Research

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11 pages, 799 KiB

Open AccessArticle

Impact of Breast Density Awareness on Knowledge about Breast Cancer Risk Factors and the Self-Perceived Risk of Breast Cancer

by Kristina Bojanic, Sonja Vukadin, Filip Sarcevic, Luka Malenica, Kaja Grgic, Robert Smolic, Kristina Kralik, Ines Bilic Curcic, Gordana Ivanac, George Y. Wu and Martina Smolic

Diagnostics 2020, 10(7), 496; https://doi.org/10.3390/diagnostics10070496 - 20 Jul 2020

Cited by 6 | Viewed by 2653

Abstract

Breast density (BD) reduces sensitivity of mammography, and is a strong risk factor for breast cancer (BC). Data about women’s awareness and knowledge of BD are limited. Our aim is to examine whether the BD information disclosure and BD awareness among women without [...] Read more.

Breast density (BD) reduces sensitivity of mammography, and is a strong risk factor for breast cancer (BC). Data about women’s awareness and knowledge of BD are limited. Our aim is to examine whether the BD information disclosure and BD awareness among women without BC are related to their knowledge about BC risk factors. We examined self-reported BC risk perception and its association to BD awareness and level of health literacy. A cross-sectional, single site study included 263 Croatian women without BC who had mammographic examination. Data were collected by interviews using questionnaires and a validated survey. Of the total, 77.1% had never heard of BD, and 22.9% were aware of their BD. Most participants who knew their BD (88.2%, p < 0.001) had higher levels of education. Majority of subjects (66.8%) had non-dense breasts and 33.2% had dense breasts. Subjects aware of their BD knew that post-menopausal hormone replacement therapy (p = 0.04) and higher BD (p = 0.03) are BC risk factors. They could more easily access information about health promotion (p = 0.03). High-BD informed women assessed their lifetime BC risk as significantly higher than all others (p = 0.03). Comprehension of BD awareness and knowledge is crucial for reinforcement of educational strategies and development of amendatory BC screening decisions. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging)

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11 pages, 1479 KiB

Open AccessArticle

Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging

by Maria Adele Marino, Doris Leithner, Janice Sung, Daly Avendano, Elizabeth A. Morris, Katja Pinker and Maxine S. Jochelson

Diagnostics 2020, 10(7), 492; https://doi.org/10.3390/diagnostics10070492 - 18 Jul 2020

Cited by 27 | Viewed by 4751

Abstract

The aim of our intra-individual comparison study was to investigate and compare the potential of radiomics analysis of contrast-enhanced mammography (CEM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status, and tumor [...] Read more.

The aim of our intra-individual comparison study was to investigate and compare the potential of radiomics analysis of contrast-enhanced mammography (CEM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status, and tumor grade in patients with primary breast cancer. This retrospective study included 48 female patients with 49 biopsy-proven breast cancers who underwent pretreatment breast CEM and MRI. Radiomics analysis was performed by using MaZda software. Radiomics parameters were correlated with tumor histology (invasive vs. non-invasive), hormonal status (HR+ vs. HR−), and grading (low grade G1 + G2 vs. high grade G3). CEM radiomics analysis yielded classification accuracies of up to 92% for invasive vs. non-invasive breast cancers, 95.6% for HR+ vs. HR− breast cancers, and 77.8% for G1 + G2 vs. G3 invasive cancers. MRI radiomics analysis yielded classification accuracies of up to 90% for invasive vs. non-invasive breast cancers, 82.6% for HR+ vs. HR− breast cancers, and 77.8% for G1+G2 vs. G3 cancers. Preliminary results indicate a potential of both radiomics analysis of DCE-MRI and CEM for non-invasive assessment of tumor-invasiveness, hormone receptor status, and tumor grade. CEM may serve as an alternative to MRI if MRI is not available or contraindicated. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging)

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11 pages, 5858 KiB

Open AccessArticle

Multidetector Computed Tomography with Dedicated Protocol for Breast Cancer Locoregional Staging: Feasibility Study

by Vinicius C. Felipe, Luciana Graziano, Paula N. V. P. Barbosa, Vinicius F. Calsavara and Almir G. V. Bitencourt

Diagnostics 2020, 10(7), 479; https://doi.org/10.3390/diagnostics10070479 - 14 Jul 2020

Cited by 5 | Viewed by 2638

Abstract

Background: The aim of this study was to demonstrate the feasibility of performing multidetector computed tomography (MDCT) with a dedicated protocol for locoregional staging in breast cancer patients. Methods: This prospective single-center study included newly diagnosed breast cancer patients submitted to contrast-enhanced chest [...] Read more.

Background: The aim of this study was to demonstrate the feasibility of performing multidetector computed tomography (MDCT) with a dedicated protocol for locoregional staging in breast cancer patients. Methods: This prospective single-center study included newly diagnosed breast cancer patients submitted to contrast-enhanced chest MDCT and breast magnetic resonance imaging (MRI). MDCT was performed in prone position and using subtraction techniques. Fleiss’ Kappa coefficient (K) and intraclass correlation coefficient (ICC) were used to assess agreement between MRI, MDCT, and pathology, when available. Results: Thirty-three patients were included (mean age: 47 years). Breast MRI and MDCT showed at least substantial agreement for evaluation of tumor extension (k = 0.674), presence of multifocality (k = 0.669), multicentricity (k = 0.857), nipple invasion (k = 1.000), skin invasion (k = 0.872), and suspicious level I axillary lymph nodes (k = 0.613). MDCT showed higher number of suspicious axillary lymph nodes than MRI, especially on levels II and III. Both methods had similar correlation with tumor size (MRI ICC: 0.807; p = 0.008 vs. MDCT ICC: 0.750; p = 0.020) and T staging (k = 0.699) on pathology. Conclusions: MDCT with dedicated breast protocol is feasible and showed substantial agreement with MRI features in stage II or III breast cancer patients. This method could potentially allow one-step locoregional and systemic staging, reducing costs and improving logistics for these patients. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging)

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10 pages, 2837 KiB

Open AccessArticle

Efficient Anomaly Detection with Generative Adversarial Network for Breast Ultrasound Imaging

by Tomoyuki Fujioka, Kazunori Kubota, Mio Mori, Yuka Kikuchi, Leona Katsuta, Mizuki Kimura, Emi Yamaga, Mio Adachi, Goshi Oda, Tsuyoshi Nakagawa, Yoshio Kitazume and Ukihide Tateishi

Diagnostics 2020, 10(7), 456; https://doi.org/10.3390/diagnostics10070456 - 04 Jul 2020

Cited by 26 | Viewed by 3759

Abstract

We aimed to use generative adversarial network (GAN)-based anomaly detection to diagnose images of normal tissue, benign masses, or malignant masses on breast ultrasound. We retrospectively collected 531 normal breast ultrasound images from 69 patients. Data augmentation was performed and 6372 (531 × [...] Read more.

We aimed to use generative adversarial network (GAN)-based anomaly detection to diagnose images of normal tissue, benign masses, or malignant masses on breast ultrasound. We retrospectively collected 531 normal breast ultrasound images from 69 patients. Data augmentation was performed and 6372 (531 × 12) images were available for training. Efficient GAN-based anomaly detection was used to construct a computational model to detect anomalous lesions in images and calculate abnormalities as an anomaly score. Images of 51 normal tissues, 48 benign masses, and 72 malignant masses were analyzed for the test data. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of this anomaly detection model were calculated. Malignant masses had significantly higher anomaly scores than benign masses (p < 0.001), and benign masses had significantly higher scores than normal tissues (p < 0.001). Our anomaly detection model had high sensitivities, specificities, and AUC values for distinguishing normal tissues from benign and malignant masses, with even greater values for distinguishing normal tissues from malignant masses. GAN-based anomaly detection shows high performance for the detection and diagnosis of anomalous lesions in breast ultrasound images. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging)

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13 pages, 1998 KiB

Open AccessArticle

Mammographic Breast Density and Urbanization: Interactions with BMI, Environmental, Lifestyle, and Other Patient Factors

by Nick Perry, Sue Moss, Steve Dixon, Sue Milner, Kefah Mokbel, Charlotte Lemech, Hendrik-Tobias Arkenau, Stephen Duffy and Katja Pinker

Diagnostics 2020, 10(6), 418; https://doi.org/10.3390/diagnostics10060418 - 20 Jun 2020

Cited by 2 | Viewed by 3337

Abstract

Mammographic breast density (MBD) is an important imaging biomarker of breast cancer risk, but it has been suggested that increased MBD is not a genuine finding once corrected for age and body mass index (BMI). This study examined the association of various factors, [...] Read more.

Mammographic breast density (MBD) is an important imaging biomarker of breast cancer risk, but it has been suggested that increased MBD is not a genuine finding once corrected for age and body mass index (BMI). This study examined the association of various factors, including both residing in and working in the urban setting, with MBD. Questionnaires were completed by 1144 women attending for mammography at the London Breast Institute in 2012–2013. Breast density was assessed with an automated volumetric breast density measurement system (Volpara) and compared with subjective radiologist assessment. Multivariable linear regression was used to model the relationship between MBD and residence in the urban setting as well as working in the urban setting, adjusting for both age and BMI and other menstrual, reproductive, and lifestyle factors. Urban residence was significantly associated with an increasing percent of MBD, but this association became non-significant when adjusted for age and BMI. This was not the case for women who were both residents in the urban setting and still working. Our results suggest that the association between urban women and increased MBD can be partially explained by their lower BMI, but for women still working, there appear to be other contributing factors. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging)

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11 pages, 2438 KiB

Open AccessArticle

Exploring Association of Breast Pain, Pregnancy, and Body Mass Index with Breast Tissue Elasticity in Healthy Women: Glandular and Fat Differences

by Martina Dzoic Dominkovic, Gordana Ivanac, Kristina Bojanic, Kristina Kralik, Martina Smolic, Eugen Divjak, Robert Smolic and Boris Brkljacic

Diagnostics 2020, 10(6), 393; https://doi.org/10.3390/diagnostics10060393 - 10 Jun 2020

Cited by 5 | Viewed by 3215

Abstract

Breast sonoelastography is a relatively novel ultrasound (US) method that enables estimation of tissue stiffness to estimate the elasticity of normal breast tissue and seek to correlate it with well-known breast cancer risk factors. Two hundred women of different age were included in [...] Read more.

Breast sonoelastography is a relatively novel ultrasound (US) method that enables estimation of tissue stiffness to estimate the elasticity of normal breast tissue and seek to correlate it with well-known breast cancer risk factors. Two hundred women of different age were included in the study and completed a questionnaire about personal, familiar, and reproductive history. Glandular and fatty tissue elasticity in all breast quadrants was measured by shear wave elastography (SWE). Mean elastographic values of breast tissue were calculated and compared to personal history risk factors. Elasticity of normal glandular tissue (66.4 kilopascals (kPa)) was higher than fatty tissue (26.1 kPa) in all breast quadrants and in both breasts. Lower outer quadrant (LOQ) had the lowest elasticity values of both parenchyma and fat. Higher elasticity values of breast tissue were confirmed in the left breast than in the right breast. Glandular and fat tissue elasticity negatively correlated with body mass index (BMI). Women with mastodynia had higher glandular elastographic values compared to subjects without breast pain. Nuliparity was also associated with higher elasticity of glandular breast tissue. The results of this study are promising and could, over time, contribute to a better understanding of glandular breast tissue elasticity as a potential risk factor for breast cancer. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging)

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17 pages, 2012 KiB

Open AccessArticle

Subjective Versus Quantitative Methods of Assessing Breast Density

by Wijdan Alomaim, Desiree O’Leary, John Ryan, Louise Rainford, Michael Evanoff and Shane Foley

Diagnostics 2020, 10(5), 331; https://doi.org/10.3390/diagnostics10050331 - 21 May 2020

Cited by 6 | Viewed by 3246

Abstract

In order to find a consistent, simple and time-efficient method of assessing mammographic breast density (MBD), different methods of assessing density comparing subjective, quantitative, semi-subjective and semi-quantitative methods were investigated. Subjective MBD of anonymized mammographic cases (n = 250) from a national [...] Read more.

In order to find a consistent, simple and time-efficient method of assessing mammographic breast density (MBD), different methods of assessing density comparing subjective, quantitative, semi-subjective and semi-quantitative methods were investigated. Subjective MBD of anonymized mammographic cases (n = 250) from a national breast-screening programme was rated by 49 radiologists from two countries (UK and USA) who were voluntarily recruited. Quantitatively, three measurement methods, namely VOLPARA, Hand Delineation (HD) and ImageJ (IJ) were used to calculate breast density using the same set of cases, however, for VOLPARA only mammographic cases (n = 122) with full raw digital data were included. The agreement level between methods was analysed using weighted kappa test. Agreement between UK and USA radiologists and VOLPARA varied from moderate (κw = 0.589) to substantial (κw = 0.639), respectively. The levels of agreement between USA, UK radiologists, VOLPARA with IJ were substantial (κw = 0.752, 0.768, 0.603), and with HD the levels of agreement varied from moderate to substantial (κw = 0.632, 0.680, 0.597), respectively. This study found that there is variability between subjective and objective MBD assessment methods, internationally. These results will add to the evidence base, emphasising the need for consistent, simple and time-efficient MBD assessment methods. Additionally, the quickest method to assess density is the subjective assessment, followed by VOLPARA, which is compatible with a busy clinical setting. Moreover, the use of a more limited two-scale system improves agreement levels and could help minimise any potential country bias. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging)

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9 pages, 1218 KiB

Open AccessArticle

External Validation of a Risk Stratification Score for B3 Breast Lesions Detected at Ultrasound Core Needle Biopsy

by Cristina Grippo, Pooja Jagmohan, Paola Clauser, Panagiotis Kapetas, Arthur Meier, Annabel M. Stöger, Anna D’Angelo and Pascal A. T. Baltzer

Diagnostics 2020, 10(4), 181; https://doi.org/10.3390/diagnostics10040181 - 26 Mar 2020

Cited by 4 | Viewed by 2783

Abstract

Objective: The aim of this study was to externally validate the feasibility and robustness of a risk-stratification score for B3 lesions based on clinical, pathological, and radiological data for improved clinical decision making. Methods: 129 consecutive histologically confirmed B3 lesions diagnosed at ultrasound-guided [...] Read more.

Objective: The aim of this study was to externally validate the feasibility and robustness of a risk-stratification score for B3 lesions based on clinical, pathological, and radiological data for improved clinical decision making. Methods: 129 consecutive histologically confirmed B3 lesions diagnosed at ultrasound-guided biopsy at our institution were included in this retrospective study. Patient- and lesion-related variables were independently assessed by two blinded breast radiologists (R1, R2), by assigning each feature a score from 0 to 2 (maximum sum-score of 5). Sensitivity, specificity, positive and negative predictive values were calculated at two different thresholds (≥1 and 2). Categorical variables were compared using Chi-squared and Fisher exact tests. The diagnostic accuracy of the score to distinguish benign from malignant B3 lesions was assessed by receiver operating characteristic (ROC) analysis. Results: Surgery was performed on 117/129 (90.6%) lesions and 11 of these 117 (9.4%) lesions were malignant. No cancers were found at follow-up of at least 24 months. Area under the ROC-curve was 0.736 (R1) to 0.747 (R2), with no significant difference between the two readers (p = 0.5015). Using a threshold of ≥1, a sensitivity, specificity, PPV, and NPV of 90%/90% (R1/R2), 39%/38% (R1/R2), 11%/12% (R1/R2) and 97%/98% (R1/R2) were identified. Both readers classified 47 lesions with a score ≤1 (low risk of associated malignancy). Of these, only one malignant lesion was underdiagnosed (Ductal carcinoma in situ-G1). Conclusions: In our external validation, the score showed a high negative predictive value and has the potential to reduce unnecessary surgeries or re-biopsies for ultrasound-detected B3-lesions by up to 39%. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging)

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9 pages, 1148 KiB

Open AccessArticle

Breast Ultrasound Image Synthesis using Deep Convolutional Generative Adversarial Networks

by Tomoyuki Fujioka, Mio Mori, Kazunori Kubota, Yuka Kikuchi, Leona Katsuta, Mio Adachi, Goshi Oda, Tsuyoshi Nakagawa, Yoshio Kitazume and Ukihide Tateishi

Diagnostics 2019, 9(4), 176; https://doi.org/10.3390/diagnostics9040176 - 06 Nov 2019

Cited by 32 | Viewed by 4377

Abstract

Deep convolutional generative adversarial networks (DCGANs) are newly developed tools for generating synthesized images. To determine the clinical utility of synthesized images, we generated breast ultrasound images and assessed their quality and clinical value. After retrospectively collecting 528 images of 144 benign masses [...] Read more.

Deep convolutional generative adversarial networks (DCGANs) are newly developed tools for generating synthesized images. To determine the clinical utility of synthesized images, we generated breast ultrasound images and assessed their quality and clinical value. After retrospectively collecting 528 images of 144 benign masses and 529 images of 216 malignant masses in the breasts, synthesized images were generated using a DCGAN with 50, 100, 200, 500, and 1000 epochs. The synthesized (n = 20) and original (n = 40) images were evaluated by two radiologists, who scored them for overall quality, definition of anatomic structures, and visualization of the masses on a five-point scale. They also scored the possibility of images being original. Although there was no significant difference between the images synthesized with 1000 and 500 epochs, the latter were evaluated as being of higher quality than all other images. Moreover, 2.5%, 0%, 12.5%, 37.5%, and 22.5% of the images synthesized with 50, 100, 200, 500, and 1000 epochs, respectively, and 14% of the original images were indistinguishable from one another. Interobserver agreement was very good (|r| = 0.708–0.825, p < 0.001). Therefore, DCGAN can generate high-quality and realistic synthesized breast ultrasound images that are indistinguishable from the original images. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging)

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26 pages, 2397 KiB

Open AccessArticle

Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

by Dina A. Ragab, Maha Sharkas and Omneya Attallah

Diagnostics 2019, 9(4), 165; https://doi.org/10.3390/diagnostics9040165 - 26 Oct 2019

Cited by 41 | Viewed by 4873

Abstract

Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images were enhanced to increase the contrast. Second, the pectoral muscle was eliminated and the breast was [...] Read more.

Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images were enhanced to increase the contrast. Second, the pectoral muscle was eliminated and the breast was suppressed from the mammogram. Afterward, some statistical features were extracted. Next, k-nearest neighbor (k-NN) and decision trees classifiers were used to classify the normal and abnormal lesions. Moreover, multiple classifier systems (MCS) was constructed as it usually improves the classification results. The MCS has two structures, cascaded and parallel structures. Finally, two wrapper feature selection (FS) approaches were applied to identify those features, which influence classification accuracy. The two data sets (1) the mammographic image analysis society digital mammogram database (MIAS) and (2) the digital mammography dream challenge were combined together to test the CAD system proposed. The highest accuracy achieved with the proposed CAD system before FS was 99.7% using the Adaboosting of the J48 decision tree classifiers. The highest accuracy after FS was 100%, which was achieved with k-NN classifier. Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed CAD system was able to accurately classify normal and abnormal lesions in mammogram samples. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging)

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Review

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13 pages, 567 KiB

Open AccessReview

Breast Cancer Detection—A Synopsis of Conventional Modalities and the Potential Role of Microwave Imaging

by Brian M. Moloney, Declan O’Loughlin, Sami Abd Elwahab and Michael J. Kerin

Diagnostics 2020, 10(2), 103; https://doi.org/10.3390/diagnostics10020103 - 14 Feb 2020

Cited by 39 | Viewed by 6264

Abstract

Global statistics have demonstrated that breast cancer is the most frequently diagnosed invasive cancer and the leading cause of cancer death among female patients. Survival following a diagnosis of breast cancer is grossly determined by the stage of the disease at the time [...] Read more.

Global statistics have demonstrated that breast cancer is the most frequently diagnosed invasive cancer and the leading cause of cancer death among female patients. Survival following a diagnosis of breast cancer is grossly determined by the stage of the disease at the time of initial diagnosis, highlighting the importance of early detection. Improving early diagnosis will require a multi-faceted approach to optimizing the use of currently available imaging modalities and investigating new methods of detection. The application of microwave technologies in medical diagnostics is an emerging field of research, with breast cancer detection seeing the most significant progress in the last twenty years. In this review, the application of current conventional imaging modalities is discussed, and recurrent shortcomings highlighted. Microwave imaging is rapid and inexpensive. If the preliminary results of its diagnostic capacity are substantiated, microwave technology may offer a non-ionizing, non-invasive, and painless adjunct or stand-alone modality that could possibly be implemented in routine diagnostic breast care. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging)

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