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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 28227

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Dr. Ernest Usang Ekpo

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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

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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

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Published Papers (11 papers)

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Research

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16 pages, 3275 KiB

Open AccessArticle

Enhancing Microcalcification Detection in Mammography with YOLO-v8 Performance and Clinical Implications

by Wei-Chung Shia and Tien-Hsiung Ku

Diagnostics 2024, 14(24), 2875; https://doi.org/10.3390/diagnostics14242875 - 20 Dec 2024

Viewed by 512

Abstract

Background: Microcalcifications in the breast are often an early warning sign of breast cancer, and their accurate detection is crucial for the early discovery and management of the disease. In recent years, deep learning technology, particularly models based on object detection, has significantly improved the ability to detect microcalcifications. This study aims to use the advanced YOLO-v8 object detection algorithm to identify breast microcalcifications and explore its advantages in terms of performance and clinical application. Methods: This study collected mammograms from 7615 female participants, with a dataset including 10,323 breast images containing microcalcifications. We used the YOLO-v8 model for microcalcification detection and trained and validated the model using five-fold cross-validation. The model’s performance was evaluated through metrics such as accuracy, recall, F1 score, mAP50, and mAP50-95. Additionally, this study explored the potential applications of this technology in clinical practice. Results: The YOLO-v8 model achieved an mAP50 of 0.921, an mAP50-95 of 0.709, an F1 score of 0.82, a detection accuracy of 0.842, and a recall rate of 0.796 in breast microcalcification detection. Compared to previous similar deep learning object detection techniques like Mask R-CNN, YOLO-v8 has shown improvements in both speed and accuracy. Conclusions: YOLO-v8 outperforms traditional detection methods in detecting breast microcalcifications. Its multi-scale detection capability significantly enhances both speed and accuracy, making it more clinically practical for large-scale screenings. Future research should further explore the model’s potential in benign and malignant classification to promote its application in clinical settings, assisting radiologists in diagnosing breast cancer more efficiently. Full article

(This article belongs to the Special Issue Advances in Breast Imaging and Analytics)

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11 pages, 4742 KiB

Open AccessArticle

Diabetic Mastopathy: A Monocentric Study to Explore This Uncommon Breast Disease

by Luciano Mariano, Luca Nicosia, Sofia Scolari, Sara Pasi, Sofia Netti, Giovanni Mazzarol, Antuono Latronico and Enrico Cassano

Diagnostics 2024, 14(23), 2749; https://doi.org/10.3390/diagnostics14232749 - 6 Dec 2024

Viewed by 759

Abstract

Background: Diabetic Mastopathy (DMP) is an uncommon benign fibro-inflammatory condition that occurs in women with long-standing diabetes mellitus (DM), particularly type 1. It often mimics breast cancer (BC) in clinical and imaging presentations, leading to diagnostic challenges. Methods: A retrospective monocentric study was conducted, analyzing clinical, radiologic, and pathological data from 28 women diagnosed with DMP over 10 years at the European Institute of Oncology. Data on DM type, age at DMP diagnosis, associated autoimmune conditions, imaging features, and surgical outcomes were collected and compared with the existing literature. Results: The majority (82%) of the patients had type 1 DM, with most diagnosed with DMP before age 40. Common complications included retinopathy (46%) and neuropathy (35%). Imaging often suggested malignancy, necessitating core needle biopsies for diagnosis. Surgical intervention occurred in 55% of cases, with a recurrence rate of 32%. One case of BC was observed. Conclusions: DMP remains challenging due to its resemblance to BC. Conservative management is typical, but the recurrence rate post-surgery highlights the importance of ongoing monitoring. Although DMP does not significantly increase BC risk, caution is advised, especially for immunocompromised patients. Further studies are needed to comprehensively understand DMP’s relationship with BC. Full article

(This article belongs to the Special Issue Advances in Breast Imaging and Analytics)

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11 pages, 1149 KiB

Open AccessArticle

Mammographic Breast Density at Breast Cancer Diagnosis and Breast Cancer-Specific Survival

by Ibrahem Kanbayti, Judith Akwo, Akwa Erim, Ekaete Ukpong and Ernest Ekpo

Diagnostics 2024, 14(21), 2382; https://doi.org/10.3390/diagnostics14212382 - 25 Oct 2024

Viewed by 650

Abstract

Background: Breast density impacts upon breast cancer risk and recurrence, but its influence on breast cancer-specific survival is unclear. This study examines the influence of mammographic breast density (MBD) at diagnosis on breast cancer-specific survival. Methods: The data of 224 patients diagnosed with breast cancer were analyzed. Two area-based MBD measurement tools—AutoDensity and LIBRA—were used to measure MBD via a mammogram of the contralateral breast acquired at the time of diagnosis. These patients were split into two groups based on their percent breast density (PBD): high (PBD ≥ 20%) versus low (PBD < 20%). Breast cancer-specific survival in each of these PBD groups was assessed at a median follow-up of 34 months using Kaplan–Meier analysis and the Cox proportional hazards model. Results: The proportion of women with low PBD who died from breast cancer was significantly higher than that seen with high PBD (p = 0.01). The 5-year breast cancer-specific survival was poorer among women with low PBD than those with high PBD (0.348; 95% CI: 0.13–0.94) vs. 0.87; 95% CI: (0.8–0.96); p < 0.001)]. Women with higher breast density demonstrated longer survival regardless of the method of PBD measurement: LIBRA [log-rank test (Mantel–Cox): 9.4; p = 0.002)]; AutoDensity [log-rank test (Mantel–Cox) 7.6; p = 0.006]. Multivariate analysis also demonstrated that there was a higher risk of breast cancer-related deaths in women with low PBD (adjusted HR: 5.167; 95% CI: 1.974–13.521; p = 0.001). Conclusion: Women with <20% breast density at breast cancer diagnosis demonstrate poor survival regarding the disease. The impact of breast density on survival is not influenced by the method of measurement. Full article

(This article belongs to the Special Issue Advances in Breast Imaging and Analytics)

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21 pages, 4669 KiB

Open AccessArticle

Pre-Reconstruction Processing with the Cycle-Consist Generative Adversarial Network Combined with Attention Gate to Improve Image Quality in Digital Breast Tomosynthesis

by Tsutomu Gomi, Kotomi Ishihara, Satoko Yamada and Yukio Koibuchi

Diagnostics 2024, 14(17), 1957; https://doi.org/10.3390/diagnostics14171957 - 4 Sep 2024

Viewed by 955

Abstract

The current study proposed and evaluated “residual squeeze and excitation attention gate” (rSEAG), a novel network that can improve image quality by reducing distortion attributed to artifacts. This method was established by modifying the Cycle Generative Adversarial Network (cycleGAN)-based generator network using projection data for pre-reconstruction processing in digital breast tomosynthesis. Residual squeeze and excitation were installed in the bridge of the generator network, and the attention gate was installed in the skip connection between the encoder and decoder. Based on the radiation dose index (exposure index and division index) incident on the detector, the cases approved by the ethics committee and used for the study were classified as reference (675 projection images) and object (675 projection images). For the cases, unsupervised data containing a mixture of cases with and without masses were used. The cases were trained using cycleGAN with rSEAG and the conventional networks (ResUNet and U-Net). For testing, predictive processing was performed on cases (60 projection images) that were not used for learning. Images were generated using filtered backprojection reconstruction (kernel: Ramachandran and Lakshminarayanan) from projection data for testing data and without pre-reconstruction processing data (evaluation: in-focus plane). The distortion was evaluated using perception-based image quality evaluation (PIQE) analysis, texture analysis (feature: “Homogeneity” and “Contrast”), and a statistical model with a Gumbel distribution. PIQE has a low rSEAG value. Texture analysis showed that rSEAG and a network without cycleGAN were similar in terms of the “Contrast” feature. In dense breasts, ResUNet had the lowest “Contrast” feature and U-Net had differences between cases. The maximal variations in the Gumbel plot, rSEAG reduced the high-frequency ripple artifacts. In this study, rSEAG could improve distortion and reduce ripple artifacts. Full article

(This article belongs to the Special Issue Advances in Breast Imaging and Analytics)

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12 pages, 904 KiB

Open AccessArticle

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 3 | Viewed by 2061

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 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

Open AccessArticle

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 5 | Viewed by 2565

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 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

Open AccessArticle

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 13 | Viewed by 5377

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 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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16 pages, 9691 KiB

Open AccessReview

Tomosynthesis-Guided Biopsy: A Troubleshooting Guide

by Reve Chahine, Madiha Hijazi, Najwa Radwan, Ghina Berjawi and Lara Nassar

Diagnostics 2025, 15(3), 295; https://doi.org/10.3390/diagnostics15030295 - 27 Jan 2025

Viewed by 328

Abstract

Since its introduction, digital breast tomosynthesis (DBT) has been widely incorporated in screening for breast cancer due to its lesser recall and higher cancer detection rates. Some screen-detected lesions may be visible only by DBT, requiring biopsy using DBT guidance. This review article dissects the different steps of tomosynthesis-guided biopsy and discusses the different obstacles that might be encountered during each step while providing the appropriate solutions, hence allowing physicians to perform a successful biopsy with the least patient discomfort. Full article

(This article belongs to the Special Issue Advances in Breast Imaging and Analytics)

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29 pages, 1049 KiB

Open AccessReview

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 - 2 Jun 2023

Cited by 7 | Viewed by 5433

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 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

Open AccessReview

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 - 4 Mar 2023

Cited by 14 | Viewed by 5752

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 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

Open AccessCase 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 2487

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 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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