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Diagnostics
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Advances in Breast Radiology
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Advances in Breast Radiology

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 8744

Special Issue Editors

Dr. Maria Adele Marino

E-Mail Website
Guest Editor
Department of Biomedical Sciences and Morphologic and Functional Imaging, University of Messina, Messina, Italy
Interests: breast imaging; digital mammography; digital breast tomosynthesis; breast cancer; breast MRI; ultrasound; elastography; contrast-enhanced ultrasound; contrast-enhanced mammography
Dr. Elisabetta Giannotti

E-Mail Website
Guest Editor
Nottingham Breast Institute, Nottingham University Hospitals, Nottingham NG5 1PB, UK
Interests: breast imaging; digital mammography; digital breast tomosynthesis; breast cancer; breast MRI; ultrasound; elastography; contrast-enhanced ultrasound; contrast-enhanced mammography
Daly Avendano

E-Mail Website
Guest Editor Assistant
Tecnologico de Monterrey, School of Medicine and Health Science, Monterrey, Nuevo León, Mexico
Interests: breast imaging; digital mammography; digital breast tomosynthesis; breast cancer; breast MRI; ultrasound; elastography; contrast-enhanced ultrasound; contrast-enhanced mammography

Special Issue Information

Dear Colleagues, 

This Special Issue of Diagnostics is focused on Advances in Breast Radiology, with special interest in contrast-enhanced techniques used in the field of breast imaging. Contrast-enhanced MRI is an established tool for breast cancer detection and diagnosis, and much interest has recently been raised by the 2D counterpart (i.e., contrast-enhanced mammography), which has proved its diagnostic accuracy. Contrast-enhanced ultrasound is taking on an emerging role, although still limited. It is mainly used in the setting of multiparametric US protocols for breast cancer depiction. The aim of this Special Issue is to highlight the main advances in breast radiology, with particular attention to contrast-enhanced imaging and the possible implication of artificial intelligence in this specific field.

Dr. Maria Adele Marino
Dr. Elisabetta Giannotti
Guest Editors
Daly Avendano
Guest Editor Assistant

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 contrast-enhanced MRI
  • breast contrast-enhanced US
  • breast contrast-enhanced mammography

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Published Papers (5 papers)

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10 pages, 1142 KiB  
Article
Relationship between Volpara Density Grade and Compressed Breast Thickness in Japanese Patients with Breast Cancer
by Mio Adachi, Toshiyuki Ishiba, Sakiko Maruya, Kumiko Hayashi, Yuichi Kumaki, Goshi Oda and Tomoyuki Aruga
Diagnostics 2024, 14(15), 1651; https://doi.org/10.3390/diagnostics14151651 - 31 Jul 2024
Cited by 1 | Viewed by 693
Abstract
Background: High breast density found using mammographs (MGs) reduces positivity rates and is considered a risk factor for breast cancer. Research on the relationship between Volpara density grade (VDG) and compressed breast thickness (CBT) in the Japanese population is still lacking. Moreover, little [...] Read more.
Background: High breast density found using mammographs (MGs) reduces positivity rates and is considered a risk factor for breast cancer. Research on the relationship between Volpara density grade (VDG) and compressed breast thickness (CBT) in the Japanese population is still lacking. Moreover, little attention has been paid to pseudo-dense breasts with CBT < 30 mm among high-density breasts. We investigated VDG, CBT, and apparent high breast density in patients with breast cancer. Methods: Women who underwent MG and breast cancer surgery at our institution were included. VDG and CBT were measured. VDG was divided into a non-dense group (NDG) and a dense group (DG). Results: This study included 419 patients. VDG was negatively correlated with CBT. The DG included younger patients with lower body mass index (BMI) and thinner CBT. In the DG, patients with CBT < 30 mm had lower BMI and higher VDG; however, no significant difference was noted in the positivity rate of the two groups. Conclusions: Younger women tend to have higher breast density, resulting in thinner CBT, which may pose challenges in detecting breast cancer on MGs. However, there was no significant difference in the breast cancer detection rate between CBT < 30 mm and CBT ≥ 30 mm. Full article
(This article belongs to the Special Issue Advances in Breast Radiology)
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13 pages, 4339 KiB  
Article
Evaluating the Margins of Breast Cancer Tumors by Using Digital Breast Tomosynthesis with Deep Learning: A Preliminary Assessment
by Wei-Chung Shia, Yu-Hsun Kuo, Fang-Rong Hsu, Joseph Lin, Wen-Pei Wu, Hwa-Koon Wu, Wei-Cheng Yeh and Dar-Ren Chen
Diagnostics 2024, 14(10), 1032; https://doi.org/10.3390/diagnostics14101032 - 16 May 2024
Cited by 1 | Viewed by 1081
Abstract
Background: The assessment information of tumor margins is extremely important for the success of the breast cancer surgery and whether the patient undergoes a second operation. However, conducting surgical margin assessments is a time-consuming task that requires pathology-related skills and equipment, and often [...] Read more.
Background: The assessment information of tumor margins is extremely important for the success of the breast cancer surgery and whether the patient undergoes a second operation. However, conducting surgical margin assessments is a time-consuming task that requires pathology-related skills and equipment, and often cannot be provided in a timely manner. To address this challenge, digital breast tomosynthesis technology was utilized to generate detailed cross-sectional images of the breast tissue and integrate deep learning algorithms for image segmentation, achieving an assessment of tumor margins during surgery. Methods: this study utilized post-operative tissue samples from 46 patients who underwent breast-conserving treatment, and generated image sets using digital breast tomosynthesis for the training and evaluation of deep learning models. Results: Deep learning algorithms effectively identifying the tumor area. They achieved a Mean Intersection over Union (MIoU) of 0.91, global accuracy of 99%, weighted IoU of 44%, precision of 98%, recall of 83%, F1 score of 89%, and dice coefficient of 93% on the training dataset; for the testing dataset, MIoU was at 83%, global accuracy at 97%, weighted IoU at 38%, precision at 87%, recall rate at 69%, F1 score at 76%, dice coefficient at 86%. Conclusions: The initial evaluation suggests that the deep learning-based image segmentation method is highly accurate in measuring breast tumor margins. This helps provide information related to tumor margins during surgery, and by using different datasets, this research method can also be applied to the surgical margin assessment of various types of tumors. Full article
(This article belongs to the Special Issue Advances in Breast Radiology)
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14 pages, 7320 KiB  
Article
Breast Delineation in Full-Field Digital Mammography Using the Segment Anything Model
by Andrés Larroza, Francisco Javier Pérez-Benito, Raquel Tendero, Juan Carlos Perez-Cortes, Marta Román and Rafael Llobet
Diagnostics 2024, 14(10), 1015; https://doi.org/10.3390/diagnostics14101015 - 15 May 2024
Viewed by 1072
Abstract
Breast cancer is a major health concern worldwide. Mammography, a cost-effective and accurate tool, is crucial in combating this issue. However, low contrast, noise, and artifacts can limit the diagnostic capabilities of radiologists. Computer-Aided Diagnosis (CAD) systems have been developed to overcome these [...] Read more.
Breast cancer is a major health concern worldwide. Mammography, a cost-effective and accurate tool, is crucial in combating this issue. However, low contrast, noise, and artifacts can limit the diagnostic capabilities of radiologists. Computer-Aided Diagnosis (CAD) systems have been developed to overcome these challenges, with the accurate outlining of the breast being a critical step for further analysis. This study introduces the SAM-breast model, an adaptation of the Segment Anything Model (SAM) for segmenting the breast region in mammograms. This method enhances the delineation of the breast and the exclusion of the pectoral muscle in both medio lateral-oblique (MLO) and cranio-caudal (CC) views. We trained the models using a large, multi-center proprietary dataset of 2492 mammograms. The proposed SAM-breast model achieved the highest overall Dice Similarity Coefficient (DSC) of 99.22% ± 1.13 and Intersection over Union (IoU) 98.48% ± 2.10 over independent test images from five different datasets (two proprietary and three publicly available). The results are consistent across the different datasets, regardless of the vendor or image resolution. Compared with other baseline and deep learning-based methods, the proposed method exhibits enhanced performance. The SAM-breast model demonstrates the power of the SAM to adapt when it is tailored to specific tasks, in this case, the delineation of the breast in mammograms. Comprehensive evaluations across diverse datasets—both private and public—attest to the method’s robustness, flexibility, and generalization capabilities. Full article
(This article belongs to the Special Issue Advances in Breast Radiology)
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12 pages, 2232 KiB  
Article
Prognostic Molecular Biomarkers in Breast Cancer Lesions with Non-Mass Enhancement on MR
by Mei-Lin Wang, Yu-Pin Chang, Chen-Hao Wu, Chuan-Han Chen, Mein-Kai Gueng, Yi-Ying Wu and Jyh-Wen Chai
Diagnostics 2024, 14(7), 747; https://doi.org/10.3390/diagnostics14070747 - 30 Mar 2024
Viewed by 1377
Abstract
Clustered ring enhancement (CRE) is a new lexicon for non-mass enhancement (NME) of breast MR in the 5th BIRADS, indicating a high suspicion of malignancy. We wonder if the presence of CRE correlates with expression of prognostic molecular biomarkers of breast cancer. A [...] Read more.
Clustered ring enhancement (CRE) is a new lexicon for non-mass enhancement (NME) of breast MR in the 5th BIRADS, indicating a high suspicion of malignancy. We wonder if the presence of CRE correlates with expression of prognostic molecular biomarkers of breast cancer. A total of 58 breast lesions, which MRI reported with NME, were collected between July 2013 and December 2018. The patterns of enhancement including CRE were reviewed and the pathological results with expression of molecular biomarkers were collected. The association between MRI NME, pathological, and IHC stain findings were investigated under univariate analysis. A total of 58 breast lesions were pathologically proven to have breast cancer, comprising 31 lesions with CRE and 27 lesions without CRE on breast MRI. The expression of the estrogen receptor (ER) (p = 0.017) and the progesterone receptor (PR) (p = 0.017) was significantly lower in lesions with CRE as compared with those without CRE. The expression of Ki-67 (≥25%) was significantly higher in lesions with CRE (p = 0.046). The lesions with CRE had a lower expression ratio of ER (50.71 ± 45.39% vs. 74.26 ± 33.59%, p = 0.028). Our study indicated that lesions with CRE may possess different features from those without CRE in molecular expression, bearing a more aggressive behavior. Full article
(This article belongs to the Special Issue Advances in Breast Radiology)
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17 pages, 826 KiB  
Systematic Review
The Applications of High-Intensity Focused Ultrasound (HIFU) Ablative Therapy in the Treatment of Primary Breast Cancer: A Systematic Review
by Dania Zulkifli, Hanani Abdul Manan, Noorazrul Yahya and Hamzaini Abdul Hamid
Diagnostics 2023, 13(15), 2595; https://doi.org/10.3390/diagnostics13152595 - 4 Aug 2023
Cited by 10 | Viewed by 3621
Abstract
Background: This study evaluates the role of high-intensity focused ultrasound (HIFU) ablative therapy in treating primary breast cancer. Methods: PubMed and Scopus databases were searched according to the PRISMA guidelines to identify studies from 2002 to November 2022. Eligible studies were selected based [...] Read more.
Background: This study evaluates the role of high-intensity focused ultrasound (HIFU) ablative therapy in treating primary breast cancer. Methods: PubMed and Scopus databases were searched according to the PRISMA guidelines to identify studies from 2002 to November 2022. Eligible studies were selected based on criteria such as experimental study type, the use of HIFU therapy as a treatment for localised breast cancer with objective clinical evaluation, i.e., clinical, radiological, and pathological outcomes. Nine studies were included in this study. Results: Two randomised controlled trials and seven non-randomised clinical trials fulfilled the inclusion criteria. The percentage of patients who achieved complete (100%) coagulation necrosis varied from 17% to 100% across all studies. Eight of the nine studies followed the treat-and-resect protocol in which HIFU-ablated tumours were surgically resected for pathological evaluation. Most breast cancers were single, solitary, and palpable breast tumours. Haematoxylin and eosin stains used for histopathological evaluation showed evidence of coagulation necrosis. Radiological evaluation by MRI showed an absence of contrast enhancement in the HIFU-treated tumour and 1.5 to 2 cm of normal breast tissue, with a thin peripheral rim of enhancement indicative of coagulation necrosis. All studies did not report severe complications, i.e., haemorrhage and infection. Common complications related to HIFU ablation were local mammary oedema, pain, tenderness, and mild to moderate burns. Only one third-degree burn was reported. Generally, the cosmetic outcome was good. The five-year disease-free survival rate was 95%, as reported in two RCTs. Conclusions: HIFU ablation can induce tumour coagulation necrosis in localised breast cancer, with a favourable safety profile and cosmetic outcome. However, there is variable evidence of complete coagulation necrosis in the HIFU-treated tumour. Histopathological evidence of coagulation necrosis has been inconsistent, and there is no reliable radiological modality to assess coagulation necrosis confidently. Further exploration is needed to establish the accurate ablation margin with a reliable radiological modality for treatment and follow-up. HIFU therapy is currently limited to single, palpable breast tumours. More extensive and randomised clinical trials are needed to evaluate HIFU therapy for breast cancer, especially where the tumour is left in situ. Full article
(This article belongs to the Special Issue Advances in Breast Radiology)
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