Research

12 pages, 886 KiB

Open AccessArticle

The Correlation between Morpho-Dynamic Contrast-Enhanced Mammography (CEM) Features and Prognostic Factors in Breast Cancer: A Single-Center Retrospective Analysis

by Claudia Lucia Piccolo, Ilenia Celli, Claudio Bandini, Manuela Tommasiello, Matteo Sammarra, Lorenzo Faggioni, Dania Cioni, Bruno Beomonte Zobel and Emanuele Neri

Cancers 2024, 16(5), 870; https://doi.org/10.3390/cancers16050870 - 22 Feb 2024

Viewed by 801

Abstract

Breast cancer, a major contributor to female mortality globally, presents challenges in detection, prompting exploration beyond digital mammography. Contrast-Enhanced Mammography (CEM), integrating morphological and functional information, emerges as a promising alternative, offering advantages in cost-effectiveness and reduced anxiety compared to MRI. This study [...] Read more.

Breast cancer, a major contributor to female mortality globally, presents challenges in detection, prompting exploration beyond digital mammography. Contrast-Enhanced Mammography (CEM), integrating morphological and functional information, emerges as a promising alternative, offering advantages in cost-effectiveness and reduced anxiety compared to MRI. This study investigates CEM’s correlation with breast cancer prognostic factors, encompassing histology, grade, and molecular markers. In a retrospective analysis involving 114 women, CEM revealed diverse lesion characteristics. Statistical analyses identified correlations between specific CEM features, such as spiculated margins and irregular shape, and prognostic factors like tumor grade and molecular markers. Notably, spiculated margins predicted lower grade and HER2 status, while irregular shape correlated with PgR and Ki-67 status. The study emphasizes CEM’s potential in predicting breast cancer prognosis, shedding light on tumor behavior. Despite the limitations, including sample size and single-observer analysis, the findings advocate for CEM’s role in stratifying breast cancers based on biological characteristics. CEM features, particularly spiculated margins, irregular shape, and enhancement dynamics, may serve as valuable indicators for personalized treatment decisions. Further research is crucial to validate these correlations and enhance CEM’s clinical utility in breast cancer assessment. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Current Trends and Future Direction)

► Show Figures

Figure 1

14 pages, 1862 KiB

Open AccessArticle

Discrepancy between Tumor Size Assessed by Full-Field Digital Mammography or Ultrasonography (cT) and Pathology (pT) in a Multicenter Series of Breast Metaplastic Carcinoma Patients

by Mirosława Püsküllüoğlu, Katarzyna Świderska, Aleksandra Konieczna, Wojciech Rudnicki, Renata Pacholczak-Madej, Michał Kunkiel, Aleksandra Grela-Wojewoda, Anna Mucha-Małecka, Jerzy W. Mituś, Ewa Stobiecka, Janusz Ryś, Michał Jarząb and Marek Ziobro

Cancers 2024, 16(1), 188; https://doi.org/10.3390/cancers16010188 - 30 Dec 2023

Viewed by 851

Abstract

Metaplastic breast cancer (BC-Mp) presents diagnostic and therapeutic complexities, with scant literature available. Correct assessment of tumor size by ultrasound (US) and full-field digital mammography (FFDM) is crucial for treatment planning. Methods: A retrospective cohort study was conducted on databases encompassing records of [...] Read more.

Metaplastic breast cancer (BC-Mp) presents diagnostic and therapeutic complexities, with scant literature available. Correct assessment of tumor size by ultrasound (US) and full-field digital mammography (FFDM) is crucial for treatment planning. Methods: A retrospective cohort study was conducted on databases encompassing records of BC patients (2012–2022) at the National Research Institutes of Oncology (Warsaw, Gliwice and Krakow Branches). Inclusion criteria comprised confirmed diagnosis in postsurgical pathology reports with tumor size details (pT) and availability of tumor size from preoperative US and/or FFDM. Patients subjected to neoadjuvant systemic treatment were excluded. Demographics and clinicopathological data were gathered. Results: Forty-five females were included. A total of 86.7% were triple-negative. The median age was 66 years (range: 33–89). The median pT was 41.63 mm (6–130), and eight patients were N-positive. Median tumor size assessed by US and FFDM was 31.81 mm (9–100) and 34.14 mm (0–120), respectively. Neither technique demonstrated superiority (p > 0.05), but they both underestimated the tumor size (p = 0.002 for US and p = 0.018 for FFDM). Smaller tumors (pT1-2) were statistically more accurately assessed by any technique (p < 0.001). Only pT correlated with overall survival. Conclusion: The risk of underestimation in tumor size assessment with US and FFDM has to be taken into consideration while planning surgical procedures for BC-Mp. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Current Trends and Future Direction)

► Show Figures

Figure 1

15 pages, 3039 KiB

Open AccessArticle

Correlation between Imaging Markers Derived from PET/MRI and Invasive Acquired Biomarkers in Newly Diagnosed Breast Cancer

by Kai Jannusch, Ann-Kathrin Bittner, Nils Martin Bruckmann, Janna Morawitz, Cleo Stieglitz, Frederic Dietzel, Harald H. Quick, Hideo A. Baba, Ken Herrmann, Lale Umutlu, Gerald Antoch, Julian Kirchner, Sabine Kasimir-Bauer and Oliver Hoffmann

Cancers 2023, 15(6), 1651; https://doi.org/10.3390/cancers15061651 - 08 Mar 2023

Cited by 2 | Viewed by 1410

Abstract

Purpose: Evaluate the diagnostic potential of [¹⁸F]FDG-PET/MRI data compared with invasive acquired biomarkers in newly diagnosed early breast cancer (BC). Methods: Altogether 169 women with newly diagnosed BC were included. All underwent a breast- and whole-body [¹⁸F]FDG-PET/MRI for initial [...] Read more.

Purpose: Evaluate the diagnostic potential of [¹⁸F]FDG-PET/MRI data compared with invasive acquired biomarkers in newly diagnosed early breast cancer (BC). Methods: Altogether 169 women with newly diagnosed BC were included. All underwent a breast- and whole-body [¹⁸F]FDG-PET/MRI for initial staging. A tumor-adapted volume of interest was placed in the primaries and defined bone regions on each standard uptake value (SUV)/apparent diffusion coefficient (ADC) dataset. Immunohistochemical markers, molecular subtype, tumor grading, and disseminated tumor cells (DTCs) of each patient were assessed after ultrasound-guided biopsy of the primaries and bone marrow (BM) aspiration. Correlation analysis and group comparisons were assessed. Results: A significant inverse correlation of estrogen-receptor (ER) expression and progesterone-receptor (PR) expression towards SUV_max was found (ER: r = 0.27, p < 0.01; PR: r = 0.19, p < 0.05). HER2-receptor expression showed no significant correlation towards SUV and ADC values. A significant positive correlation between Ki67 and SUVmax and SUV_mean (r = 0.42 p < 0.01; r = 0.19 p < 0.05) was shown. Tumor grading significantly correlated with SUV_max and SUV_mean (ρ = 0.36 and ρ = 0.39, both p’s < 0.01). There were no group differences between SUV/ADC values of DTC-positive/-negative patients. Conclusions: [¹⁸F]FDG-PET/MRI may give a first impression of BC-receptor status and BC-tumor biology during initial staging by measuring glucose metabolism but cannot distinguish between DTC-positive/-negative patients and replace biopsy. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Current Trends and Future Direction)

► Show Figures

Figure 1

13 pages, 1253 KiB

Open AccessArticle

Diagnostic and Practical Value of Abbreviated Contrast Enhanced Magnetic Resonance Imaging in Breast Cancer Diagnostics

by Martin Drinković, Ivan Drinković, Dražen Milevčić, Filip Matijević, Vlatka Drinković, Antonio Markotić, Tade Tadić and Davor Plavec

Cancers 2022, 14(22), 5645; https://doi.org/10.3390/cancers14225645 - 17 Nov 2022

Cited by 1 | Viewed by 2475

Abstract

Background: Although MRI is the most efficient method of detecting breast cancer, its standard protocol is time-consuming and expensive. The objective of this study was to compare the diagnostic accuracy of the modified innovative abbreviated MRI protocol (AMRP) and the standard magnetic resonance [...] Read more.

Background: Although MRI is the most efficient method of detecting breast cancer, its standard protocol is time-consuming and expensive. The objective of this study was to compare the diagnostic accuracy of the modified innovative abbreviated MRI protocol (AMRP) and the standard magnetic resonance protocol (SMRP) when detecting breast cancer. Methods: The research involved 477 patients referred for breast MRI due to suspected lesions. They were randomly assigned to the AMRP group (N = 232) or the SMRP group (N = 245). The AMRP comprised one native (contrast-free) and four post-contrast dynamic sequences of T1-weighted volume imaging for breast assessment (VIBRANT) and 3d MIP (maximum intensity projection) lasting for eight minutes. All the patients underwent a core biopsy of their lesions and histopathological analysis. Results: The groups were comparable regarding the pre-screening and post-diagnostic characteristics and were of average (±SD) age at breast cancer diagnosis of 53.6 ± 12.7 years. There was no significant difference between the two protocols in terms of specificity or sensitivity of breast cancer diagnosis. The sensitivity (95% Cis) of the AMRP was 99.05% (96.6–99.9%), and its specificity was 59.09% (36.4–79.3%), whereas the sensitivity of the SMRP was 98.12% (95.3–99.5%) and its specificity was 68.75% (50.0–83.9%). Most of the tumors comprised one solid lesion in one of the breasts (77.3%), followed by multicentric tumors (16%), bilateral tumors (4.3%), and multifocal tumors (1.7%). The average size of tumors was approximately 14 mm (ranging from 3 mm to 72 mm). Conclusion: Our innovative AMR protocol showed comparable specificity and sensitivity for the diagnosis of breast cancer when compared to SMRP, which is the “gold standard” for histopathological diagnosis. This can lead to great savings in terms of the time and cost of imaging and interpretation. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Current Trends and Future Direction)

► Show Figures

Figure 1

15 pages, 4294 KiB

Open AccessArticle

Radiomic Signatures Derived from Hybrid Contrast-Enhanced Ultrasound Images (CEUS) for the Assessment of Histological Characteristics of Breast Cancer: A Pilot Study

by Ioana Bene, Anca Ileana Ciurea, Cristiana Augusta Ciortea, Paul Andrei Ștefan, Larisa Dorina Ciule, Roxana Adelina Lupean and Sorin Marian Dudea

Cancers 2022, 14(16), 3905; https://doi.org/10.3390/cancers14163905 - 12 Aug 2022

Cited by 4 | Viewed by 1695

Abstract

The purpose of this study was to evaluate the diagnostic performance of radiomic features extracted from standardized hybrid contrast-enhanced ultrasound (CEUS) data for the assessment of hormone receptor status, human epidermal growth factor receptor 2 (HER2) status, tumor grade and Ki-67 in patients [...] Read more.

The purpose of this study was to evaluate the diagnostic performance of radiomic features extracted from standardized hybrid contrast-enhanced ultrasound (CEUS) data for the assessment of hormone receptor status, human epidermal growth factor receptor 2 (HER2) status, tumor grade and Ki-67 in patients with primary breast cancer. Methods: This prospective study included 72 patients with biopsy-proven breast cancer who underwent CEUS examinations between October 2020 and September 2021. Results: A radiomic analysis found the WavEnHH_s_4 parameter as an independent predictor associated with the HER2+ status with 76.92% sensitivity, and 64.41% specificity and a prediction model that could differentiate between the HER2 entities with 76.92% sensitivity and 84.75% specificity. The RWavEnLH_s-4 parameter was an independent predictor for estrogen receptor (ER) status with 55.93% sensitivity and 84.62% specificity, while a prediction model (RPerc01, RPerc10 and RWavEnLH_s_4) could differentiate between the progesterone receptor (PR) status with 44.74% sensitivity and 88.24% specificity. No texture parameter showed statistically significant results at the univariate analysis when comparing the Nottingham grade and the Ki-67 status. Conclusion: Our preliminary data indicate a potential that hybrid CEUS radiomic features allow the discrimination between breast cancers of different receptor and HER2 statuses with high specificity. Hybrid CEUS radiomic features might have the potential to provide a noninvasive, easily accessible and contrast-agent-safe method to assess tumor biology before and during treatment. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Current Trends and Future Direction)

► Show Figures

Figure 1

11 pages, 1581 KiB

Open AccessArticle

Lung Nodules Missed in Initial Staging of Breast Cancer Patients in PET/MRI—Clinically Relevant?

by Kai Jannusch, Nils Martin Bruckmann, Charlotte Johanna Geuting, Janna Morawitz, Frederic Dietzel, Christoph Rischpler, Ken Herrmann, Ann-Kathrin Bittner, Oliver Hoffmann, Svjetlana Mohrmann, Harald H. Quick, Lale Umutlu, Gerald Antoch and Julian Kirchner

Cancers 2022, 14(14), 3454; https://doi.org/10.3390/cancers14143454 - 15 Jul 2022

Cited by 1 | Viewed by 2267

Abstract

Purpose: The evaluation of the clinical relevance of missed lung nodules at initial staging of breast cancer patients in [¹⁸F]FDG-PET/MRI compared with CT. Methods: A total of 152 patients underwent an initial whole-body [¹⁸F]FDG-PET/MRI and a thoracoabdominal CT for [...] Read more.

Purpose: The evaluation of the clinical relevance of missed lung nodules at initial staging of breast cancer patients in [¹⁸F]FDG-PET/MRI compared with CT. Methods: A total of 152 patients underwent an initial whole-body [¹⁸F]FDG-PET/MRI and a thoracoabdominal CT for staging. Presence, size, shape and location for each lung nodule in [¹⁸F]FDG-PET/MRI was noted. The reference standard was established by taking initial CT and follow-up imaging into account (a two-step approach) to identify clinically-relevant lung nodules. Patient-based and lesion-based data analysis was performed. Results: No patient with clinically-relevant lung nodules was missed on a patient-based analysis with MRI VIBE, while 1/84 females was missed with MRI HASTE (1%). Lesion-based analysis revealed 4/96 (4%, VIBE) and 8/138 (6%, HASTE) missed clinically-relevant lung nodules. The average size of missed lung nodules was 3.2 mm ± 1.2 mm (VIBE) and 3.6 mm ± 1.4 mm (HASTE) and the predominant location was in the left lower quadrant and close to the hilum. Conclusion: All patients with newly-diagnosed breast cancer and clinically-relevant lung nodules were detected at initial [¹⁸F]FDG-PET/MRI staging. However, due to the lower sensitivity in detecting lung nodules, a small proportion of clinically-relevant lung nodules were missed. Thus, supplemental low-dose chest CT after neoadjuvant therapy should be considered for backup. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Current Trends and Future Direction)

► Show Figures

Figure 1

14 pages, 1735 KiB

Open AccessArticle

Radiation Dose of Contrast-Enhanced Mammography: A Two-Center Prospective Comparison

by Gisella Gennaro, Andrea Cozzi, Simone Schiaffino, Francesco Sardanelli and Francesca Caumo

Cancers 2022, 14(7), 1774; https://doi.org/10.3390/cancers14071774 - 31 Mar 2022

Cited by 16 | Viewed by 1904

Abstract

The radiation dose associated with contrast-enhanced mammography (CEM) has been investigated only by single-center studies. In this retrospective study, we aimed to compare the radiation dose between two centers performing CEM within two prospective studies, using the same type of equipment. The CEM [...] Read more.

The radiation dose associated with contrast-enhanced mammography (CEM) has been investigated only by single-center studies. In this retrospective study, we aimed to compare the radiation dose between two centers performing CEM within two prospective studies, using the same type of equipment. The CEM mean glandular dose (MGD) was computed for low energy (LE) and high energy (HE) images and their sum was calculated for each view. MGD and related parameters (entrance dose, breast thickness, compression, and density) were compared between the two centers using the Mann–Whitney test. Finally, per-patient MGD was calculated by pooling the two datasets and determining the contribution of LE and HE images. A total of 348 CEM examinations were analyzed (228 from Center 1 and 120 from Center 2). The median total MGD per view was 2.33 mGy (interquartile range 2.19–2.51 mGy) at Center 1 and 2.46 mGy (interquartile range 2.32–2.70 mGy) at Center 2, with a 0.15 mGy median difference (p < 0.001) equal to 6.2%. LE-images contributed between 64% and 77% to the total patient dose in CEM, with the remaining 23–36% being associated with HE images. The mean radiation dose for a two-view bilateral CEM exam was 4.90 mGy, about 30% higher than for digital mammography. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Current Trends and Future Direction)

► Show Figures

Figure 1

23 pages, 1541 KiB

Open AccessArticle

Patchless Multi-Stage Transfer Learning for Improved Mammographic Breast Mass Classification

by Gelan Ayana, Jinhyung Park and Se-woon Choe

Cancers 2022, 14(5), 1280; https://doi.org/10.3390/cancers14051280 - 01 Mar 2022

Cited by 17 | Viewed by 3570

Abstract

Despite great achievements in classifying mammographic breast-mass images via deep-learning (DL), obtaining large amounts of training data and ensuring generalizations across different datasets with robust and well-optimized algorithms remain a challenge. ImageNet-based transfer learning (TL) and patch classifiers have been utilized to address [...] Read more.

Despite great achievements in classifying mammographic breast-mass images via deep-learning (DL), obtaining large amounts of training data and ensuring generalizations across different datasets with robust and well-optimized algorithms remain a challenge. ImageNet-based transfer learning (TL) and patch classifiers have been utilized to address these challenges. However, researchers have been unable to achieve the desired performance for DL to be used as a standalone tool. In this study, we propose a novel multi-stage TL from ImageNet and cancer cell line image pre-trained models to classify mammographic breast masses as either benign or malignant. We trained our model on three public datasets: Digital Database for Screening Mammography (DDSM), INbreast, and Mammographic Image Analysis Society (MIAS). In addition, a mixed dataset of the images from these three datasets was used to train the model. We obtained an average five-fold cross validation AUC of 1, 0.9994, 0.9993, and 0.9998 for DDSM, INbreast, MIAS, and mixed datasets, respectively. Moreover, the observed performance improvement using our method against the patch-based method was statistically significant, with a p-value of 0.0029. Furthermore, our patchless approach performed better than patch- and whole image-based methods, improving test accuracy by 8% (91.41% vs. 99.34%), tested on the INbreast dataset. The proposed method is of significant importance in solving the need for a large training dataset as well as reducing the computational burden in training and implementing the mammography-based deep-learning models for early diagnosis of breast cancer. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Current Trends and Future Direction)

► Show Figures

Figure 1

8 pages, 507 KiB

Open AccessArticle

Gastrin-Releasing Peptide Receptor Antagonist [⁶⁸Ga]RM2 PET/CT for Staging of Pre-Treated, Metastasized Breast Cancer

by Kerstin Michalski, Lars Kemna, Jasmin Asberger, Anca L. Grosu, Philipp T. Meyer, Juri Ruf and Tanja Sprave

Cancers 2021, 13(23), 6106; https://doi.org/10.3390/cancers13236106 - 03 Dec 2021

Cited by 11 | Viewed by 2162

Abstract

Background: Positron emission tomography (PET)/computed tomography (CT) using the gastrin-releasing peptide receptor antagonist [⁶⁸Ga]RM2 has shown to be a promising imaging method for primary breast cancer (BC) with positive estrogen receptor (ER) status. This study assessed tumor visualization by [⁶⁸ [...] Read more.

Background: Positron emission tomography (PET)/computed tomography (CT) using the gastrin-releasing peptide receptor antagonist [⁶⁸Ga]RM2 has shown to be a promising imaging method for primary breast cancer (BC) with positive estrogen receptor (ER) status. This study assessed tumor visualization by [⁶⁸Ga]RM2 PET/CT in patients with pre-treated ER-positive BC and suspected metastases. Methods: This retrospective pilot study included eight female patients with initial ER-positive, pre-treated BC who underwent [⁶⁸Ga]RM2 PET/CT. Most of these patients (seven out of eight; 88%) were still being treated with or had received endocrine therapy. [⁶⁸Ga]RM2 PET/CTs were visually analyzed by two nuclear medicine specialists in consensus. Tumor manifestations were rated qualitatively (i.e., RM2-positive or RM2-negative) and quantitatively using the maximum standardized uptake value (SUVmax). SUVmax values were compared between the two subgroups (RM2-positive vs. RM2-negative). Results: Strong RM2 binding was found in all metastatic lesions of six patients (75%), whereas tracer uptake in all metastases of two patients (25%) was rated negative. Mean SUVmax of RM2-positive metastases with the highest SUVmax per patient (in lymph node and bone metastases; 15.8 ± 15.1 range: 3.7–47.8) was higher than mean SUVmax of the RM2-negative metastases with the highest SUVmax per patient (in bone metastases; 1.6 ± 0.1, range 1.5–1.7). Conclusions: Our data suggest that RM2 binding is maintained in the majority of patients with advanced disease stage of pre-treated ER-positive BC. Thus, [⁶⁸Ga]RM2 PET/CT could support treatment decision in these patients, radiotherapy planning in oligometastatic patients or selection of patients for RM2 radioligand therapy. Further studies with larger patient cohorts are warranted to confirm these findings. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Current Trends and Future Direction)

► Show Figures

Graphical abstract

Review

Jump to: Research, Other

41 pages, 2100 KiB

Open AccessReview

Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis

by Humayra Afrin, Nicholas B. Larson, Mostafa Fatemi and Azra Alizad

Cancers 2023, 15(12), 3139; https://doi.org/10.3390/cancers15123139 - 10 Jun 2023

Cited by 4 | Viewed by 2515

Abstract

Breast cancer is the second-leading cause of mortality among women around the world. Ultrasound (US) is one of the noninvasive imaging modalities used to diagnose breast lesions and monitor the prognosis of cancer patients. It has the highest sensitivity for diagnosing breast masses, [...] Read more.

Breast cancer is the second-leading cause of mortality among women around the world. Ultrasound (US) is one of the noninvasive imaging modalities used to diagnose breast lesions and monitor the prognosis of cancer patients. It has the highest sensitivity for diagnosing breast masses, but it shows increased false negativity due to its high operator dependency. Underserved areas do not have sufficient US expertise to diagnose breast lesions, resulting in delayed management of breast lesions. Deep learning neural networks may have the potential to facilitate early decision-making by physicians by rapidly yet accurately diagnosing and monitoring their prognosis. This article reviews the recent research trends on neural networks for breast mass ultrasound, including and beyond diagnosis. We discussed original research recently conducted to analyze which modes of ultrasound and which models have been used for which purposes, and where they show the best performance. Our analysis reveals that lesion classification showed the highest performance compared to those used for other purposes. We also found that fewer studies were performed for prognosis than diagnosis. We also discussed the limitations and future directions of ongoing research on neural networks for breast ultrasound. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Current Trends and Future Direction)

► Show Figures

Figure 1

18 pages, 855 KiB

Open AccessReview

Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review

by Ye-Jiao Mao, Hyo-Jung Lim, Ming Ni, Wai-Hin Yan, Duo Wai-Chi Wong and James Chung-Wai Cheung

Cancers 2022, 14(2), 367; https://doi.org/10.3390/cancers14020367 - 12 Jan 2022

Cited by 40 | Viewed by 5749

Abstract

Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further [...] Read more.

Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ³ 80%, while only half of them attained acceptable specificity ³ 95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Current Trends and Future Direction)

► Show Figures

Figure 1

36 pages, 6418 KiB

Open AccessReview

Radionuclide-Based Imaging of Breast Cancer: State of the Art

by Huiling Li, Zhen Liu, Lujie Yuan, Kevin Fan, Yongxue Zhang, Weibo Cai and Xiaoli Lan

Cancers 2021, 13(21), 5459; https://doi.org/10.3390/cancers13215459 - 30 Oct 2021

Cited by 21 | Viewed by 3777

Abstract

Breast cancer is a malignant tumor that can affect women worldwide and endanger their health and wellbeing. Early detection of breast cancer can significantly improve the prognosis and survival rate of patients, but with traditional anatomical imagine methods, it is difficult to detect [...] Read more.

Breast cancer is a malignant tumor that can affect women worldwide and endanger their health and wellbeing. Early detection of breast cancer can significantly improve the prognosis and survival rate of patients, but with traditional anatomical imagine methods, it is difficult to detect lesions before morphological changes occur. Radionuclide-based molecular imaging based on positron emission tomography (PET) and single-photon emission computed tomography (SPECT) displays its advantages for detecting breast cancer from a functional perspective. Radionuclide labeling of small metabolic compounds can be used for imaging biological processes, while radionuclide labeling of ligands/antibodies can be used for imaging receptors. Noninvasive visualization of biological processes helps elucidate the metabolic state of breast cancer, while receptor-targeted radionuclide molecular imaging is sensitive and specific for visualization of the overexpressed molecular markers in breast cancer, contributing to early diagnosis and better management of cancer patients. The rapid development of radionuclide probes aids the diagnosis of breast cancer in various aspects. These probes target metabolism, amino acid transporters, cell proliferation, hypoxia, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), gastrin-releasing peptide receptor (GRPR) and so on. This article provides an overview of the development of radionuclide molecular imaging techniques present in preclinical or clinical studies, which are used as tools for early breast cancer diagnosis. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Current Trends and Future Direction)

► Show Figures

Graphical abstract

Other

Jump to: Research, Review

12 pages, 241 KiB

Open AccessSystematic Review

A Systematic Review of Breast Implant-Associated Squamous Cell Carcinoma

by Sujan Niraula, Anjan Katel, Amit Barua, Anna Weiss, Myla S. Strawderman, Huina Zhang, Oscar Manrique, Avice O’Connell, Sirish Raj Pandey and Ajay Dhakal

Cancers 2023, 15(18), 4516; https://doi.org/10.3390/cancers15184516 - 12 Sep 2023

Cited by 2 | Viewed by 1470

Abstract

Breast augmentation is considered safe, but rare cases of breast implant-associated squamous cell carcinoma (BIA-SCC) have been reported. This study aimed to systematically review published cases of BIA-SCC, providing valuable clinical data. The review included 14 articles and 18 cases of BIA-SCC. An [...] Read more.

Breast augmentation is considered safe, but rare cases of breast implant-associated squamous cell carcinoma (BIA-SCC) have been reported. This study aimed to systematically review published cases of BIA-SCC, providing valuable clinical data. The review included 14 articles and 18 cases of BIA-SCC. An increasing trend in reported BIA-SCC cases was observed, with four cases in the 1990s and 14 cases since 2010. The mean age of affected patients was 56 years, and symptoms typically appeared around 21 years after breast augmentation. Silicone implants used in cosmetic procedures were most commonly associated with BIA-SCC. Implant removal was necessary in all cases, and some patients required a mastectomy. Treatment approaches varied, with the selective use of chemotherapy and/or radiotherapy. The estimated 6-month mortality rate was 11.1%, while the 12-month mortality rate was 23.8%. The estimated 6-month mortality rate should be cautiously interpreted due to the limited sample size. It appears lower than the rate reported by the American Society of Plastic Surgeons, without clear reasons for this discrepancy. This study highlights the importance of enhanced monitoring and information sharing to improve detection and management of BIA-SCC. Healthcare providers should maintain vigilance during the long-term follow-up of breast augmentation patients. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Current Trends and Future Direction)

10 pages, 398 KiB

Open AccessSystematic Review

Breast-Specific Gamma Imaging: An Added Value in the Diagnosis of Breast Cancer, a Systematic Review

by Maria Silvia De Feo, Marko Magdi Abdou Sidrak, Miriam Conte, Viviana Frantellizzi, Andrea Marongiu, Flaminia De Cristofaro, Susanna Nuvoli, Angela Spanu and Giuseppe De Vincentis

Cancers 2022, 14(19), 4619; https://doi.org/10.3390/cancers14194619 - 23 Sep 2022

Cited by 1 | Viewed by 1460

Abstract

Purpose: Breast cancer is the most common solid tumor and the second highest cause of death in the United States. Detection and diagnosis of breast tumors includes various imaging modalities, such as mammography (MMG), ultrasound (US), and contrast-enhancement MRI. Breast-specific gamma imaging (BSGI) [...] Read more.

Purpose: Breast cancer is the most common solid tumor and the second highest cause of death in the United States. Detection and diagnosis of breast tumors includes various imaging modalities, such as mammography (MMG), ultrasound (US), and contrast-enhancement MRI. Breast-specific gamma imaging (BSGI) is an emerging tool, whereas morphological imaging has the disadvantage of a higher absorbed dose. Our aim was to assess if this imaging method is a more valuable choice in detecting breast malignant lesions compared to morphological counterparts. Methods: research on Medline from 1995 to June 2022 was conducted. Studies that compared at least one anatomical imaging modality with BSGI were screened and assessed through QUADAS2 for risk of bias and applicability concerns assessment. Sensitivity, specificity, positive and negative predictive value (PPV and NPV) were reported. Results: A total of 15 studies compared BSGI with MMG, US, and MRI. BSGI sensitivity was similar to MRI, but specificity was higher. Specificity was always higher than MMG and US. BSGI had higher PPV and NPV. When used for the evaluation of a suspected breast lesion, the overall sensitivity was better than the examined overall sensitivity when BSGI was excluded. Risk of bias and applicability concerns domain showed mainly low risk of bias. Conclusion: BSGI is a valuable imaging modality with similar sensitivity to MRI but higher specificity, although at the cost of higher radiation burden. Full article

(This article belongs to the Special Issue Breast Cancer Imaging: Current Trends and Future Direction)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Breast Cancer Imaging: Current Trends and Future Direction

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (14 papers)

Research

Review

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI