issue is that breast cancer post-treatment events such as recurrence and secondary cancer are major causal factors for breast-cancer-related deaths. Therefore; the accurate prediction of treatment outcomes is crucial to develop informed options for tailoring follow-up strategies and reducing breast cancer deaths. Current outcome prediction tools; which are based on clinicopathologic data; show moderate predictive powers at best. Recent evidence indicates that medical images contain covert information that can be modelled to improve the risk prediction; detection; and prognosis of breast cancer. Interestingly; novel technologies such as artificial intelligence and machine learning provide opportunities to extract and model image-based information and genomic and clinicopathologic data as well as data from medical health records to transform the prediction; detection; and prognosis of breast cancer.

The purpose of this Special Issue is to investigate how breast imaging hardware technologies and image interpreters (radiologists) can be further optimised to facilitate early detection; and how intelligent software technologies can be used to extract information from breast images and combine with genomic and clinicopathologic data as well as medical health records to improve the prediction; detection; and prognosis of breast cancer.

Dr. Ernest Usang Ekpo
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 cancer
  • breast cancer early detection
  • breast cancer assessment
  • breast cancer risk prediction
  • breast cancer prognosis
  • breast density
  • breast radiomics
  • breast imaging technologies
  • digital mammography
  • digital breast tomosynthesis
  • ultrasound
  • breast computed tomography
  • breast magnetic resonance imaging
  • molecular breast imaging
  • artificial intelligence
  • machine learning
  • technology and observer performance

Published Papers (6 papers)

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Research

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12 pages, 904 KiB  
Article
Clinicopathological and Imaging Features of Breast Papillary Lesions and Their Association with Pathologic Nipple Discharge
by Jeongeum Oh and Ji Yeon Park
Diagnostics 2023, 13(5), 878; https://doi.org/10.3390/diagnostics13050878 - 24 Feb 2023
Cited by 2 | Viewed by 1270
Abstract
No studies have evaluated whether any clinicopathological or imaging characteristics of breast papillary lesions are associated with pathological nipple discharge (PND). We analyzed 301 surgically confirmed papillary breast lesions diagnosed between January 2012 and June 2022. We evaluated clinical (age of patient, size [...] Read more.
No studies have evaluated whether any clinicopathological or imaging characteristics of breast papillary lesions are associated with pathological nipple discharge (PND). We analyzed 301 surgically confirmed papillary breast lesions diagnosed between January 2012 and June 2022. We evaluated clinical (age of patient, size of lesion, pathologic nipple discharge, palpability, personal/family history of breast cancer or papillary lesion, location, multiplicity, and bilaterality) and imaging characteristics (Breast Imaging Reporting and Data System (BI-RADS), sonographic, and mammographic findings) and compared malignant versus non-malignant lesions and papillary lesions with versus without PND. The malignant group was significantly older than the non-malignant group (p < 0.001). Those in the malignant group were more palpable and larger (p < 0.001). Family history of cancer and peripheral location in the malignant group were more frequent than in the non-malignant group (p = 0.022 and p < 0.001). The malignant group showed higher BI-RADS, irregular shape, complex cystic and solid echo pattern, posterior enhancement on ultrasound (US), fatty breasts, visibility, and mass type on mammography (p < 0.001, 0.003, 0.009, <0.001, <0.001, <0.001, and 0.01, respectively). On multivariate logistic regression analysis, peripheral location, palpability, and age of ≥50 years were factors significantly associated with malignancy (OR: 4.125, 3.556, and 3.390, respectively; p = 0.004, 0.034, and 0.011, respectively). Central location, intraductal nature, hyper/isoechoic pattern, and ductal change were more frequent in the PND group (p = 0.003, p < 0.001, p < 0.001, and p < 0.001, respectively). Ductal change was significantly associated with PND on multivariate analysis (OR, 5.083; p = 0.029). Our findings will help clinicians examine patients with PND and breast papillary lesions more effectively. Full article
(This article belongs to the Special Issue Advances in Breast Imaging and Analytics)
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11 pages, 1232 KiB  
Article
Diagnostic Usefulness of Diffusion-Weighted MRI for Axillary Lymph Node Evaluation in Patients with Breast Cancer
by Pyeonghwa Cho, Chang Suk Park, Ga Eun Park, Sung Hun Kim, Hyeon Sook Kim and Se-Jeong Oh
Diagnostics 2023, 13(3), 513; https://doi.org/10.3390/diagnostics13030513 - 31 Jan 2023
Cited by 2 | Viewed by 1628
Abstract
This study aimed to determine whether apparent diffusion coefficient (ADC) and morphological features on diffusion-weighted MRI (DW-MRI) can discriminate metastatic axillary lymph nodes (ALNs) from benign in patients with breast cancer. Two radiologists measured ADC, long and short diameters, long-to-short diameter ratio, and [...] Read more.
This study aimed to determine whether apparent diffusion coefficient (ADC) and morphological features on diffusion-weighted MRI (DW-MRI) can discriminate metastatic axillary lymph nodes (ALNs) from benign in patients with breast cancer. Two radiologists measured ADC, long and short diameters, long-to-short diameter ratio, and cortical thickness and assessed eccentric cortical thickening, loss of fatty hilum, irregular margin, asymmetry in shape or number, and rim sign of ALNs on DW-MRI and categorized them into benign or suspicious ALNs. Pathologic reports were used as a reference standard. Statistical analysis was performed using the Mann–Whitney U test and chi-square test. Overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of DW-MRI were calculated. The ADC of metastatic ALNs was 0.905 × 10−3 mm2/s, and that of benign ALNs was 0.991 × 10−3 mm2/s (p = 0.243). All morphologic features showed significant difference between the two groups. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the final categorization on DW-MRI were 77.1%, 93.3%, 79.4%, 92.5%, and 86.2%, respectively. Our results suggest that morphologic evaluation of ALNs on DWI can discriminate metastatic ALNs from benign. The ADC value of metastatic ALNs was lower than that of benign nodes, but the difference was not statistically significant. Full article
(This article belongs to the Special Issue Advances in Breast Imaging and Analytics)
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18 pages, 1354 KiB  
Article
Development of an Artificial Intelligence-Based Breast Cancer Detection Model by Combining Mammograms and Medical Health Records
by Nguyen Thi Hoang Trang, Khuong Quynh Long, Pham Le An and Tran Ngoc Dang
Diagnostics 2023, 13(3), 346; https://doi.org/10.3390/diagnostics13030346 - 17 Jan 2023
Cited by 6 | Viewed by 3677
Abstract
Background: Artificial intelligence (AI)-based computational models that analyze breast cancer have been developed for decades. The present study was implemented to investigate the accuracy and efficiency of combined mammography images and clinical records for breast cancer detection using machine learning and deep learning [...] Read more.
Background: Artificial intelligence (AI)-based computational models that analyze breast cancer have been developed for decades. The present study was implemented to investigate the accuracy and efficiency of combined mammography images and clinical records for breast cancer detection using machine learning and deep learning classifiers. Methods: This study was verified using 731 images from 357 women who underwent at least one mammogram and had clinical records for at least six months before mammography. The model was trained on mammograms and clinical variables to discriminate benign and malignant lesions. Multiple pre-trained deep CNN models to detect cancer in mammograms, including X-ception, VGG16, ResNet-v2, ResNet50, and CNN3 were employed. Machine learning models were constructed using k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), Artificial Neural Network (ANN), and gradient boosting machine (GBM) in the clinical dataset. Results: The detection performance obtained an accuracy of 84.5% with a specificity of 78.1% at a sensitivity of 89.7% and an AUC of 0.88. When trained on mammography image data alone, the result achieved a slightly lower score than the combined model (accuracy, 72.5% vs. 84.5%, respectively). Conclusions: A breast cancer-detection model combining machine learning and deep learning models was performed in this study with a satisfactory result, and this model has potential clinical applications. Full article
(This article belongs to the Special Issue Advances in Breast Imaging and Analytics)
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Review

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29 pages, 1049 KiB  
Review
New Biomarkers and Treatment Advances in Triple-Negative Breast Cancer
by Brahim El Hejjioui, Salma Lamrabet, Sarah Amrani Joutei, Nadia Senhaji, Touria Bouhafa, Moulay Abdelilah Malhouf, Sanae Bennis and Laila Bouguenouch
Diagnostics 2023, 13(11), 1949; https://doi.org/10.3390/diagnostics13111949 - 02 Jun 2023
Cited by 2 | Viewed by 2904
Abstract
Triple-negative breast cancer (TNBC) is a specific subtype of breast cancer lacking hormone receptor expression and HER2 gene amplification. TNBC represents a heterogeneous subtype of breast cancer, characterized by poor prognosis, high invasiveness, high metastatic potential, and a tendency to relapse. In this [...] Read more.
Triple-negative breast cancer (TNBC) is a specific subtype of breast cancer lacking hormone receptor expression and HER2 gene amplification. TNBC represents a heterogeneous subtype of breast cancer, characterized by poor prognosis, high invasiveness, high metastatic potential, and a tendency to relapse. In this review, the specific molecular subtypes and pathological aspects of triple-negative breast cancer are illustrated, with particular attention to the biomarker characteristics of TNBC, namely: regulators of cell proliferation and migration and angiogenesis, apoptosis-regulating proteins, regulators of DNA damage response, immune checkpoints, and epigenetic modifications. This paper also focuses on omics approaches to exploring TNBC, such as genomics to identify cancer-specific mutations, epigenomics to identify altered epigenetic landscapes in cancer cells, and transcriptomics to explore differential mRNA and protein expression. Moreover, updated neoadjuvant treatments for TNBC are also mentioned, underlining the role of immunotherapy and novel and targeted agents in the treatment of TNBC. Full article
(This article belongs to the Special Issue Advances in Breast Imaging and Analytics)
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18 pages, 13798 KiB  
Review
Recent Advances in Ultrasound Breast Imaging: From Industry to Clinical Practice
by Orlando Catalano, Roberta Fusco, Federica De Muzio, Igino Simonetti, Pierpaolo Palumbo, Federico Bruno, Alessandra Borgheresi, Andrea Agostini, Michela Gabelloni, Carlo Varelli, Antonio Barile, Andrea Giovagnoni, Nicoletta Gandolfo, Vittorio Miele and Vincenza Granata
Diagnostics 2023, 13(5), 980; https://doi.org/10.3390/diagnostics13050980 - 04 Mar 2023
Cited by 5 | Viewed by 3460
Abstract
Breast ultrasound (US) has undergone dramatic technological improvement through recent decades, moving from a low spatial resolution, grayscale-limited technique to a highly performing, multiparametric modality. In this review, we first focus on the spectrum of technical tools that have become commercially available, including [...] Read more.
Breast ultrasound (US) has undergone dramatic technological improvement through recent decades, moving from a low spatial resolution, grayscale-limited technique to a highly performing, multiparametric modality. In this review, we first focus on the spectrum of technical tools that have become commercially available, including new microvasculature imaging modalities, high-frequency transducers, extended field-of-view scanning, elastography, contrast-enhanced US, MicroPure, 3D US, automated US, S-Detect, nomograms, images fusion, and virtual navigation. In the subsequent section, we discuss the broadened current application of US in breast clinical scenarios, distinguishing among primary US, complementary US, and second-look US. Finally, we mention the still ongoing limitations and the challenging aspects of breast US. Full article
(This article belongs to the Special Issue Advances in Breast Imaging and Analytics)
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Other

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8 pages, 5166 KiB  
Case Report
Initial Imaging Findings of Breast Liposarcoma: A Case Report
by Sharifa Khalid Alduraibi
Diagnostics 2023, 13(14), 2428; https://doi.org/10.3390/diagnostics13142428 - 20 Jul 2023
Viewed by 1529
Abstract
Liposarcoma of the breast is a rare form of cancerous tumor that can be mistaken for primary breast cancer. A recent instance involved a woman who was 54 years old and went in for her annual screening mammogram. The mammogram revealed that she [...] Read more.
Liposarcoma of the breast is a rare form of cancerous tumor that can be mistaken for primary breast cancer. A recent instance involved a woman who was 54 years old and went in for her annual screening mammogram. The mammogram revealed that she had a 1 cm focal asymmetry of equal density in her right axillary tail, approximately 9 cm from the nipple. After nine months, the patient observed a rapidly growing mass even though the initial ultrasound scan did not detect anything unusual. A targeted mammogram demonstrated a large and dense mass confined to the right axillary tail, followed by an ultrasound scan that revealed a heterogeneous hyperechoic, echogenic mass. Histopathology after surgery showed that the patient had an undifferentiated pleomorphic breast liposarcoma. This diagnosis was reached after the patient underwent surgery.Liposarcoma of the breast is a concerning condition that needs careful management and close monitoring, although it is relatively uncommon. Early detection of the patient’s condition and prompt treatment can help improve the patient’s prognosis. This can be accomplished by remaining vigilant with routine screenings and following up on any unusual findings or changes in breast tissue. However, it is possible to diagnose this condition as primary breast cancer incorrectly; consequently, healthcare providers need to conduct comprehensive evaluations to ensure diagnostic accuracy and the delivery of appropriate treatment. Full article
(This article belongs to the Special Issue Advances in Breast Imaging and Analytics)
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