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Advances in Breast Cancer Imaging and Treatment
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Advances in Breast Cancer Imaging and Treatment

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 39380

Special Issue Editors

Prof. Dr. Federica Pediconi

E-Mail Website
Guest Editor
Department of Radiological, Oncological and Pathological Sciences, Università degli Studi di Roma La Sapienza, Rome, Italy
Interests: breast imaging; comprised interventional; MRI
Special Issues, Collections and Topics in MDPI journals
Dr. Francesca Galati

E-Mail Website
Guest Editor
Department of Radiological, Oncological and Pathological Sciences, Università degli Studi di Roma La Sapienza, Rome, Italy
Interests: breast imaging; comprised interventional; oncology imaging

Special Issue Information

Dear Colleagues,

Breast cancer is the most commonly diagnosed cancer in women, and the first cause of cancer-related death in the female population. In spite of the high incidence, the breast cancer mortality rate has been declining for several years due to more effective treatments and early cancer detection, owing to the progressive spread of modern breast imaging techniques such as DBT, elastosonography, CEM and MRI functional techniques. Moreover, radiomics/radiogenomics image analysis and artificial intelligence are guiding new approaches to highlight breast cancer characteristics and to stratify patients according to risk of disease, risk of recurrence, overall survival and other prognostic and predictive factors (e.g., response to treatment). Over the last few decades, the proportion of early-stage breast cancers at diagnosis has increased, reaching about 80%, and breast-conserving surgery is the standard approach for early-stage breast cancer. However, a number of new minimally invasive imaging-guided treatments for breast cancer have been tested in order to personalize treatment, reduce invasiveness and limit functional and cosmetic drawbacks.

This Special Issue of Diagnostics is focused on the latest research on the diagnosis and prognosis of breast cancer through novel imaging techniques, as well as on personalized therapies with special attention to minimally invasive imaging-guided procedures.

Prof. Dr. Federica Pediconi
Dr. Francesca Galati
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 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

  • MRI
  • PET/MRI
  • CEM
  • DBT
  • US
  • elastosonography
  • minimally invasive imaging-guided procedures
  • radiomics/radiogenomics
  • artificial intelligence
  • breast cancer prognosis
  • breast cancer therapy monitoring

Published Papers (16 papers)

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Research

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13 pages, 721 KiB  
Article
Estimating Local Diagnostic Reference Levels for Mammography in Dubai
by Kaltham Abdulwahid Noor, Norhashimah Mohd Norsuddin, Muhammad Khalis Abdul Karim, Iza Nurzawani Che Isa and Wadha Alshamsi
Diagnostics 2024, 14(1), 8; https://doi.org/10.3390/diagnostics14010008 - 20 Dec 2023
Viewed by 961
Abstract
As the total volume of mammograms in Dubai is increasing consistently, it is crucial to focus on the process of dose optimization by determining dose reference levels for such sensitive radiographic examinations as mammography. This work aimed to determine local diagnostic reference levels [...] Read more.
As the total volume of mammograms in Dubai is increasing consistently, it is crucial to focus on the process of dose optimization by determining dose reference levels for such sensitive radiographic examinations as mammography. This work aimed to determine local diagnostic reference levels (DRLs) for mammography procedures in Dubai at different ranges of breast thickness. A total of 2599 anonymized mammograms were randomly retrieved from a central dose survey database. Mammographic cases for screening women aged from 40 to 69 years were included, while cases of breast implants and breast thickness outside the range of 20–100 mm were excluded. Mean, median, and 75 percentiles were obtained for the mean glandular dose (MGD) distribution of each mammography projection for all compressed breast thickness (CBT) ranges. The local DRLs for mammography in Dubai were found to be between 0.80 mGy and 0.82 mGy for the craniocaudal (CC) projection and between 0.89 mGy and 0.971.8 mGy for the mediolateral oblique (MLO) projection. Local DRLs were proposed according to different breast thicknesses, starting from 20 to 100 mm. All groups of CBT showed a slight difference in MGD values, with higher values in MLO views rather than CC views. The local DRLs in this study were lower than some other Middle Eastern countries and lower than the standard reference levels reported by the International Atomic Energy Agency (IAEA) at 3 mGy/view. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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21 pages, 3665 KiB  
Article
Breast Cancer Detection and Prevention Using Machine Learning
by Arslan Khalid, Arif Mehmood, Amerah Alabrah, Bader Fahad Alkhamees, Farhan Amin, Hussain AlSalman and Gyu Sang Choi
Diagnostics 2023, 13(19), 3113; https://doi.org/10.3390/diagnostics13193113 - 2 Oct 2023
Cited by 4 | Viewed by 11986
Abstract
Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two [...] Read more.
Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two subtypes: invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). The advancements in artificial intelligence (AI) and machine learning (ML) techniques have made it possible to develop more accurate and reliable models for diagnosing and treating this disease. From the literature, it is evident that the incorporation of MRI and convolutional neural networks (CNNs) is helpful in breast cancer detection and prevention. In addition, the detection strategies have shown promise in identifying cancerous cells. The CNN Improvements for Breast Cancer Classification (CNNI-BCC) model helps doctors spot breast cancer using a trained deep learning neural network system to categorize breast cancer subtypes. However, they require significant computing power for imaging methods and preprocessing. Therefore, in this research, we proposed an efficient deep learning model that is capable of recognizing breast cancer in computerized mammograms of varying densities. Our research relied on three distinct modules for feature selection: the removal of low-variance features, univariate feature selection, and recursive feature elimination. The craniocaudally and medial-lateral views of mammograms are incorporated. We tested it with a large dataset of 3002 merged pictures gathered from 1501 individuals who had digital mammography performed between February 2007 and May 2015. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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12 pages, 4648 KiB  
Article
Diagnostic Performance of Contrast-Enhanced Digital Mammography versus Conventional Imaging in Women with Dense Breasts
by Giuliana Moffa, Francesca Galati, Roberto Maroncelli, Veronica Rizzo, Federica Cicciarelli, Marcella Pasculli and Federica Pediconi
Diagnostics 2023, 13(15), 2520; https://doi.org/10.3390/diagnostics13152520 - 28 Jul 2023
Cited by 1 | Viewed by 976
Abstract
The aim of this prospective study was to compare the diagnostic performance of contrast-enhanced mammography (CEM) versus digital mammography (DM) combined with breast ultrasound (BUS) in women with dense breasts. Between March 2021 and February 2022, patients eligible for CEM with the breast [...] Read more.
The aim of this prospective study was to compare the diagnostic performance of contrast-enhanced mammography (CEM) versus digital mammography (DM) combined with breast ultrasound (BUS) in women with dense breasts. Between March 2021 and February 2022, patients eligible for CEM with the breast composition category ACR BI-RADS c–d at DM and an abnormal finding (BI-RADS 3-4-5) at DM and/or BUS were considered. During CEM, a nonionic iodinated contrast agent (Iohexol 350 mg I/mL, 1.5 mL/kg) was power-injected intravenously. Images were evaluated independently by two breast radiologists. Findings classified as BI-RADS 1–3 were considered benign, while BI-RADS 4–5 were considered malignant. In case of discrepancies, the higher category was considered for DM+BUS. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated, using histology/≥12-month follow-up as gold standards. In total, 51 patients with 65 breast lesions were included. 59 (90.7%) abnormal findings were detected at DM+BUS, and 65 (100%) at CEM. The inter-reader agreement was excellent (Cohen’s k = 0.87 for DM+BUS and 0.97 for CEM). CEM showed a 93.5% sensitivity (vs. 90.3% for DM+BUS), a 79.4–82.4% specificity (vs. 32.4–35.5% for DM+BUS) (McNemar p = 0.006), a 80.6–82.9% PPV (vs. 54.9–56.0% for DM+BUS), a 93.1–93.3% NPV (vs. 78.6–80.0% for DM+BUS), and a 86.1–87.7% accuracy (vs. 60.0–61.5% for DM+BUS). The AUC was higher for CEM than for DM+BUS (0.865 vs. 0.613 for Reader 1, and 0.880 vs. 0.628, for Reader 2) (p < 0.001). In conclusion, CEM had a better diagnostic performance than DM and BUS alone and combined together in patients with dense breasts. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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13 pages, 2564 KiB  
Article
Predicting Malignancy of Breast Imaging Findings Using Quantitative Analysis of Contrast-Enhanced Mammography (CEM)
by Matthew M. Miller, Abu Hasnat Mohammad Rubaiyat and Gustavo K. Rohde
Diagnostics 2023, 13(6), 1129; https://doi.org/10.3390/diagnostics13061129 - 16 Mar 2023
Cited by 2 | Viewed by 1544
Abstract
We sought to develop new quantitative approaches to characterize the spatial distribution of mammographic density and contrast enhancement of suspicious contrast-enhanced mammography (CEM) findings to improve malignant vs. benign classifications of breast lesions. We retrospectively analyzed all breast lesions that underwent CEM imaging [...] Read more.
We sought to develop new quantitative approaches to characterize the spatial distribution of mammographic density and contrast enhancement of suspicious contrast-enhanced mammography (CEM) findings to improve malignant vs. benign classifications of breast lesions. We retrospectively analyzed all breast lesions that underwent CEM imaging and tissue sampling at our institution from 2014–2020 in this IRB-approved study. A penalized linear discriminant analysis was used to classify lesions based on the averaged histograms of radial distributions of mammographic density and contrast enhancement. T-tests were used to compare the classification accuracies of density, contrast, and concatenated density and contrast histograms. Logistic regression and AUC-ROC analyses were used to assess if adding demographic and clinical data improved the model accuracy. A total of 159 suspicious findings were evaluated. Density histograms were more accurate in classifying lesions as malignant or benign than a random classifier (62.37% vs. 48%; p < 0.001), but the concatenated density and contrast histograms demonstrated a higher accuracy (71.25%; p < 0.001) than the density histograms alone. Including the demographic and clinical data in our models led to a higher AUC-ROC than concatenated density and contrast images (0.81 vs. 0.70; p < 0.001). In the classification of invasive vs. non-invasive malignancy, the concatenated density and contrast histograms demonstrated no significant improvement in accuracy over the density histograms alone (77.63% vs. 78.59%; p = 0.504). Our findings suggest that quantitative differences in the radial distribution of mammographic density could be used to discriminate malignant from benign breast findings; however, classification accuracy was significantly improved with the addition of contrast-enhanced imaging data from CEM. Adding patient demographic and clinical information further improved the classification accuracy. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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16 pages, 1658 KiB  
Article
Contrast Enhanced Mammography (CEM) Enhancing Asymmetry: Single-Center First Case Analysis
by Giuliano Migliaro, Giulia Bicchierai, Pietro Valente, Federica Di Naro, Diego De Benedetto, Francesco Amato, Cecilia Boeri, Ermanno Vanzi, Vittorio Miele and Jacopo Nori
Diagnostics 2023, 13(6), 1011; https://doi.org/10.3390/diagnostics13061011 - 7 Mar 2023
Cited by 1 | Viewed by 2108
Abstract
(1) Purpose: The latest Breast Imaging Reporting and Data System (BI-RADS) lexicon for CEM introduced a new descriptor, enhancing asymmetries (EAs). The purpose of this study was to determine which types of lesions were correlated with EAs. (2) Methods: A total of 3359 [...] Read more.
(1) Purpose: The latest Breast Imaging Reporting and Data System (BI-RADS) lexicon for CEM introduced a new descriptor, enhancing asymmetries (EAs). The purpose of this study was to determine which types of lesions were correlated with EAs. (2) Methods: A total of 3359 CEM exams, executed at AOUC Careggi in Florence, Italy between 2019 and 2021 were retrospectively assessed by two radiologists. For each of the EAs found, the size, the enhancing conspicuity (degree of enhancement relative to background described as low, moderate, or high), whether there was a corresponding finding in the traditional radiology images (US or mammography), the biopsy results when performed including any follow-up exams, and the presence of background parenchymal enhancement (BPE) of the normal breast tissue (minimal, mild, moderate, marked) were described. (3) Results: A total of 64 women were included, 36 of them underwent CEM for a preoperative staging assessment, and 28 for a problem-solving examination. Among the 64 EAs, 19/64 (29.69%) resulted in being category B5 (B5) lesions, 5/64 (7.81%) as category B3 (B3) lesions, and 40/64(62.50%) were negative or benign either after biopsy or second-look exams or follow-up. We assessed that EAs with higher enhancing conspicuity correlated significantly with a higher risk of B5 lesions (p: 0.0071), especially bigger ones (p: 0.0274). Conclusions: EAs can relate both with benign and tumoral lesions, and they need to be assessed as the other CEM descriptors, with re-evaluation of low-energy images and second-look exams, particularly larger EAs with higher enhancing conspicuity. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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12 pages, 1418 KiB  
Article
Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions
by Alberto Stefano Tagliafico, Massimo Calabrese, Nicole Brunetti, Alessandro Garlaschi, Simona Tosto, Giuseppe Rescinito, Gabriele Zoppoli, Michele Piana and Cristina Campi
Diagnostics 2023, 13(6), 1007; https://doi.org/10.3390/diagnostics13061007 - 7 Mar 2023
Cited by 1 | Viewed by 1613
Abstract
Radiomics and artificial intelligence have been increasingly applied in breast MRI. However, the advantages of using radiomics to evaluate lesions amenable to MR-guided vacuum-assisted breast biopsy (MR-VABB) are unclear. This study includes patients scheduled for MR-VABB, corresponding to subjects with MRI-only visible lesions, [...] Read more.
Radiomics and artificial intelligence have been increasingly applied in breast MRI. However, the advantages of using radiomics to evaluate lesions amenable to MR-guided vacuum-assisted breast biopsy (MR-VABB) are unclear. This study includes patients scheduled for MR-VABB, corresponding to subjects with MRI-only visible lesions, i.e., with a negative second-look ultrasound. The first acquisition of the multiphase dynamic contrast-enhanced MRI (DCE-MRI) sequence was selected for image segmentation and radiomics analysis. A total of 80 patients with a mean age of 55.8 years ± 11.8 (SD) were included. The dataset was then split into a training set (50 patients) and a validation set (30 patients). Twenty out of the 30 patients with a positive histology for cancer were in the training set, while the remaining 10 patients with a positive histology were included in the test set. Logistic regression on the training set provided seven features with significant p values (<0.05): (1) ‘AverageIntensity’, (2) ‘Autocorrelation’, (3) ‘Contrast’, (4) ‘Compactness’, (5) ‘StandardDeviation’, (6) ‘MeanAbsoluteDeviation’ and (7) ‘InterquartileRange’. AUC values of 0.86 (95% C.I. 0.73–0.94) for the training set and 0.73 (95% C.I. 0.54–0.87) for the test set were obtained for the radiomics model. Radiological evaluation of the same lesions scheduled for MR-VABB had AUC values of 0.42 (95% C.I. 0.28–0.57) for the training set and 0.4 (0.23–0.59) for the test set. In this study, a radiomics logistic regression model applied to DCE-MRI images increased the diagnostic accuracy of standard radiological evaluation of MRI suspicious findings in women scheduled for MR-VABB. Confirming this performance in large multicentric trials would imply that using radiomics in the assessment of patients scheduled for MR-VABB has the potential to reduce the number of biopsies, in suspicious breast lesions where MR-VABB is required, with clear advantages for patients and healthcare resources. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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11 pages, 1881 KiB  
Article
Is the Level of Contrast Enhancement on Contrast-Enhanced Mammography (CEM) Associated with the Presence and Biological Aggressiveness of Breast Cancer?
by Alaa Marzogi, Pascal A. T. Baltzer, Panagiotis Kapetas, Ruxandra I. Milos, Maria Bernathova, Thomas H. Helbich and Paola Clauser
Diagnostics 2023, 13(4), 754; https://doi.org/10.3390/diagnostics13040754 - 16 Feb 2023
Cited by 2 | Viewed by 1787
Abstract
There is limited information about whether the level of enhancement on contrast-enhanced mammography (CEM) can be used to predict malignancy. The purpose of this study was to correlate the level of enhancement with the presence of malignancy and breast cancer (BC) aggressiveness on [...] Read more.
There is limited information about whether the level of enhancement on contrast-enhanced mammography (CEM) can be used to predict malignancy. The purpose of this study was to correlate the level of enhancement with the presence of malignancy and breast cancer (BC) aggressiveness on CEM. This IRB-approved, cross-sectional, retrospective study included consecutive patients examined with CEM for unclear or suspicious findings on mammography or ultrasound. Excluded were examinations performed after biopsy or during neoadjuvant treatment for BC. Three breast radiologists who were blinded to patient data evaluated the images. The enhancement intensity was rated from 0 (no enhancement) to 3 (distinct enhancement). ROC analysis was performed. Sensitivity and negative likelihood ratio (LR-) were calculated after dichotomizing enhancement intensity as negative (0) versus positive (1–3). A total of 156 lesions (93 malignant, 63 benign) in 145 patients (mean age 59 ± 11.6 years) were included. The mean ROC curve was 0.827. Mean sensitivity was 95.4%. Mean LR- was 0.12%. Invasive cancer presented predominantly (61.8%) with distinct enhancement. A lack of enhancement was mainly observed for ductal carcinoma in situ. Stronger enhancement intensity was positively correlated with cancer aggressiveness, but the absence of enhancement should not be used to downgrade suspicious calcifications. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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11 pages, 511 KiB  
Article
Early Assessment of Neoadjuvant Chemotherapy Response Using Multiparametric Magnetic Resonance Imaging in Luminal B-like Subtype of Breast Cancer Patients: A Single-Center Prospective Study
by Lucija Kovacevic, Marko Petrovecki, Lea Korsa, Zlatko Marusic, Ivo Dumic-Cule and Maja Prutki
Diagnostics 2023, 13(4), 694; https://doi.org/10.3390/diagnostics13040694 - 12 Feb 2023
Cited by 1 | Viewed by 1556
Abstract
This study aimed to evaluate the performance of multiparametric breast magnetic resonance imaging (mpMRI) for predicting response to neoadjuvant chemotherapy (NAC) in patients with luminal B subtype breast cancer. The prospective study included thirty-five patients treated with NAC for both early and locally [...] Read more.
This study aimed to evaluate the performance of multiparametric breast magnetic resonance imaging (mpMRI) for predicting response to neoadjuvant chemotherapy (NAC) in patients with luminal B subtype breast cancer. The prospective study included thirty-five patients treated with NAC for both early and locally advanced breast cancer of the luminal B subtype at the University Hospital Centre Zagreb between January 2015 and December 2018. All patients underwent breast mpMRI before and after two cycles of NAC. Evaluation of mpMRI examinations included analysis of both morphological (shape, margins, and pattern of enhancement) and kinetic characteristics (initial signal increase and post-initial behavior of the time-signal intensity curve), which were additionally interpreted with a Göttingen score (GS). Histopathological analysis of surgical specimens included grading the tumor response based on the residual cancer burden (RCB) grading system and revealed 29 NAC responders (RCB-0 (pCR), I, II) and 6 NAC non-responders (RCB-III). Changes in GS were compared with RCB classes. A lack of GS decrease after the second cycle of NAC is associated with RCB class and non-responders to NAC. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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14 pages, 867 KiB  
Article
Precision Medicine in Breast Cancer: Do MRI Biomarkers Identify Patients Who Truly Benefit from the Oncotype DX Recurrence Score® Test?
by Francesca Galati, Valentina Magri, Giuliana Moffa, Veronica Rizzo, Andrea Botticelli, Enrico Cortesi and Federica Pediconi
Diagnostics 2022, 12(11), 2730; https://doi.org/10.3390/diagnostics12112730 - 8 Nov 2022
Cited by 1 | Viewed by 1702
Abstract
The aim of this study was to combine breast MRI-derived biomarkers with clinical-pathological parameters to identify patients who truly need an Oncotype DX Breast Recurrence Score® (ODXRS) genomic assay, currently used to predict the benefit of adjuvant chemotherapy in ER-positive/HER2-negative early breast [...] Read more.
The aim of this study was to combine breast MRI-derived biomarkers with clinical-pathological parameters to identify patients who truly need an Oncotype DX Breast Recurrence Score® (ODXRS) genomic assay, currently used to predict the benefit of adjuvant chemotherapy in ER-positive/HER2-negative early breast cancer, with the ultimate goal of customizing therapeutic decisions while reducing healthcare costs. Patients who underwent a preoperative multiparametric MRI of the breast and ODXRS tumor profiling were retrospectively included in this study. Imaging sets were evaluated independently by two breast radiologists and classified according to the 2013 American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) lexicon. In a second step of the study, a combined oncologic and radiologic assessment based on clinical-pathological and radiological data was performed, in order to identify patients who may need adjuvant chemotherapy. Results were correlated with risk levels expressed by ODXRS, using the decision made on the basis of the ODXRS test as a gold standard. The χ2 test was used to evaluate associations between categorical variables, and significant ones were further investigated using logistic regression analyses. A total of 58 luminal-like, early-stage breast cancers were included. A positive correlation was found between ODXRS and tumor size (p = 0.003), staging (p = 0.001) and grading (p = 0.005), and between BI-RADS categories and ODXRS (p < 0.05 for both readers), the latter being confirmed at multivariate regression analysis. Moreover, BI-RADS categories proved to be positive predictors of the therapeutic decision taken after performing an ODXRS assay. A statistically significant association was also found between the therapeutic decision based on the ODXRS and the results of combined onco-radiologic assessment (p < 0.001). Our study suggests that there is a correlation between BI-RADS categories at MRI and ODXRS and that a combined onco-radiological assessment may predict the decision made on the basis of the results of ODXRS genomic test. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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13 pages, 3679 KiB  
Article
An Innovative Concept of a 3D-Coded Aperture Imaging System Proposed for Early Breast Cancer Detection
by Khalid Hussain, Mohammed A. Alnafea, M Iqbal Saripan, Djelloul Mahboub, Rozi Mahmud, Wan Azizun Wan Adnan and Dong Xianling
Diagnostics 2022, 12(10), 2529; https://doi.org/10.3390/diagnostics12102529 - 18 Oct 2022
Cited by 1 | Viewed by 1722
Abstract
Coded Aperture (CA) imaging has recently been used in nuclear medicine, but still, there is no commercial SPECT imaging camera based on CA for cancer detection. The literature is rich in examples of using the CA for planar and thin 3D imaging. However, [...] Read more.
Coded Aperture (CA) imaging has recently been used in nuclear medicine, but still, there is no commercial SPECT imaging camera based on CA for cancer detection. The literature is rich in examples of using the CA for planar and thin 3D imaging. However, thick 3D reconstruction is still challenging for small lesion detection. This paper presents the results of mosaic modified uniformly redundant array (MURA) mask/antimask CA combined with a maximum-likelihood expectation-maximization (MLEM) algorithm. The MLEM is an iterative algorithm applied to a mosaic MURA CA mask/antimask for 3D anthropomorphic breast phantom reconstruction, slice by slice. The difference between the mask and the antimask suppresses the background noise to enhance the quality of reconstructed images. Furthermore, all reconstructed slices are stacked to form a 3D breast phantom image from single-projection data. The results of phantom reconstruction with 8 mm, 6 mm, 4 mm, and 3 mm lesions are presented. Moreover, the proposed SPECT imaging camera can reconstruct a 3D breast phantom from single-projection data of the patient’s scanning. To assess the quality of lesions in the reconstructed images, the contrast-to-background ratio (CBR), the peak signal-to-noise ratio (PSNR) and mean square error (MSE) were measured. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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9 pages, 2413 KiB  
Article
Percutaneous CT-Guided Bone Lesion Biopsy for Confirmation of Bone Metastases in Patients with Breast Cancer
by Lucija Kovacevic, Mislav Cavka, Zlatko Marusic, Elvira Kresic, Andrija Stajduhar, Lora Grbanovic, Ivo Dumic-Cule and Maja Prutki
Diagnostics 2022, 12(9), 2094; https://doi.org/10.3390/diagnostics12092094 - 29 Aug 2022
Cited by 3 | Viewed by 1562
Abstract
We aimed to determine diagnostic accuracy of CT-guided bone lesion biopsy for the confirmation of bone metastases in patients with breast cancer and assessment of hormone receptor status in metastatic tissue. A total of 56 female patients with breast cancer that underwent CT-guided [...] Read more.
We aimed to determine diagnostic accuracy of CT-guided bone lesion biopsy for the confirmation of bone metastases in patients with breast cancer and assessment of hormone receptor status in metastatic tissue. A total of 56 female patients with breast cancer that underwent CT-guided biopsy of suspected bone metastasis were enrolled in this retrospective study. Three different techniques were employed to obtain samples from various sites of skeleton. Collectively, 11 true negative and 3 false negative findings were revealed. The sensitivity of CT-guided biopsy for diagnosing bone metastases was 93.6%, specificity was 100% and accuracy was 94.8%. Discordance in progesterone receptor status and complete concordance in estrogen receptor status was observed. Based on our single-center experience, bone metastasis biopsy should be routinely performed in patients with breast cancer and suspicious bone lesions, due to the impact on further treatment. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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Review

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15 pages, 3359 KiB  
Review
Male Breast: A Review of the Literature and Current State of the Art of Diagnostic Imaging Work-Up
by Anna D’Angelo, Antonio Portaluri, Flavia Caprini, Carmelo Sofia, Francesca Ferrara, Elvira Condorelli, Ludovica Iaccarino, Francesca Catanzariti, Matteo Mancino, Charlotte M. L. Trombadori, Paolo Belli and Maria Adele Marino
Diagnostics 2023, 13(24), 3620; https://doi.org/10.3390/diagnostics13243620 - 7 Dec 2023
Viewed by 1350
Abstract
Pathological conditions affecting the male breast (MB) share some similarities with those found in women, while others are specific to men. The first part of this review provides an overview of MB disorders, exploring the most common types of MB diseases. The second [...] Read more.
Pathological conditions affecting the male breast (MB) share some similarities with those found in women, while others are specific to men. The first part of this review provides an overview of MB disorders, exploring the most common types of MB diseases. The second part then emphasizes the state-of-the-art approaches proposed in the literature for screening and follow-up with MB cancer patients, which highlights the importance of tailored strategies for diagnosis, follow-up, and identifying high-risk populations. Considering the increasing attention in recent years on the topic, transgender individuals are also included in this review. Together with the MB, it is an understudied category thus far. This review aims to raise awareness among radiologists that MBs should be approached differently from female breasts, contributing to the advancement of medical knowledge, improving patient outcomes, and promoting early detection of MB disorders. The review also provides an update on breast cancer and screening in the transgender population. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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26 pages, 17119 KiB  
Review
Imaging of the Reconstructed Breast
by Theodora Kanavou, Dimitrios P. Mastorakos, Panagiotis D. Mastorakos, Eleni C. Faliakou and Alexandra Athanasiou
Diagnostics 2023, 13(20), 3186; https://doi.org/10.3390/diagnostics13203186 - 12 Oct 2023
Viewed by 1778
Abstract
The incidence of breast cancer and, therefore, the need for breast reconstruction are expected to increase. The many reconstructive options available and the changing aspects of the field make this a complex area of plastic surgery, requiring knowledge and expertise. Two major types [...] Read more.
The incidence of breast cancer and, therefore, the need for breast reconstruction are expected to increase. The many reconstructive options available and the changing aspects of the field make this a complex area of plastic surgery, requiring knowledge and expertise. Two major types of breast reconstruction can be distinguished: breast implants and autologous flaps. Both present advantages and disadvantages. Autologous fat grafting is also commonly used. MRI is the modality of choice for evaluating breast reconstruction. Knowledge of the type of reconstruction is preferable to provide the maximum amount of pertinent information and avoid false positives. Early complications include seroma, hematoma, and infection. Late complications depend on the type of reconstruction. Implant rupture and implant capsular contracture are frequently encountered. Depending on the implant type, specific MRI signs can be depicted. In the case of myocutaneous flap, fat necrosis, fibrosis, and vascular compromise represent the most common complications. Late cancer recurrence is much less common. Rarely reported late complications include breast-implant-associated large cell anaplastic lymphoma (BIA-ALCL) and, recently described and even rarer, breast-implant-associated squamous cell carcinoma (BIA-SCC). In this review article, the various types of breast reconstruction will be presented, with emphasis on pertinent imaging findings and complications. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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15 pages, 4135 KiB  
Review
Pregnancy-Associated Breast Cancer: A Diagnostic and Therapeutic Challenge
by Francesca Galati, Valentina Magri, Paula Andrea Arias-Cadena, Giuliana Moffa, Veronica Rizzo, Marcella Pasculli, Andrea Botticelli and Federica Pediconi
Diagnostics 2023, 13(4), 604; https://doi.org/10.3390/diagnostics13040604 - 7 Feb 2023
Cited by 7 | Viewed by 2815
Abstract
Pregnancy-associated breast cancer (PABC) is commonly defined as a breast cancer occurring during pregnancy, throughout 1 year postpartum, or during lactation. Despite being a rare circumstance, PABC is one of the most common types of malignancies occurring during pregnancy and lactation, with growing [...] Read more.
Pregnancy-associated breast cancer (PABC) is commonly defined as a breast cancer occurring during pregnancy, throughout 1 year postpartum, or during lactation. Despite being a rare circumstance, PABC is one of the most common types of malignancies occurring during pregnancy and lactation, with growing incidence in developed countries, due both to decreasing age at onset of breast cancer and to increasing maternal age. Diagnosis and management of malignancy in the prenatal and postnatal settings are challenging for practitioners, as the structural and functional changes that the breast undergoes may be misleading for both the radiologist and the clinician. Furthermore, safety concerns for the mother and child, as well as psychological aspects in this unique and delicate condition, need to be constantly considered. In this comprehensive review, clinical, diagnostic, and therapeutic aspects of PABC (including surgery, chemotherapy and other systemic treatments, and radiotherapy) are presented and fully discussed, based on medical literature, current international clinical guidelines, and systematic practice. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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17 pages, 919 KiB  
Review
Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis—A Rapid Review
by Nicole Brunetti, Massimo Calabrese, Carlo Martinoli and Alberto Stefano Tagliafico
Diagnostics 2023, 13(1), 58; https://doi.org/10.3390/diagnostics13010058 - 26 Dec 2022
Cited by 20 | Viewed by 3397
Abstract
Background: Ultrasound (US) is a fundamental diagnostic tool in breast imaging. However, US remains an operator-dependent examination. Research into and the application of artificial intelligence (AI) in breast US are increasing. The aim of this rapid review was to assess the current development [...] Read more.
Background: Ultrasound (US) is a fundamental diagnostic tool in breast imaging. However, US remains an operator-dependent examination. Research into and the application of artificial intelligence (AI) in breast US are increasing. The aim of this rapid review was to assess the current development of US-based artificial intelligence in the field of breast cancer. Methods: Two investigators with experience in medical research performed literature searching and data extraction on PubMed. The studies included in this rapid review evaluated the role of artificial intelligence concerning BC diagnosis, prognosis, molecular subtypes of breast cancer, axillary lymph node status, and the response to neoadjuvant chemotherapy. The mean values of sensitivity, specificity, and AUC were calculated for the main study categories with a meta-analytical approach. Results: A total of 58 main studies, all published after 2017, were included. Only 9/58 studies were prospective (15.5%); 13/58 studies (22.4%) used an ML approach. The vast majority (77.6%) used DL systems. Most studies were conducted for the diagnosis or classification of BC (55.1%). At present, all the included studies showed that AI has excellent performance in breast cancer diagnosis, prognosis, and treatment strategy. Conclusions: US-based AI has great potential and research value in the field of breast cancer diagnosis, treatment, and prognosis. More prospective and multicenter studies are needed to assess the potential impact of AI in breast ultrasound. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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Jump to: Research, Review

11 pages, 2559 KiB  
Protocol
A Multicentric, Single Arm, Prospective, Stratified Clinical Investigation to Confirm MammoWave’s Ability in Breast Lesions Detection
by Daniel Álvarez Sánchez-Bayuela, Navid Ghavami, Cristina Romero Castellano, Alessandra Bigotti, Mario Badia, Lorenzo Papini, Giovanni Raspa, Gianmarco Palomba, Mohammad Ghavami, Riccardo Loretoni, Massimo Calabrese, Alberto Tagliafico and Gianluigi Tiberi
Diagnostics 2023, 13(12), 2100; https://doi.org/10.3390/diagnostics13122100 - 17 Jun 2023
Cited by 2 | Viewed by 1505
Abstract
Novel techniques, such as microwave imaging, have been implemented in different prototypes and are under clinical validation, especially for breast cancer detection, due to their harmless technology and possible clinical advantages over conventional imaging techniques. In the prospective study presented in this work, [...] Read more.
Novel techniques, such as microwave imaging, have been implemented in different prototypes and are under clinical validation, especially for breast cancer detection, due to their harmless technology and possible clinical advantages over conventional imaging techniques. In the prospective study presented in this work, we aim to investigate through a multicentric European clinical trial (ClinicalTrials.gov Identifier NCT05300464) the effectiveness of the MammoWave microwave imaging device, which uses a Huygens-principle-based radar algorithm for image reconstruction and comprises dedicated image analysis software. A detailed clinical protocol has been prepared outlining all aspects of this study, which will involve adult females having a radiologist study output obtained using conventional exams (mammography and/or ultrasound and/or magnetic resonance imaging) within the previous month. A maximum number of 600 volunteers will be recruited at three centres in Italy and Spain, where they will be asked to sign an informed consent form prior to the MammoWave scan. Conductivity weighted microwave images, representing the homogeneity of the tissues’ dielectric properties, will be created for each breast, using a conductivity = 0.3 S/m. Subsequently, several microwave image parameters (features) will be used to quantify the images’ non-homogenous behaviour. A selection of these features is expected to allow for distinction between breasts with lesions (either benign or malignant) and those without radiological findings. For all the selected features, we will use Welch’s t-test to verify the statistical significance, using the gold standard output of the radiological study review. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging and Treatment)
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