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Moriakov N, Peters J, Mann R, Karssemeijer N, van Dijck J, Broeders M, Teuwen J. Improving lesion volume measurements on digital mammograms. Med Image Anal 2024; 97:103269. [PMID: 39024973 DOI: 10.1016/j.media.2024.103269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 06/23/2024] [Accepted: 07/08/2024] [Indexed: 07/20/2024]
Abstract
Lesion volume is an important predictor for prognosis in breast cancer. However, it is currently impossible to compute lesion volumes accurately from digital mammography data, which is the most popular and readily available imaging modality for breast cancer. We make a step towards a more accurate lesion volume measurement on digital mammograms by developing a model that allows to estimate lesion volumes on processed mammogram. Processed mammograms are the images routinely used by radiologists in clinical practice as well as in breast cancer screening and are available in medical centers. Processed mammograms are obtained from raw mammograms, which are the X-ray data coming directly from the scanner, by applying certain vendor-specific non-linear transformations. At the core of our volume estimation method is a physics-based algorithm for measuring lesion volumes on raw mammograms. We subsequently extend this algorithm to processed mammograms via a deep learning image-to-image translation model that produces synthetic raw mammograms from processed mammograms in a multi-vendor setting. We assess the reliability and validity of our method using a dataset of 1778 mammograms with an annotated mass. Firstly, we investigate the correlations between lesion volumes computed from mediolateral oblique and craniocaudal views, with a resulting Pearson correlation of 0.93 [95% confidence interval (CI) 0.92 - 0.93]. Secondly, we compare the resulting lesion volumes from true and synthetic raw data, with a resulting Pearson correlation of 0.998 [95%CI 0.998 - 0.998] . Finally, for a subset of 100 mammograms with a malignant mass and concurrent MRI examination available, we analyze the agreement between lesion volume on mammography and MRI, resulting in an intraclass correlation coefficient of 0.81 [95%CI 0.73 - 0.87] for consistency and 0.78 [95%CI 0.66 - 0.86] for absolute agreement. In conclusion, we developed an algorithm to measure mammographic lesion volume that reached excellent reliability and good validity, when using MRI as ground truth. The algorithm may play a role in lesion characterization and breast cancer prognostication on mammograms.
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Affiliation(s)
- Nikita Moriakov
- Department of Radiation Oncology, Netherlands Cancer Institute, The Netherlands; Department of Medical Imaging, Radboud University Medical Center, The Netherlands; Institute for Informatics, University of Amsterdam, The Netherlands.
| | - Jim Peters
- Department for Health Evidence, Radboud University Medical Center, The Netherlands
| | - Ritse Mann
- Department of Medical Imaging, Radboud University Medical Center, The Netherlands
| | - Nico Karssemeijer
- Department of Medical Imaging, Radboud University Medical Center, The Netherlands
| | - Jos van Dijck
- Department for Health Evidence, Radboud University Medical Center, The Netherlands
| | - Mireille Broeders
- Department for Health Evidence, Radboud University Medical Center, The Netherlands
| | - Jonas Teuwen
- Department of Radiation Oncology, Netherlands Cancer Institute, The Netherlands; Department of Medical Imaging, Radboud University Medical Center, The Netherlands
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Mariapun S, Ho WK, Eriksson M, Mohd Taib NA, Yip CH, Rahmat K, Hall P, Teo SH. Association of area- and volumetric-mammographic density and breast cancer risk in women of Asian descent: a case control study. Breast Cancer Res 2024; 26:79. [PMID: 38750574 PMCID: PMC11094942 DOI: 10.1186/s13058-024-01829-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 04/19/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Mammographic density (MD) has been shown to be a strong and independent risk factor for breast cancer in women of European and Asian descent. However, the majority of Asian studies to date have used BI-RADS as the scoring method and none have evaluated area and volumetric densities in the same cohort of women. This study aims to compare the association of MD measured by two automated methods with the risk of breast cancer in Asian women, and to investigate if the association is different for premenopausal and postmenopausal women. METHODS In this case-control study of 531 cases and 2297 controls, we evaluated the association of area-based MD measures and volumetric-based MD measures with breast cancer risk in Asian women using conditional logistic regression analysis, adjusting for relevant confounders. The corresponding association by menopausal status were assessed using unconditional logistic regression. RESULTS We found that both area and volume-based MD measures were associated with breast cancer risk. Strongest associations were observed for percent densities (OR (95% CI) was 2.06 (1.42-2.99) for percent dense area and 2.21 (1.44-3.39) for percent dense volume, comparing women in highest density quartile with those in the lowest quartile). The corresponding associations were significant in postmenopausal but not premenopausal women (premenopausal versus postmenopausal were 1.59 (0.95-2.67) and 1.89 (1.22-2.96) for percent dense area and 1.24 (0.70-2.22) and 1.96 (1.19-3.27) for percent dense volume). However, the odds ratios were not statistically different by menopausal status [p difference = 0.782 for percent dense area and 0.486 for percent dense volume]. CONCLUSIONS This study confirms the associations of mammographic density measured by both area and volumetric methods and breast cancer risk in Asian women. Stronger associations were observed for percent dense area and percent dense volume, and strongest effects were seen in postmenopausal individuals.
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Affiliation(s)
- Shivaani Mariapun
- Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
- School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia, Semenyih, Selangor, Malaysia
| | - Weang-Kee Ho
- Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
- School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia, Semenyih, Selangor, Malaysia
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Nur Aishah Mohd Taib
- Faculty of Medicine, University Malaya Cancer Research Institute, University Malaya, Kuala Lumpur, Malaysia
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Cheng-Har Yip
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Subang Jaya Medical Centre, Subang Jaya, Malaysia
| | - Kartini Rahmat
- Faculty of Medicine, University Malaya Cancer Research Institute, University Malaya, Kuala Lumpur, Malaysia
- Biomedical Imaging Department, Faculty of Medicine, Universiti Malaya Research Imaging Centre, University of Malaya, Kuala Lumpur, Malaysia
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Soo-Hwang Teo
- Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia.
- Faculty of Medicine, University Malaya Cancer Research Institute, University Malaya, Kuala Lumpur, Malaysia.
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3
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Yang J, Mehta N, Demirci G, Hu X, Ramakrishnan MS, Naguib M, Chen C, Tsai CL. Anomaly-guided weakly supervised lesion segmentation on retinal OCT images. Med Image Anal 2024; 94:103139. [PMID: 38493532 PMCID: PMC11016376 DOI: 10.1016/j.media.2024.103139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 01/23/2024] [Accepted: 03/05/2024] [Indexed: 03/19/2024]
Abstract
The availability of big data can transform the studies in biomedical research to generate greater scientific insights if expert labeling is available to facilitate supervised learning. However, data annotation can be labor-intensive and cost-prohibitive if pixel-level precision is required. Weakly supervised semantic segmentation (WSSS) with image-level labeling has emerged as a promising solution in medical imaging. However, most existing WSSS methods in the medical domain are designed for single-class segmentation per image, overlooking the complexities arising from the co-existence of multiple classes in a single image. Additionally, the multi-class WSSS methods from the natural image domain cannot produce comparable accuracy for medical images, given the challenge of substantial variation in lesion scales and occurrences. To address this issue, we propose a novel anomaly-guided mechanism (AGM) for multi-class segmentation in a single image on retinal optical coherence tomography (OCT) using only image-level labels. AGM leverages the anomaly detection and self-attention approach to integrate weak abnormal signals with global contextual information into the training process. Furthermore, we include an iterative refinement stage to guide the model to focus more on the potential lesions while suppressing less relevant regions. We validate the performance of our model with two public datasets and one challenging private dataset. Experimental results show that our approach achieves a new state-of-the-art performance in WSSS for lesion segmentation on OCT images.
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Affiliation(s)
- Jiaqi Yang
- Graduate Center CUNY, 365 5th Ave, NY 10016, USA.
| | - Nitish Mehta
- New York University Department of Ophthalmology, NYU Langone Health, 222 E. 41st St., 3rd Floor, NY 10017, USA
| | | | - Xiaoling Hu
- Stony Brook University, 100 Nicolls Rd, Stony Brook 11794, USA
| | - Meera S Ramakrishnan
- New York University Department of Ophthalmology, NYU Langone Health, 222 E. 41st St., 3rd Floor, NY 10017, USA
| | - Mina Naguib
- New York University Department of Ophthalmology, NYU Langone Health, 222 E. 41st St., 3rd Floor, NY 10017, USA
| | - Chao Chen
- Stony Brook University, 100 Nicolls Rd, Stony Brook 11794, USA
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Yamamuro M, Asai Y, Hashimoto N, Yasuda N, Kimura H, Yamada T, Nemoto M, Kimura Y, Handa H, Yoshida H, Abe K, Tada M, Habe H, Nagaoka T, Nin S, Ishii K, Kondo Y. Utility of U-Net for the objective segmentation of the fibroglandular tissue region on clinical digital mammograms. Biomed Phys Eng Express 2022; 8. [PMID: 35728581 DOI: 10.1088/2057-1976/ac7ada] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/21/2022] [Indexed: 11/11/2022]
Abstract
This study investigates the equivalence or compatibility between U-Net and visual segmentations of fibroglandular tissue regions by mammography experts for calculating the breast density and mean glandular dose (MGD). A total of 703 mediolateral oblique-view mammograms were used for segmentation. Two region types were set as the ground truth (determined visually): (1) one type included only the region where fibroglandular tissue was identifiable (called the 'dense region'); (2) the other type included the region where the fibroglandular tissue may have existed in the past, provided that apparent adipose-only parts, such as the retromammary space, are excluded (the 'diffuse region'). U-Net was trained to segment the fibroglandular tissue region with an adaptive moment estimation optimiser, five-fold cross-validated with 400 training and 100 validation mammograms, and tested with 203 mammograms. The breast density and MGD were calculated using the van Engeland and Dance formulas, respectively, and compared between U-Net and the ground truth with the Dice similarity coefficient and Bland-Altman analysis. Dice similarity coefficients between U-Net and the ground truth were 0.895 and 0.939 for the dense and diffuse regions, respectively. In the Bland-Altman analysis, no proportional or fixed errors were discovered in either the dense or diffuse region for breast density, whereas a slight proportional error was discovered in both regions for the MGD (the slopes of the regression lines were -0.0299 and -0.0443 for the dense and diffuse regions, respectively). Consequently, the U-Net and ground truth were deemed equivalent (interchangeable) for breast density and compatible (interchangeable following four simple arithmetic operations) for MGD. U-Net-based segmentation of the fibroglandular tissue region was satisfactory for both regions, providing reliable segmentation for breast density and MGD calculations. U-Net will be useful in developing a reliable individualised screening-mammography programme, instead of relying on the visual judgement of mammography experts.
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Affiliation(s)
- Mika Yamamuro
- Radiology Center, Kindai University Hospital, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan.,Graduate School of Health Sciences, Niigata University, 2-746, Asahimachidori, Chuouku, Niigata 951-8518, Japan
| | - Yoshiyuki Asai
- Radiology Center, Kindai University Hospital, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Naomi Hashimoto
- Radiology Center, Kindai University Hospital, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Nao Yasuda
- Radiology Center, Kindai University Hospital, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Hiorto Kimura
- Radiology Center, Kindai University Hospital, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Takahiro Yamada
- Division of Positron Emission Tomography Institute of Advanced Clinical Medicine, Kindai University, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Mitsutaka Nemoto
- Department of Computational Systems Biology, Kindai University Faculty of Biology-Oriented Science and Technology, 930, Nishimitani, Kinokawa, Wakayama 649-6433, Japan
| | - Yuichi Kimura
- Department of Computational Systems Biology, Kindai University Faculty of Biology-Oriented Science and Technology, 930, Nishimitani, Kinokawa, Wakayama 649-6433, Japan
| | - Hisashi Handa
- Department of Informatics, Kindai University Faculty of Science and Engineering, 3-4-1, Kowakae, Higashi-osaka, Osaka 577-8502, Japan
| | - Hisashi Yoshida
- Department of Computational Systems Biology, Kindai University Faculty of Biology-Oriented Science and Technology, 930, Nishimitani, Kinokawa, Wakayama 649-6433, Japan
| | - Koji Abe
- Department of Informatics, Kindai University Faculty of Science and Engineering, 3-4-1, Kowakae, Higashi-osaka, Osaka 577-8502, Japan
| | - Masahiro Tada
- Department of Informatics, Kindai University Faculty of Science and Engineering, 3-4-1, Kowakae, Higashi-osaka, Osaka 577-8502, Japan
| | - Hitoshi Habe
- Department of Informatics, Kindai University Faculty of Science and Engineering, 3-4-1, Kowakae, Higashi-osaka, Osaka 577-8502, Japan
| | - Takashi Nagaoka
- Department of Computational Systems Biology, Kindai University Faculty of Biology-Oriented Science and Technology, 930, Nishimitani, Kinokawa, Wakayama 649-6433, Japan
| | - Seiun Nin
- Department of Radiology, Kindai University Faculty of Medicine, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Kazunari Ishii
- Department of Radiology, Kindai University Faculty of Medicine, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Yohan Kondo
- Graduate School of Health Sciences, Niigata University, 2-746, Asahimachidori, Chuouku, Niigata 951-8518, Japan
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Patuleia SIS, van der Wall E, van Gils CH, Bakker MF, Jager A, Voorhorst-Ogink MM, van Diest PJ, Moelans CB. The changing microRNA landscape by color and cloudiness: a cautionary tale for nipple aspirate fluid biomarker analysis. Cell Oncol (Dordr) 2021; 44:1339-1349. [PMID: 34655415 PMCID: PMC8648697 DOI: 10.1007/s13402-021-00641-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 10/08/2021] [Indexed: 01/15/2023] Open
Abstract
PURPOSE Investigation of nipple aspirate fluid (NAF)-based microRNAs (miRNAs) as a potential screening tool for women at increased risk of developing breast cancer is the scope of our research. While aiming to identify discriminating NAF-miRNAs between women with different mammographic densities, we were confronted with an unexpected confounder: NAF sample appearance. Here we report and alert for the impact of NAF color and cloudiness on miRNA assessment. METHODS Seven classes of NAF colors coupled with cloudiness appearance were established. Using 173 NAF samples from 154 healthy women (19 samples were bilaterally collected), the expression of 14 target and 2 candidate endogenous control (EC) miRNAs was investigated using Taqman Advanced miRNA assays to identify significant differential expression patterns between color-cloudiness classes. Inter- and intra-individual variation of miRNA expression was analyzed using the coefficient of variation (CV). RESULTS We found that between the seven NAF classes, fold change miRNA expression differences ranged between 2.4 and 19.6 depending on the interrogated miRNA. Clear NAF samples exhibited higher miRNA expression levels compared to cloudy NAF samples with fold change differences ranging between 1.1 and 6.2. Inter-individual and intra-individual miRNA expression was fairly stable (CV < 15 %), but nevertheless impacted by NAF sample appearance. Within NAF classes, inter-individual variation was largest for green samples (CV 6-15 %) and smallest for bloody samples (CV 2-6 %). CONCLUSIONS Our data indicate that NAF color and cloudiness influence miRNA expression and should, therefore, be systematically registered using an objective color classification system. Given that sample appearance is an inherent feature of NAF, these variables should be statistically controlled for in multivariate data analyses. This cautionary note and recommendations could be of value beyond the field of NAF-miRNAs, given that variability in sample color and cloudiness is likewise observed in liquid biopsies such as urine, cerebrospinal fluid and sputum, and could thereby influence the levels of miRNAs and other biomarkers.
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Affiliation(s)
- Susana I S Patuleia
- Department of Pathology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Elsken van der Wall
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Carla H van Gils
- Department of Epidemiology of the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Marije F Bakker
- Department of Epidemiology of the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Agnes Jager
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Marleen M Voorhorst-Ogink
- Department of Pathology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Cathy B Moelans
- Department of Pathology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
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Hernández A, Miranda DA, Pertuz S. Algorithms and methods for computerized analysis of mammography images in breast cancer risk assessment. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 212:106443. [PMID: 34656014 DOI: 10.1016/j.cmpb.2021.106443] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 09/22/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES The computerized analysis of mammograms for the development of quantitative biomarkers is a growing field with applications in breast cancer risk assessment. Computerized image analysis offers the possibility of using different methods and algorithms to extract additional information from screening and diagnosis images to aid in the assessment of breast cancer risk. In this work, we review the algorithms and methods for the automated, computerized analysis of mammography images for the task mentioned, and discuss the main challenges that the development and improvement of these methods face today. METHODS We review the recent progress in two main branches of mammography-based risk assessment: parenchymal analysis and breast density estimation, including performance indicators of most of the studies considered. Parenchymal analysis methods are divided into feature-based methods and deep learning-based methods; breast density methods are grouped into area-based, volume-based, and breast categorization methods. Additionally, we identify the challenges that these study fields currently face. RESULTS Parenchymal analysis using deep learning algorithms are on the rise, with some studies showing high-performance indicators, such as an area under the receiver operating characteristic curve of up to 90. Methods for risk assessment using breast density report a wider variety of performance indicators; however, we can also identify that the approaches using deep learning methods yield high performance in each of the subdivisions considered. CONCLUSIONS Both breast density estimation and parenchymal analysis are promising tools for the task of breast cancer risk assessment; deep learning methods have shown performance comparable or superior to the other considered methods. All methods considered face challenges such as the lack of objective comparison between them and the lack of access to datasets from different populations.
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Affiliation(s)
| | | | - Said Pertuz
- Universidad Industrial de Santander, Bucaramanga, Colombia.
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Abstract
This article gives a brief overview of the development of artificial intelligence in clinical breast imaging. For multiple decades, artificial intelligence (AI) methods have been developed and translated for breast imaging tasks such as detection, diagnosis, and assessing response to therapy. As imaging modalities arise to support breast cancer screening programs and diagnostic examinations, including full-field digital mammography, breast tomosynthesis, ultrasound, and MRI, AI techniques parallel the efforts with more complex algorithms, faster computers, and larger data sets. AI methods include human-engineered radiomics algorithms and deep learning methods. Examples of these AI-supported clinical tasks are given along with commentary on the future.
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Affiliation(s)
- Qiyuan Hu
- Committee on Medical Physics, Department of Radiology, The University of Chicago, 5841 S Maryland Avenue, MC2026, Chicago, IL 60637, USA
| | - Maryellen L Giger
- Committee on Medical Physics, Department of Radiology, The University of Chicago, 5841 S Maryland Avenue, MC2026, Chicago, IL 60637, USA.
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Inoue K, Kawasaki A, Koshimizu K, Ariizumi C, Unno K, Nagashima M, Mizuno K, Misumi M, Tsutsumi C, Sasaki T, Doi T. [Automatic Quantification of Breast Density from Mammography Using Deep Learning]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2021; 77:1165-1172. [PMID: 34670923 DOI: 10.6009/jjrt.2021_jsrt_77.10.1165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
BACKGROUND In the field of breast screening using mammography, announcing to the examinees whether they are dense or not has not been deprecated in Japan. One of the reasons is a shortage of objectivity estimating their dense breast. Our aim is to build a system with deep learning algorithm to calculate and quantify objective breast density automatically. MATERIAL AND METHOD Mammography images taken in our institute that were diagnosed as category 1 were collected. Each processed image was transformed into eight-bit grayscale, with the size of 2294 pixels by 1914 pixels. The "base pixel value" was calculated from the fatty area within the breast for each image. The "relative density" was calculated by dividing each pixel value by the base pixel value. Semantic segmentation algorithm was used to automatically segment the area of breast tissue within the mammography image, which was resized to 144 pixels by 120 pixels. By aggregating the relative density within the breast tissue area, the "breast density" was obtained automatically. RESULT From each but one mammography image, the breast density was successfully calculated automatically. By defining a dense breast as the breast density being greater than or equal to 30%, the evaluation of the dense breast was consistent with that by a computer and human (76.6%). CONCLUSION Deep learning provides an excellent estimation of quantification of breast density. This system could contribute to improve the efficiency of mammography screening system.
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Affiliation(s)
| | | | | | | | - Keiko Unno
- Shonan Memorial Hospital, Breast Cancer Center
| | | | - Kayo Mizuno
- Shonan Memorial Hospital, Breast Cancer Center
| | | | | | - Takeshi Sasaki
- Department of Next-Generation Pathology Information Networking, Faculty of Medicine, The University of Tokyo
| | - Takako Doi
- Shonan Memorial Hospital, Breast Cancer Center
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10
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Comparison of breast density assessment between human eye and automated software on digital and synthetic mammography: Impact on breast cancer risk. Diagn Interv Imaging 2020; 101:811-819. [PMID: 32819886 DOI: 10.1016/j.diii.2020.07.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 07/07/2020] [Accepted: 07/27/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE To evaluate the agreement between automatic assessment software of breast density based on artificial intelligence (AI) and visual assessment by a senior and a junior radiologist, as well as the impact on the assessment of breast cancer risk (BCR) at 5 years. MATERIALS AND METHODS We retrospectively included 311 consecutive women (mean age, 55.6±8.5 [SD]; range: 40-74 years) without a personal history of breast cancer who underwent routine mammography between January 1, 2019 and February 28, 2019. Mammographic breast density (MBD) was independently evaluated by a junior and a senior reader on digital mammography (DM) and synthetic mammography (SM) using BI-RADS (5th edition) and by an AI software. For each MBD, BCR at 5 years was estimated per woman by the AI software. Interobserver agreement for MBD between the two readers and the AI software were evaluated by quadratic κ coefficients. Reproducibility of BCR was assessed by intraclass correlation coefficient (ICC). RESULTS Agreement for MBD assessment on DM and SM was almost perfect between senior and junior radiologists (κ=0.88 [95% CI: 0.84-0.92] and κ=0.86 [95% CI: 0.82-0.90], respectively) and substantial between the senior radiologist and AI (κ=0.79; 95% CI: 0.73-0.84). There was substantial agreement between DM and SM for the senior radiologist (κ=0.79; 95% CI: 0.74-0.84). BCR evaluation at 5 years was highly reproducible between the two radiologists on DM and SM (ICC=0.98 [95% CI: 0.97-0.98] for both), between BCR evaluation based on DM and SM evaluated by the senior (ICC=0.96; 95% CI: 0.95-0.97) or junior radiologist (ICC=0.97; 95% CI: 0.96-0.98) and between the senior radiologist and AI (ICC=0.96; 95% CI: 0.95-0.97). CONCLUSION This preliminary study demonstrates a very good agreement for BCR evaluation based on the evaluation of MBD by a senior radiologist, junior radiologist and AI software.
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Alomaim W, O’Leary D, Ryan J, Rainford L, Evanoff M, Foley S. Subjective Versus Quantitative Methods of Assessing Breast Density. Diagnostics (Basel) 2020; 10:diagnostics10050331. [PMID: 32455552 PMCID: PMC7277954 DOI: 10.3390/diagnostics10050331] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 05/16/2020] [Accepted: 05/19/2020] [Indexed: 11/16/2022] Open
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 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.
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Affiliation(s)
- Wijdan Alomaim
- Radiography & Medical Imaging, Fatima College of Health Sciences, Abu Dhabi, UAE
- Correspondence: ; Tel.: +9712-5078639
| | - Desiree O’Leary
- Radiography (Diagnostic Imaging), Keele University, Keele ST5 5BG, UK; D.s.o'
| | - John Ryan
- Radiography & Diagnostic Imaging, School of Medicine, University College Dublin, 4 Dublin, Ireland; (J.R.); (L.R.); (S.F.)
| | - Louise Rainford
- Radiography & Diagnostic Imaging, School of Medicine, University College Dublin, 4 Dublin, Ireland; (J.R.); (L.R.); (S.F.)
| | | | - Shane Foley
- Radiography & Diagnostic Imaging, School of Medicine, University College Dublin, 4 Dublin, Ireland; (J.R.); (L.R.); (S.F.)
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Pepłońska B, Janasik B, McCormack V, Bukowska-Damska A, Kałużny P. Cadmium and volumetric mammographic density: A cross-sectional study in Polish women. PLoS One 2020; 15:e0233369. [PMID: 32433664 PMCID: PMC7239444 DOI: 10.1371/journal.pone.0233369] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 05/04/2020] [Indexed: 01/26/2023] Open
Abstract
INTRODUCTION Cadmium (Cd) is a heavy metal, which is widespread in the environment and has been hypothesized to be a metalloestrogen and a breast cancer risk factor. Mammographic density (MD) reflects the composition of the breast and was proposed to be used as a surrogate marker for breast cancer. The aim of our study was to investigate association between cadmium concentration in urine and mammographic density. METHODS A cross-sectional study included 517 women aged 40-60 years who underwent screening mammography in Łódź, Poland. Data were collected through personal interviews and anthropometric measurements. Spot morning urine samples were obtained. The examination of the breasts included both craniocaudal and mediolateral oblique views. Raw data ("for processing") generated by the digital mammography system were analysed using Volpara Imaging Software, The volumetric breast density(%) and fibrograndular tissue volume(cm3) were determined. Cadmium concentration in urine was analysed using the standard ICP-MS method. RESULTS After adjusting for key confounders including age, BMI, family breast cancer, mammographic device, season of the year of mammography, and age at menarche, an inverse association of Cd and volumetric breast density was found, which was attenuated after further adjustment for smoking. Associations of Cd with dense volume were null. CONCLUSIONS These findings suggest that Cd is not positively associated with breast density, a strong marker of breast cancer risk, when examined in a cross-sectional fashion.
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Affiliation(s)
- Beata Pepłońska
- Department of Environmental Epidemiology, Nofer Institute of Occupational Medicine, Lodz, Poland
| | - Beata Janasik
- Department of Biological and Environmental Monitoring, Nofer Institute of Occupational Medicine, Lodz, Poland
| | - Valerie McCormack
- Section of Environment and Radiation, International Agency for research on Cancer, Lyon, France
| | | | - Paweł Kałużny
- Department of Environmental Epidemiology, Nofer Institute of Occupational Medicine, Lodz, Poland
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Torres GF, Sassi A, Arponen O, Holli-Helenius K, Laaperi AL, Rinta-Kiikka I, Kamarainen J, Pertuz S. Morphological Area Gradient: System-independent Dense Tissue Segmentation in Mammography Images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:4855-4858. [PMID: 31946948 DOI: 10.1109/embc.2019.8857320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Breast density has been identified as one of the strongest risk factors for breast cancer. However, the development of reliable and reproducible methods for the automatic dense tissue segmentation has been an important challenge. Due to the complexity of the acquisition process of mammography images, current approaches need to be calibrated for specific mammographic systems or require access to raw mammograms. In this work, we introduce the Morphological Area Gradient (MAG) as a generic measure for mammography images. MAG is generic in the sense that it does not need calibration or access to raw mammograms. At the core of MAG is the derivative of the area of segmented tissue with respect to the pixel intensity. We have found that the high-density regions can be automatically segmented by minimizing the MAG of a mammogram. To verify the performance of MAG, we collected 566 full-field digital mammograms using two different medical devices and a human expert manually annotated the high-density regions in each image. The proposed MAG method yields a median absolute error of 7.6% and a Dices similarity coefficient of 0.83, which are superior to other clinically validated state-of-the-art algorithms.
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Bakker MF, de Lange SV, Pijnappel RM, Mann RM, Peeters PHM, Monninkhof EM, Emaus MJ, Loo CE, Bisschops RHC, Lobbes MBI, de Jong MDF, Duvivier KM, Veltman J, Karssemeijer N, de Koning HJ, van Diest PJ, Mali WPTM, van den Bosch MAAJ, Veldhuis WB, van Gils CH. Supplemental MRI Screening for Women with Extremely Dense Breast Tissue. N Engl J Med 2019; 381:2091-2102. [PMID: 31774954 DOI: 10.1056/nejmoa1903986] [Citation(s) in RCA: 368] [Impact Index Per Article: 73.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
BACKGROUND Extremely dense breast tissue is a risk factor for breast cancer and limits the detection of cancer with mammography. Data are needed on the use of supplemental magnetic resonance imaging (MRI) to improve early detection and reduce interval breast cancers in such patients. METHODS In this multicenter, randomized, controlled trial in the Netherlands, we assigned 40,373 women between the ages of 50 and 75 years with extremely dense breast tissue and normal results on screening mammography to a group that was invited to undergo supplemental MRI or to a group that received mammography screening only. The groups were assigned in a 1:4 ratio, with 8061 in the MRI-invitation group and 32,312 in the mammography-only group. The primary outcome was the between-group difference in the incidence of interval cancers during a 2-year screening period. RESULTS The interval-cancer rate was 2.5 per 1000 screenings in the MRI-invitation group and 5.0 per 1000 screenings in the mammography-only group, for a difference of 2.5 per 1000 screenings (95% confidence interval [CI], 1.0 to 3.7; P<0.001). Of the women who were invited to undergo MRI, 59% accepted the invitation. Of the 20 interval cancers that were diagnosed in the MRI-invitation group, 4 were diagnosed in the women who actually underwent MRI (0.8 per 1000 screenings) and 16 in those who did not accept the invitation (4.9 per 1000 screenings). The MRI cancer-detection rate among the women who actually underwent MRI screening was 16.5 per 1000 screenings (95% CI, 13.3 to 20.5). The positive predictive value was 17.4% (95% CI, 14.2 to 21.2) for recall for additional testing and 26.3% (95% CI, 21.7 to 31.6) for biopsy. The false positive rate was 79.8 per 1000 screenings. Among the women who underwent MRI, 0.1% had either an adverse event or a serious adverse event during or immediately after the screening. CONCLUSIONS The use of supplemental MRI screening in women with extremely dense breast tissue and normal results on mammography resulted in the diagnosis of significantly fewer interval cancers than mammography alone during a 2-year screening period. (Funded by the University Medical Center Utrecht and others; DENSE ClinicalTrials.gov number, NCT01315015.).
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Affiliation(s)
- Marije F Bakker
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Stéphanie V de Lange
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Ruud M Pijnappel
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Ritse M Mann
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Petra H M Peeters
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Evelyn M Monninkhof
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Marleen J Emaus
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Claudette E Loo
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Robertus H C Bisschops
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Marc B I Lobbes
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Matthijn D F de Jong
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Katya M Duvivier
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Jeroen Veltman
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Nico Karssemeijer
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Harry J de Koning
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Paul J van Diest
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Willem P T M Mali
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Maurice A A J van den Bosch
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Wouter B Veldhuis
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
| | - Carla H van Gils
- From the Julius Center for Health Sciences and Primary Care (M.F.B., S.V.L., P.H.M.P., E.M.M., C.H.G.) and the Departments of Radiology (S.V.L., R.M.P., M.J.E., W.P.T.M.M., M.A.A.J.B., W.B.V.) and Pathology (P.J.D.), University Medical Center Utrecht, Utrecht University, Utrecht, the Dutch Expert Center for Screening (R.M.P.) and the Department of Radiology, Radboud University Nijmegen Medical Center (R.M.M., N.K.), Nijmegen, the Department of Radiology, Antoni van Leeuwenhoek Hospital (C.E.L.), and the Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam (K.M.D.), Amsterdam, the Department of Radiology, Albert Schweitzer Hospital, Dordrecht (R.H.C.B.), the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, and the Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen (M.B.I.L.), the Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch (M.D.F.J.), the Department of Radiology, Hospital Group Twente, Almelo (J.V.), and the Department of Public Health, Erasmus Medical Center, Rotterdam (H.J.K.) - all in the Netherlands; and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London (P.H.M.P.)
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Fattori Alves AF, Menegatti Pavan AL, Giacomini G, Quini CC, Marrone Ribeiro S, Garcia Marquez R, Bentlin MR, Trindade AP, Miranda JRDA, Pina DRD. Radiographic predictors determined with an objective assessment tool for neonatal patients with necrotizing enterocolitis. JORNAL DE PEDIATRIA (VERSÃO EM PORTUGUÊS) 2019. [DOI: 10.1016/j.jpedp.2018.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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Radiographic predictors determined with an objective assessment tool for neonatal patients with necrotizing enterocolitis. J Pediatr (Rio J) 2019; 95:674-681. [PMID: 31679612 DOI: 10.1016/j.jped.2018.05.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2018] [Revised: 05/16/2018] [Accepted: 05/17/2018] [Indexed: 11/21/2022] Open
Abstract
OBJECTIVE The objective of this study was to develop and validate a computational tool to assist radiological decisions on necrotizing enterocolitis. METHODOLOGY Patients that exhibited clinical signs and radiographic evidence of Bell's stage 2 or higher were included in the study, resulting in 64 exams. The tool was used to classify localized bowel wall thickening and intestinal pneumatosis using full-width at half-maximum measurements and texture analyses based on wavelet energy decomposition. Radiological findings of suspicious bowel wall thickening and intestinal pneumatosis loops were confirmed by both patient surgery and histopathological analysis. Two experienced radiologists selected an involved bowel and a normal bowel in the same radiography. The full-width at half-maximum and wavelet-based texture feature were then calculated and compared using the Mann-Whitney U test. Specificity, sensibility, positive and negative predictive values were calculated. RESULTS The full-width at half-maximum results were significantly different between normal and distended loops (median of 10.30 and 15.13, respectively). Horizontal, vertical, and diagonal wavelet energy measurements were evaluated at eight levels of decomposition. Levels 7 and 8 in the horizontal direction presented significant differences. For level 7, median was 0.034 and 0.088 for normal and intestinal pneumatosis groups, respectively, and for level 8 median was 0.19 and 0.34, respectively. CONCLUSIONS The developed tool could detect differences in radiographic findings of bowel wall thickening and IP that are difficult to diagnose, demonstrating the its potential in clinical routine. The tool that was developed in the present study may help physicians to investigate suspicious bowel loops, thereby considerably improving diagnosis and clinical decisions.
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Quantitative Volumetric K-Means Cluster Segmentation of Fibroglandular Tissue and Skin in Breast MRI. J Digit Imaging 2019; 31:425-434. [PMID: 29047034 DOI: 10.1007/s10278-017-0031-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Mammographic breast density (MBD) is the most commonly used method to assess the volume of fibroglandular tissue (FGT). However, MRI could provide a clinically feasible and more accurate alternative. There were three aims in this study: (1) to evaluate a clinically feasible method to quantify FGT with MRI, (2) to assess the inter-rater agreement of MRI-based volumetric measurements and (3) to compare them to measurements acquired using digital mammography and 3D tomosynthesis. This retrospective study examined 72 women (mean age 52.4 ± 12.3 years) with 105 disease-free breasts undergoing diagnostic 3.0-T breast MRI and either digital mammography or tomosynthesis. Two observers analyzed MRI images for breast and FGT volumes and FGT-% from T1-weighted images (0.7-, 2.0-, and 4.0-mm-thick slices) using K-means clustering, data from histogram, and active contour algorithms. Reference values were obtained with Quantra software. Inter-rater agreement for MRI measurements made with 2-mm-thick slices was excellent: for FGT-%, r = 0.994 (95% CI 0.990-0.997); for breast volume, r = 0.985 (95% CI 0.934-0.994); and for FGT volume, r = 0.979 (95% CI 0.958-0.989). MRI-based FGT-% correlated strongly with MBD in mammography (r = 0.819-0.904, P < 0.001) and moderately to high with MBD in tomosynthesis (r = 0.630-0.738, P < 0.001). K-means clustering-based assessments of the proportion of the fibroglandular tissue in the breast at MRI are highly reproducible. In the future, quantitative assessment of FGT-% to complement visual estimation of FGT should be performed on a more regular basis as it provides a component which can be incorporated into the individual's breast cancer risk stratification.
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Natesan R, Wiskin J, Lee S, Malik BH. Quantitative Assessment of Breast Density: Transmission Ultrasound is Comparable to Mammography with Tomosynthesis. Cancer Prev Res (Phila) 2019; 12:871-876. [DOI: 10.1158/1940-6207.capr-19-0268] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 09/19/2019] [Accepted: 10/16/2019] [Indexed: 11/16/2022]
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Pertuz S, Sassi A, Karivaara-Mäkelä M, Holli-Helenius K, Lääperi AL, Rinta-Kiikka I, Arponen O, Kämäräinen JK. Micro-parenchymal patterns for breast cancer risk assessment. Biomed Phys Eng Express 2019. [DOI: 10.1088/2057-1976/ab42f4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Østerås BH, Martinsen ACT, Gullien R, Skaane P. Digital Mammography versus Breast Tomosynthesis: Impact of Breast Density on Diagnostic Performance in Population-based Screening. Radiology 2019; 293:60-68. [PMID: 31407968 DOI: 10.1148/radiol.2019190425] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
BackgroundPrevious studies comparing digital breast tomosynthesis (DBT) to digital mammography (DM) have shown conflicting results regarding breast density and diagnostic performance.PurposeTo compare true-positive and false-positive interpretations in DM versus DBT according to volumetric density, age, and mammographic findings.Materials and MethodsFrom November 2010 to December 2012, 24 301 women aged 50-69 years (mean age, 59.1 years ± 5.7) were prospectively included in the Oslo Tomosynthesis Screening Trial. Participants received same-compression DM and DBT with independent double reading for both DM and DM plus DBT reading modes. Eight experienced radiologists rated the images by using a five-point scale for probability of malignancy. Participants were followed up for 2 years to assess for interval cancers. Breast density was assessed by using automatic volumetric software (scale, 1-4). Differences in true-positive rates, false-positive rates, and mammographic findings were assessed by using confidence intervals (Newcombe paired method) and P values (McNemar and χ2 tests).ResultsThe true-positive rate of DBT was higher than that of DM for density groups (range, 12%-24%; P < .001 for density scores of 2 and 3, and P > .05 for density scores of 1 and 4) and age groups (range, 15%-35%; P < .05 for all age groups), mainly due to the higher number of spiculated masses and architectural distortions found at DBT (P < .001 for density scores of 2 and 3; P < .05 for women aged 55-69 years). The false-positive rate was lower for DBT than for DM in all age groups (range, -0.6% to -1.2%; P < .01) and density groups (range, -0.7 to -1.0%; P < .005) owing to fewer asymmetric densities (P ≤ .001), except for extremely dense breasts (0.1%, P = .82).ConclusionDigital breast tomosynthesis enabled the detection of more cancers in all density and age groups compared with digital mammography, especially cancers classified as spiculated masses and architectural distortions. The improvement in cancer detection rate showed a positive correlation with age. With use of digital breast tomosynthesis, false-positive findings were lower due to fewer asymmetric densities, except in extremely dense breasts.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by Fuchsjäger and Adelsmayr in this issue.
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Affiliation(s)
- Bjørn Helge Østerås
- From the Department of Diagnostic Physics (B.H.Ø., A.C.T.M.) and Division of Radiology and Nuclear Medicine (R.G., P.S.), Oslo University Hospital, Building 20, Gaustad, PO Box 4959, Nydalen, 0424 Oslo, Norway; and Institute of Clinical Medicine (B.H.Ø., P.S.) and Department of Physics (A.C.T.M.), University of Oslo, Oslo, Norway
| | - Anne Catrine T Martinsen
- From the Department of Diagnostic Physics (B.H.Ø., A.C.T.M.) and Division of Radiology and Nuclear Medicine (R.G., P.S.), Oslo University Hospital, Building 20, Gaustad, PO Box 4959, Nydalen, 0424 Oslo, Norway; and Institute of Clinical Medicine (B.H.Ø., P.S.) and Department of Physics (A.C.T.M.), University of Oslo, Oslo, Norway
| | - Randi Gullien
- From the Department of Diagnostic Physics (B.H.Ø., A.C.T.M.) and Division of Radiology and Nuclear Medicine (R.G., P.S.), Oslo University Hospital, Building 20, Gaustad, PO Box 4959, Nydalen, 0424 Oslo, Norway; and Institute of Clinical Medicine (B.H.Ø., P.S.) and Department of Physics (A.C.T.M.), University of Oslo, Oslo, Norway
| | - Per Skaane
- From the Department of Diagnostic Physics (B.H.Ø., A.C.T.M.) and Division of Radiology and Nuclear Medicine (R.G., P.S.), Oslo University Hospital, Building 20, Gaustad, PO Box 4959, Nydalen, 0424 Oslo, Norway; and Institute of Clinical Medicine (B.H.Ø., P.S.) and Department of Physics (A.C.T.M.), University of Oslo, Oslo, Norway
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Saadatmand S, Geuzinge HA, Rutgers EJT, Mann RM, de Roy van Zuidewijn DBW, Zonderland HM, Tollenaar RAEM, Lobbes MBI, Ausems MGEM, van 't Riet M, Hooning MJ, Mares-Engelberts I, Luiten EJT, Heijnsdijk EAM, Verhoef C, Karssemeijer N, Oosterwijk JC, Obdeijn IM, de Koning HJ, Tilanus-Linthorst MMA, van Deurzen CHM, Loo CE, Wesseling J, Schlooz-Vries M, van der Meij S, Mesker W, Keymeulen K, Contant C, Madsen E, Koppert LB, Rothbarth J, Veldhuis WB, Witkamp AJ, Tetteroo E, de Monye C, van Rosmalen MM, Remmelzwaal J, Gort HBW, Roi-Antonides R, Wasser MNJM, van Druten E. MRI versus mammography for breast cancer screening in women with familial risk (FaMRIsc): a multicentre, randomised, controlled trial. Lancet Oncol 2019; 20:1136-1147. [DOI: 10.1016/s1470-2045(19)30275-x] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 03/21/2019] [Accepted: 03/22/2019] [Indexed: 01/03/2023]
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Fieselmann A, Förnvik D, Förnvik H, Lång K, Sartor H, Zackrisson S, Kappler S, Ritschl L, Mertelmeier T. Volumetric breast density measurement for personalized screening: accuracy, reproducibility, consistency, and agreement with visual assessment. J Med Imaging (Bellingham) 2019; 6:031406. [PMID: 30746394 PMCID: PMC6362711 DOI: 10.1117/1.jmi.6.3.031406] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 12/27/2018] [Indexed: 01/22/2023] Open
Abstract
Assessment of breast density at the point of mammographic examination could lead to optimized breast cancer screening pathways. The onsite breast density information may offer guidance of when to recommend supplemental imaging for women in a screening program. A software application (Insight BD, Siemens Healthcare GmbH) for fast onsite quantification of volumetric breast density is evaluated. The accuracy of the method is assessed using breast tissue equivalent phantom experiments resulting in a mean absolute error of 3.84%. Reproducibility of measurement results is analyzed using 8427 exams in total, comparing for each exam (if available) the densities determined from left and right views, from cranio-caudal and medio-lateral oblique views, from full-field digital mammograms (FFDM) and digital breast tomosynthesis (DBT) data and from two subsequent exams of the same breast. Pearson correlation coefficients of 0.937, 0.926, 0.950, and 0.995 are obtained. Consistency of the results is demonstrated by evaluating the dependency of the breast density on women's age. Furthermore, the agreement between breast density categories computed by the software with those determined visually by 32 radiologists is shown by an overall percentage agreement of 69.5% for FFDM and by 64.6% for DBT data. These results demonstrate that the software delivers accurate, reproducible, and consistent measurements that agree well with the visual assessment of breast density by radiologists.
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Affiliation(s)
| | - Daniel Förnvik
- Lund University and Skåne University Hospital, Department of Translational Medicine, Malmö, Sweden
| | - Hannie Förnvik
- Lund University and Skåne University Hospital, Department of Translational Medicine, Malmö, Sweden
| | - Kristina Lång
- Lund University and Skåne University Hospital, Department of Translational Medicine, Malmö, Sweden
- Institute for Biomedical Engineering, ETH Zurich, Zurich, Switzerland
| | - Hanna Sartor
- Lund University and Skåne University Hospital, Department of Translational Medicine, Malmö, Sweden
| | - Sophia Zackrisson
- Lund University and Skåne University Hospital, Department of Translational Medicine, Malmö, Sweden
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Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin 2019; 69:127-157. [PMID: 30720861 PMCID: PMC6403009 DOI: 10.3322/caac.21552] [Citation(s) in RCA: 615] [Impact Index Per Article: 123.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.
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Affiliation(s)
- Wenya Linda Bi
- Assistant Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Ahmed Hosny
- Research Scientist, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Matthew B. Schabath
- Associate Member, Department of Cancer EpidemiologyH. Lee Moffitt Cancer Center and Research InstituteTampaFL
| | - Maryellen L. Giger
- Professor of Radiology, Department of RadiologyUniversity of ChicagoChicagoIL
| | - Nicolai J. Birkbak
- Research Associate, The Francis Crick InstituteLondonUnited Kingdom
- Research Associate, University College London Cancer InstituteLondonUnited Kingdom
| | - Alireza Mehrtash
- Research Assistant, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
- Research Assistant, Department of Electrical and Computer EngineeringUniversity of British ColumbiaVancouverBCCanada
| | - Tavis Allison
- Research Assistant, Department of RadiologyColumbia University College of Physicians and SurgeonsNew YorkNY
- Research Assistant, Department of RadiologyNew York Presbyterian HospitalNew YorkNY
| | - Omar Arnaout
- Assistant Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Christopher Abbosh
- Research Fellow, The Francis Crick InstituteLondonUnited Kingdom
- Research Fellow, University College London Cancer InstituteLondonUnited Kingdom
| | - Ian F. Dunn
- Associate Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Raymond H. Mak
- Associate Professor, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Rulla M. Tamimi
- Associate Professor, Department of MedicineBrigham and Women’s Hospital, Dana‐Farber Cancer Institute, Harvard Medical SchoolBostonMA
| | - Clare M. Tempany
- Professor of Radiology, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Charles Swanton
- Professor, The Francis Crick InstituteLondonUnited Kingdom
- Professor, University College London Cancer InstituteLondonUnited Kingdom
| | - Udo Hoffmann
- Professor of Radiology, Department of RadiologyMassachusetts General Hospital and Harvard Medical SchoolBostonMA
| | - Lawrence H. Schwartz
- Professor of Radiology, Department of RadiologyColumbia University College of Physicians and SurgeonsNew YorkNY
- Chair, Department of RadiologyNew York Presbyterian HospitalNew YorkNY
| | - Robert J. Gillies
- Professor of Radiology, Department of Cancer PhysiologyH. Lee Moffitt Cancer Center and Research InstituteTampaFL
| | - Raymond Y. Huang
- Assistant Professor, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Hugo J. W. L. Aerts
- Associate Professor, Departments of Radiation Oncology and Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
- Professor in AI in Medicine, Radiology and Nuclear Medicine, GROWMaastricht University Medical Centre (MUMC+)MaastrichtThe Netherlands
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A Review of the Role of Augmented Intelligence in Breast Imaging: From Automated Breast Density Assessment to Risk Stratification. AJR Am J Roentgenol 2019; 212:259-270. [DOI: 10.2214/ajr.18.20391] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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25
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Hinton B, Ma L, Mahmoudzadeh AP, Malkov S, Fan B, Greenwood H, Joe B, Lee V, Strand F, Kerlikowske K, Shepherd J. Derived mammographic masking measures based on simulated lesions predict the risk of interval cancer after controlling for known risk factors: a case-case analysis. Med Phys 2019; 46:1309-1316. [PMID: 30697755 DOI: 10.1002/mp.13410] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 01/13/2019] [Accepted: 01/17/2019] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Women with radiographically dense or texturally complex breasts are at increased risk for interval cancer, defined as cancers diagnosed after a normal screening examination. The purpose of this study was to create masking measures and apply them to identify interval risk in a population of women who experienced either screen-detected or interval cancers after controlling for breast density. METHODS We examined full-field digital screening mammograms acquired from 2006 to 2015. Examinations associated with 182 interval cancers were matched to 173 screen-detected cancers on age, race, exam date and time since last imaging examination. Local Image Quality Factor (IQF) values were calculated and used to create IQF maps that represented mammographic masking. We used various statistics to define global masking measures of these maps. Association of these masking measures with interval cancer vs screen-detected cancer was estimated using conditional logistic regression in a univariate and adjusted model for Breast Imaging-Reporting and Data System (BI-RADS) density. Receiver operator curves were calculated in each case to compare specificity vs sensitivity, and area under those curves were generated. Proportion of screen-detected cancer was estimated for stratifications of IQF features. RESULTS Several masking features showed significant association with interval compared to screen-detected cancers after adjusting for BI-RADS density (up to P = 2.52E-6), and the 10th percentile of the IQF value (P = 1.72E-3) showed the strongest improvement in the area under the receiver operator curve, increasing from 0.65 using only BI-RADS density to 0.69. The highest masking group had a 32% proportion of screen-detected cancers while the low masking group had a 69% proportion. CONCLUSIONS We conclude that computer vision methods using model observers may improve quantifying the probability of breast cancer detection beyond using breast density alone.
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Affiliation(s)
- Benjamin Hinton
- Department of Bioengineering, UC-San Francisco & UC-Berkeley Joint Program, San Francisco, CA, 94143, USA.,Department of Radiology and Biomedical Imaging, UC-San Francisco, San Francisco, CA, 94143, USA
| | - Lin Ma
- Kaiser Permanente Division of Research, Oakland, CA, 94612, USA
| | - Amir Pasha Mahmoudzadeh
- Department of Radiology and Biomedical Imaging, UC-San Francisco, San Francisco, CA, 94143, USA
| | | | - Bo Fan
- Department of Bioengineering, UC-San Francisco & UC-Berkeley Joint Program, San Francisco, CA, 94143, USA
| | - Heather Greenwood
- Department of Radiology and Biomedical Imaging, UC-San Francisco, San Francisco, CA, 94143, USA
| | - Bonnie Joe
- Department of Radiology and Biomedical Imaging, UC-San Francisco, San Francisco, CA, 94143, USA
| | - Vivian Lee
- Research Advocate, UCSF Breast Science Advocacy Core, San Francisco, CA, 94143, USA
| | - Fredrik Strand
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.,Department of Thoracic Radiology, Karolinska University Hospital, Solna, Sweden
| | - Karla Kerlikowske
- Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, 94143, USA
| | - John Shepherd
- University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
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Wengert GJ, Helbich TH, Leithner D, Morris EA, Baltzer PAT, Pinker K. Multimodality Imaging of Breast Parenchymal Density and Correlation with Risk Assessment. CURRENT BREAST CANCER REPORTS 2019; 11:23-33. [PMID: 35496471 PMCID: PMC9044508 DOI: 10.1007/s12609-019-0302-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Purpose of Review Breast density, or the amount of fibroglandular tissue in the breast, has become a recognized and independent marker for breast cancer risk. Public awareness of breast density as a possible risk factor for breast cancer has resulted in legislation for risk stratification purposes in many US states. This review will provide a comprehensive overview of the currently available imaging modalities for qualitative and quantitative breast density assessment and the current evidence on breast density and breast cancer risk assessment. Recent Findings To date, breast density assessment is mainly performed with mammography and to some extent with magnetic resonance imaging. Data indicate that computerized, quantitative techniques in comparison with subjective visual estimations are characterized by higher reproducibility and robustness. Summary Breast density reduces the sensitivity of mammography due to a masking effect and is also a recognized independent risk factor for breast cancer. Standardized breast density assessment using automated volumetric quantitative methods has the potential to be used for risk prediction and stratification and in determining the best screening plan for each woman.
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Wengert GJ, Helbich TH, Kapetas P, Baltzer PA, Pinker K. Density and tailored breast cancer screening: practice and prediction - an overview. Acta Radiol Open 2018; 7:2058460118791212. [PMID: 30245850 PMCID: PMC6144518 DOI: 10.1177/2058460118791212] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2018] [Accepted: 06/27/2018] [Indexed: 01/13/2023] Open
Abstract
Mammography, as the primary screening modality, has facilitated a substantial
decrease in breast cancer-related mortality in the general population. However,
the sensitivity of mammography for breast cancer detection is decreased in women
with higher breast densities, which is an independent risk factor for breast
cancer. With increasing public awareness of the implications of a high breast
density, there is an increasing demand for supplemental screening in these
patients. Yet, improvements in breast cancer detection with supplemental
screening methods come at the expense of increased false-positives, recall
rates, patient anxiety, and costs. Therefore, breast cancer screening practice
must change from a general one-size-fits-all approach to a more personalized,
risk-based one that is tailored to the individual woman’s risk, personal
beliefs, and preferences, while accounting for cost, potential harm, and
benefits. This overview will provide an overview of the available breast density assessment
modalities, the current breast density screening recommendations for women at
average risk of breast cancer, and supplemental methods for breast cancer
screening. In addition, we will provide a look at the possibilities for a
risk-adapted breast cancer screening.
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Affiliation(s)
- Georg J Wengert
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - Pascal At Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - Katja Pinker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria.,Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
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Prediction of glandularity and breast radiation dose from mammography results in Japanese women. Med Biol Eng Comput 2018; 57:289-298. [PMID: 30099671 DOI: 10.1007/s11517-018-1882-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 07/30/2018] [Indexed: 02/07/2023]
Abstract
Glandularity has a marked impact on the incidence of breast cancer and the missed lesion rate of mammography. The aim of this study was to develop a novel model for predicting glandularity and patient radiation dose using physical factors that are easily determined prior to mammography. Data regarding glandularity and mean glandular dose were obtained from 331 mammograms. A stepwise multiple regression analysis model was developed to predict glandularity using age, compressed breast thickness and body mass index (BMI), while a model to predict mean glandular dose was created using quantified glandularity, age, compressed breast thickness, height and body weight. The most significant factor for predicting glandularity was age, the influence of which was 1.8 times that of BMI. The most significant factor for predicting mean glandular dose was compressed breast thickness, the influence of which was 1.4 times that of glandularity, 3.5 times that of age and 6.1 times that of height. Both models were statistically significant (both p < 0.0001). Easily determined physical factors were able to explain 42.8% of the total variance in glandularity and 62.4% of the variance in mean glandular dose. Graphical abstract Validation results of the above prediction model made using physical factors in Japanese women. The plotted points of actual vs. prediction glandularity shown in a are distributed in the vicinity of the diagonal line, and the residual plot for predicted glandularity shows an almost random distribution as shown in b. These distributions indicate the appropriateness of the prediction model.
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Badal A, Clark M, Ghammraoui B. Reproducing two-dimensional mammograms with three-dimensional printed phantoms. J Med Imaging (Bellingham) 2018; 5:033501. [PMID: 30035152 DOI: 10.1117/1.jmi.5.3.033501] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Accepted: 06/06/2018] [Indexed: 01/06/2023] Open
Abstract
Mammography is currently the standard imaging modality used to screen women for breast abnormalities, and, as a result, it is a tool of great importance for the early detection of breast cancer. Physical phantoms are commonly used as surrogates of breast tissue to evaluate some aspects of the performance of mammography systems. However, most phantoms do not reproduce the anatomic heterogeneity of real breasts. New fabrication technologies, such as three-dimensional (3-D) printing, have created the opportunity to build more complex, anatomically realistic breast phantoms that could potentially assist in the evaluation of mammography systems. The reproducibility and relative low cost of 3-D printed objects might also enable the development of collections of representative patient models that could be used to assess the effect of anatomical variability on system performance, hence making bench testing studies a step closer to clinical trials. The primary objective of this work is to present a simple, easily reproducible methodology to design and print 3-D objects that replicate the attenuation profile observed in real two-dimensional mammograms. The secondary objective is to evaluate the capabilities and limitations of the competing 3-D printing technologies and characterize the x-ray properties of the different materials they use. Printable phantoms can be created using the open-source code introduced, which processes a raw mammography image to estimate the amount of x-ray attenuation at each pixel, and outputs a triangle mesh object that encodes the observed attenuation map. The conversion from the observed pixel gray value to a column of printed material with equivalent attenuation requires certain assumptions and knowledge of multiple imaging system parameters, such as x-ray energy spectrum, source-to-object distance, compressed breast thickness, and average breast material attenuation. To validate the proposed methodology, x-ray projections of printed phantoms were acquired with a clinical mammography system. The quality of the printing process was evaluated by comparing the mammograms of the printed phantoms and the original mammograms used to create the phantoms. The structural similarity index and the root-mean-square error were used as objective metrics to compare the two images. A detailed description of the software, a characterization of the printed materials using x-ray spectroscopy, and an evaluation of the realism of the sample printed phantoms are presented.
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Affiliation(s)
- Andreu Badal
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics and Software Reliability, Silver Spring, Maryland, United States
| | - Matthew Clark
- University of Maryland College Park, Department of Mechanical Engineering, College Park, Maryland, United States
| | - Bahaa Ghammraoui
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics and Software Reliability, Silver Spring, Maryland, United States
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Hjerkind KV, Ellingjord-Dale M, Johansson AL, Aase HS, Hoff SR, Hofvind S, Fagerheim S, dos-Santos-Silva I, Ursin G. Volumetric Mammographic Density, Age-Related Decline, and Breast Cancer Risk Factors in a National Breast Cancer Screening Program. Cancer Epidemiol Biomarkers Prev 2018; 27:1065-1074. [DOI: 10.1158/1055-9965.epi-18-0151] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 04/25/2018] [Accepted: 06/15/2018] [Indexed: 11/16/2022] Open
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Ghammraoui B, Badal A, Glick SJ. Feasibility of estimating volumetric breast density from mammographic x-ray spectra using a cadmium telluride photon-counting detector. Med Phys 2018; 45:3604-3613. [PMID: 29862520 DOI: 10.1002/mp.13031] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 04/24/2018] [Accepted: 04/26/2018] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Mammographic density of glandular breast tissue has a masking effect that can reduce lesion detection accuracy and is also a strong risk factor for breast cancer. Therefore, accurate quantitative estimation of breast density is clinically important. In this study, we investigate experimentally the feasibility of quantifying volumetric breast density with spectral mammography using a CdTe-based photon-counting detector. METHODS To demonstrate proof-of-principle, this study was carried out using the single pixel Amptek XR-100T-CdTe detector. The total number of x rays recorded by the detector from a single pencil-beam projection through 50%/50% of adipose/glandular mass fraction-equivalent phantoms was measured. Material decomposition assuming two, four, and eight energy bins was then applied to characterize the inspected phantom into adipose and glandular using log-likelihood estimation, taking into account the polychromatic source, the detector response function, and the energy-dependent attenuation. RESULTS Measurement tests were carried out for different doses, kVp settings, and different breast sizes. For dose of 1 mGy and above, the percent relative root mean square (RMS) errors of the estimated breast density was measured below 7% for all three phantom studies. It was also observed that some decrease in RMS errors was achieved using eight energy bins. For 3 and 4 cm thick phantoms, performance at 40 and 45 kVp showed similar performance. However, it was observed that 45 kVp showed better performance for a phantom thickness of 6 cm at low dose levels due to increased statistical variation at lower photon count levels with 40 kVp. CONCLUSION The results of the current study suggest that photon-counting spectral mammography systems using CdTe detectors have the potential to be used for accurate quantification of volumetric breast density on a pixel-to-pixel basis, with an RMS error of less than 7%.
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Affiliation(s)
- Bahaa Ghammraoui
- Office of Science and Engineering Laboratories, CDRH, U.S. Food and Drug Administration, Silver Spring, MD, 20993-0002, USA
| | - Andreu Badal
- Office of Science and Engineering Laboratories, CDRH, U.S. Food and Drug Administration, Silver Spring, MD, 20993-0002, USA
| | - Stephen J Glick
- Office of Science and Engineering Laboratories, CDRH, U.S. Food and Drug Administration, Silver Spring, MD, 20993-0002, USA
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Brand JS, Humphreys K, Li J, Karlsson R, Hall P, Czene K. Common genetic variation and novel loci associated with volumetric mammographic density. Breast Cancer Res 2018; 20:30. [PMID: 29665850 PMCID: PMC5904990 DOI: 10.1186/s13058-018-0954-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Accepted: 03/09/2018] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Mammographic density (MD) is a strong and heritable intermediate phenotype of breast cancer, but much of its genetic variation remains unexplained. METHODS We conducted a genetic association study of volumetric MD in a Swedish mammography screening cohort (n = 9498) to identify novel MD loci. Associations with volumetric MD phenotypes (percent dense volume, absolute dense volume, and absolute nondense volume) were estimated using linear regression adjusting for age, body mass index, menopausal status, and six principal components. We also estimated the proportion of MD variance explained by additive contributions from single-nucleotide polymorphisms (SNP-based heritability [h2SNP]) in 4948 participants of the cohort. RESULTS In total, three novel MD loci were identified (at P < 5 × 10- 8): one for percent dense volume (HABP2) and two for the absolute dense volume (INHBB, LINC01483). INHBB is an established locus for ER-negative breast cancer, and HABP2 and LINC01483 represent putative new breast cancer susceptibility loci, because both loci were associated with breast cancer in available meta-analysis data including 122,977 breast cancer cases and 105,974 control subjects (P < 0.05). h2SNP (SE) estimates for percent dense, absolute dense, and nondense volume were 0.29 (0.07), 0.31 (0.07), and 0.25 (0.07), respectively. Corresponding ratios of h2SNP to previously observed narrow-sense h2 estimates in the same cohort were 0.46, 0.72, and 0.41, respectively. CONCLUSIONS These findings provide new insights into the genetic basis of MD and biological mechanisms linking MD to breast cancer risk. Apart from identifying three novel loci, we demonstrate that at least 25% of the MD variance is explained by common genetic variation with h2SNP/h2 ratios varying between dense and nondense MD components.
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Affiliation(s)
- Judith S Brand
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden.
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
| | - Jingmei Li
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden.,Human Genetics, Genome Institute of Singapore, Singapore, Singapore
| | - Robert Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
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Garcia E, Diez Y, Diaz O, Llado X, Gubern-Merida A, Marti R, Marti J, Oliver A. Multimodal Breast Parenchymal Patterns Correlation Using a Patient-Specific Biomechanical Model. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:712-723. [PMID: 28885152 DOI: 10.1109/tmi.2017.2749685] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In this paper, we aim to produce a realistic 2-D projection of the breast parenchymal distribution from a 3-D breast magnetic resonance image (MRI). To evaluate the accuracy of our simulation, we compare our results with the local breast density (i.e., density map) obtained from the complementary full-field digital mammogram. To achieve this goal, we have developed a fully automatic framework, which registers MRI volumes to X-ray mammograms using a subject-specific biomechanical model of the breast. The optimization step modifies the position, orientation, and elastic parameters of the breast model to perform the alignment between the images. When the model reaches an optimal solution, the MRI glandular tissue is projected and compared with the one obtained from the corresponding mammograms. To reduce the loss of information during the ray-casting, we introduce a new approach that avoids resampling the MRI volume. In the results, we focus our efforts on evaluating the agreement of the distributions of glandular tissue, the degree of structural similarity, and the correlation between the real and synthetic density maps. Our approach obtained a high-structural agreement regardless the glandularity of the breast, whilst the similarity of the glandular tissue distributions and correlation between both images increase in denser breasts. Furthermore, the synthetic images show continuity with respect to large structures in the density maps.
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Lee J, Nishikawa RM. Automated mammographic breast density estimation using a fully convolutional network. Med Phys 2018; 45:1178-1190. [PMID: 29363774 DOI: 10.1002/mp.12763] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 12/06/2017] [Accepted: 12/29/2017] [Indexed: 11/11/2022] Open
Abstract
PURPOSE The purpose of this study was to develop a fully automated algorithm for mammographic breast density estimation using deep learning. METHOD Our algorithm used a fully convolutional network, which is a deep learning framework for image segmentation, to segment both the breast and the dense fibroglandular areas on mammographic images. Using the segmented breast and dense areas, our algorithm computed the breast percent density (PD), which is the faction of dense area in a breast. Our dataset included full-field digital screening mammograms of 604 women, which included 1208 mediolateral oblique (MLO) and 1208 craniocaudal (CC) views. We allocated 455, 58, and 91 of 604 women and their exams into training, testing, and validation datasets, respectively. We established ground truth for the breast and the dense fibroglandular areas via manual segmentation and segmentation using a simple thresholding based on BI-RADS density assessments by radiologists, respectively. Using the mammograms and ground truth, we fine-tuned a pretrained deep learning network to train the network to segment both the breast and the fibroglandular areas. Using the validation dataset, we evaluated the performance of the proposed algorithm against radiologists' BI-RADS density assessments. Specifically, we conducted a correlation analysis between a BI-RADS density assessment of a given breast and its corresponding PD estimate by the proposed algorithm. In addition, we evaluated our algorithm in terms of its ability to classify the BI-RADS density using PD estimates, and its ability to provide consistent PD estimates for the left and the right breast and the MLO and CC views of the same women. To show the effectiveness of our algorithm, we compared the performance of our algorithm against a state of the art algorithm, laboratory for individualized breast radiodensity assessment (LIBRA). RESULT The PD estimated by our algorithm correlated well with BI-RADS density ratings by radiologists. Pearson's rho values of our algorithm for CC view, MLO view, and CC-MLO-averaged were 0.81, 0.79, and 0.85, respectively, while those of LIBRA were 0.58, 0.71, and 0.69, respectively. For CC view and CC-MLO averaged cases, the difference in rho values between the proposed algorithm and LIBRA showed statistical significance (P < 0.006). In addition, our algorithm provided reliable PD estimates for the left and the right breast (Pearson's ρ > 0.87) and for the MLO and CC views (Pearson's ρ = 0.76). However, LIBRA showed a lower Pearson's rho value (0.66) for both the left and right breasts for the CC view. In addition, our algorithm showed an excellent ability to separate each sub BI-RADS breast density class (statistically significant, p-values = 0.0001 or less); only one comparison pair, density 1 and density 2 in the CC view, was not statistically significant (P = 0.54). However, LIBRA failed to separate breasts in density 1 and 2 for both the CC and MLO views (P > 0.64). CONCLUSION We have developed a new deep learning based algorithm for breast density segmentation and estimation. We showed that the proposed algorithm correlated well with BI-RADS density assessments by radiologists and outperformed an existing state of the art algorithm.
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Affiliation(s)
- Juhun Lee
- Department of Radiology, University of Pittsburgh, 3362 Fifth Ave.,, Pittsburgh, PA, 15213, USA
| | - Robert M Nishikawa
- Department of Radiology, University of Pittsburgh, 3362 Fifth Ave.,, Pittsburgh, PA, 15213, USA
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Giger ML. Machine Learning in Medical Imaging. J Am Coll Radiol 2018; 15:512-520. [PMID: 29398494 DOI: 10.1016/j.jacr.2017.12.028] [Citation(s) in RCA: 251] [Impact Index Per Article: 41.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 12/20/2017] [Indexed: 12/12/2022]
Abstract
Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. A brief overview of the field is given here, allowing the reader to recognize the terminology, the various subfields, and components of machine learning, as well as the clinical potential. Radiomics, an expansion of computer-aided diagnosis, has been defined as the conversion of images to minable data. The ultimate benefit of quantitative radiomics is to (1) yield predictive image-based phenotypes of disease for precision medicine or (2) yield quantitative image-based phenotypes for data mining with other -omics for discovery (ie, imaging genomics). For deep learning in radiology to succeed, note that well-annotated large data sets are needed since deep networks are complex, computer software and hardware are evolving constantly, and subtle differences in disease states are more difficult to perceive than differences in everyday objects. In the future, machine learning in radiology is expected to have a substantial clinical impact with imaging examinations being routinely obtained in clinical practice, providing an opportunity to improve decision support in medical image interpretation. The term of note is decision support, indicating that computers will augment human decision making, making it more effective and efficient. The clinical impact of having computers in the routine clinical practice may allow radiologists to further integrate their knowledge with their clinical colleagues in other medical specialties and allow for precision medicine.
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Affiliation(s)
- Maryellen L Giger
- Department of Radiology, The University of Chicago, Chicago, Illinois.
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36
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Moshina N, Roman M, Sebuødegård S, Waade GG, Ursin G, Hofvind S. Comparison of subjective and fully automated methods for measuring mammographic density. Acta Radiol 2018; 59:154-160. [PMID: 28565960 DOI: 10.1177/0284185117712540] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Background Breast radiologists of the Norwegian Breast Cancer Screening Program subjectively classified mammographic density using a three-point scale between 1996 and 2012 and changed into the fourth edition of the BI-RADS classification since 2013. In 2015, an automated volumetric breast density assessment software was installed at two screening units. Purpose To compare volumetric breast density measurements from the automated method with two subjective methods: the three-point scale and the BI-RADS density classification. Material and Methods Information on subjective and automated density assessment was obtained from screening examinations of 3635 women recalled for further assessment due to positive screening mammography between 2007 and 2015. The score of the three-point scale (I = fatty; II = medium dense; III = dense) was available for 2310 women. The BI-RADS density score was provided for 1325 women. Mean volumetric breast density was estimated for each category of the subjective classifications. The automated software assigned volumetric breast density to four categories. The agreement between BI-RADS and volumetric breast density categories was assessed using weighted kappa (kw). Results Mean volumetric breast density was 4.5%, 7.5%, and 13.4% for categories I, II, and III of the three-point scale, respectively, and 4.4%, 7.5%, 9.9%, and 13.9% for the BI-RADS density categories, respectively ( P for trend < 0.001 for both subjective classifications). The agreement between BI-RADS and volumetric breast density categories was kw = 0.5 (95% CI = 0.47-0.53; P < 0.001). Conclusion Mean values of volumetric breast density increased with increasing density category of the subjective classifications. The agreement between BI-RADS and volumetric breast density categories was moderate.
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Affiliation(s)
| | | | | | - Gunvor G Waade
- Oslo and Akershus University College of Applied Sciences, Faculty of Health Science, Oslo, Norway
| | - Giske Ursin
- Cancer Registry of Norway, Oslo, Norway
- Institute of Basic Medical Sciences, Medical Faculty, University of Oslo, Oslo, Norway
- Department of Preventive Medicine, University of Southern California, CA, USA
| | - Solveig Hofvind
- Cancer Registry of Norway, Oslo, Norway
- Oslo and Akershus University College of Applied Sciences, Faculty of Health Science, Oslo, Norway
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陈 美, 陶 熙, 李 华, 陈 武, 张 华. [Low-dose digital breast tomosynthsis imaging via noise correlation based penalized weighted least-squares algorithm]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2018; 38:48-54. [PMID: 33177026 PMCID: PMC6765612 DOI: 10.3969/j.issn.1673-4254.2018.01.08] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 05/07/2017] [Indexed: 06/11/2023]
Abstract
OBJECTIVE To achieve low-dose digital breast tomosynthsis (DBT) projection recovery using penalized weighted least square algorithm incorporating accurate modeling of the variance of the projection data and noise correlation in the flat panel detector. METHODS Models were established for the quantal noise and electronic noise in the DBT system to construct the penalized weighted least squares algorithm based on noise correlation for projection data restoration. The filter back projection algorithm was then used for DBT image reconstruction. RESULTS The reconstruction results of the ACR phantom data at different dose levels showed a good performance of the proposed method in noise suppression and detail preservation. CNRs and LSNRs of the reconstructed images from the restored projections were increased by about 3.6 times compared to those of reconstructed images from the original projections. CONCLUSIONS The proposed method can significantly reduce noise and improve the quality of DBT images.
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Affiliation(s)
- 美玲 陈
- 南方医科大学 生物医学工程学院,广东 广州 510515Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- 南方医科大学 广东省医学图像处理重点实验室,广东 广州 510515Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China
| | - 熙 陶
- 南方医科大学 生物医学工程学院,广东 广州 510515Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- 南方医科大学 广东省医学图像处理重点实验室,广东 广州 510515Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China
| | - 华勇 李
- 南方医科大学 生物医学工程学院,广东 广州 510515Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- 南方医科大学 广东省医学图像处理重点实验室,广东 广州 510515Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China
| | - 武凡 陈
- 南方医科大学 生物医学工程学院,广东 广州 510515Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- 南方医科大学 广东省医学图像处理重点实验室,广东 广州 510515Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China
| | - 华 张
- 南方医科大学 生物医学工程学院,广东 广州 510515Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- 南方医科大学 广东省医学图像处理重点实验室,广东 广州 510515Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China
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Raj SD, Fein-Zachary V, Slanetz PJ. Deciphering the Breast Density Inform Law Movement: Implications for Practice. Semin Ultrasound CT MR 2018; 39:16-24. [PMID: 29317035 DOI: 10.1053/j.sult.2017.08.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Although dense breast tissue is a normal and routine finding on screening mammography, dense breast tissue is associated with an independent increased risk for breast cancer. It is well known that screening mammography has a decreased sensitivity for cancer detection in women with dense breasts. Over the past decade, there has been increased interest generated among patients, physicians, and legislators regarding how best to screen dense-breasted women culminating in 2009 with the passage of a breast density notification law in Connecticut. Since that time, over half the United States has passed similar notification laws. Despite this, controversy remains as to the optimal supplemental screening modality to complement mammography as each imaging modality (digital breast tomosynthesis, whole breast ultrasound, magnetic resonance imaging, contrast-enhanced mammography, and molecular breast imaging) has variable benefits and limitations.
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Affiliation(s)
- Sean D Raj
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Valerie Fein-Zachary
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Priscilla J Slanetz
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
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Thakran S, Chatterjee S, Singhal M, Gupta RK, Singh A. Automatic outer and inner breast tissue segmentation using multi-parametric MRI images of breast tumor patients. PLoS One 2018; 13:e0190348. [PMID: 29320532 PMCID: PMC5761869 DOI: 10.1371/journal.pone.0190348] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Accepted: 12/13/2017] [Indexed: 11/22/2022] Open
Abstract
The objectives of the study were to develop a framework for automatic outer and inner breast tissue segmentation using multi-parametric MRI images of the breast tumor patients; and to perform breast density and tumor tissue analysis. MRI of the breast was performed on 30 patients at 3T-MRI. T1, T2 and PD-weighted(W) images, with and without fat saturation(WWFS), and dynamic-contrast-enhanced(DCE)-MRI data were acquired. The proposed automatic segmentation approach was performed in two steps. In step-1, outer segmentation of breast tissue from rest of body parts was performed on structural images (T2-W/T1-W/PD-W without fat saturation images) using automatic landmarks detection technique based on operations like profile screening, Otsu thresholding, morphological operations and empirical observation. In step-2, inner segmentation of breast tissue into fibro-glandular(FG), fatty and tumor tissue was performed. For validation of breast tissue segmentation, manual segmentation was carried out by two radiologists and similarity coefficients(Dice and Jaccard) were computed for outer as well as inner tissues. FG density and tumor volume were also computed and analyzed. The proposed outer and inner segmentation approach worked well for all the subjects and was validated by two radiologists. The average Dice and Jaccard coefficients value for outer segmentation using T2-W images, obtained by two radiologists, were 0.977 and 0.951 respectively. These coefficient values for FG tissue were 0.915 and 0.875 respectively whereas for tumor tissue, values were 0.968 and 0.95 respectively. The volume of segmented tumor ranged over 2.1 cm3–7.08 cm3. The proposed approach provided automatic outer and inner breast tissue segmentation, which enables automatic calculations of breast tissue density and tumor volume. This is a complete framework for outer and inner breast segmentation method for all structural images.
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Affiliation(s)
- Snekha Thakran
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Subhajit Chatterjee
- Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Meenakshi Singhal
- Department of Radiology, Fortis Memorial Research Institute, Gurgaon, India
| | - Rakesh Kumar Gupta
- Department of Radiology, Fortis Memorial Research Institute, Gurgaon, India
| | - Anup Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India.,Department of Biomedical Engineering, All India Institute of Medical Sciences Delhi, New Delhi, India
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Rahbar K, Gubern-Merida A, Patrie JT, Harvey JA. Automated Volumetric Mammographic Breast Density Measurements May Underestimate Percent Breast Density for High-density Breasts. Acad Radiol 2017; 24:1561-1569. [PMID: 28754209 DOI: 10.1016/j.acra.2017.06.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 06/20/2017] [Accepted: 06/20/2017] [Indexed: 01/22/2023]
Abstract
RATIONALE AND OBJECTIVES The purpose of this study was to evaluate discrepancy in breast composition measurements obtained from mammograms using two commercially available software methods for systematic trends in overestimation or underestimation compared to magnetic resonance-derived measurements. MATERIALS AND METHODS An institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study was performed to calculate percent breast density (PBD) by quantifying fibroglandular volume and total breast volume derived from magnetic resonance imaging (MRI) segmentation and mammograms using two commercially available software programs (Volpara and Quantra). Consecutive screening MRI exams from a 6-month period with negative or benign findings were used. The most recent mammogram within 9 months was used to derive mean density values from "for processing" images at the per breast level. Bland-Altman statistical analyses were performed to determine the mean discrepancy and the limits of agreement. RESULTS A total of 110 women with 220 breasts met the study criteria. Overall, PBD was not different between MRI (mean 10%, range 1%-41%) and Volpara (mean 10%, range 3%-29%); a small but significant difference was present in the discrepancy between MRI and Quantra (4.0%, 95% CI: 2.9 to 5.0, P < 0.001). Discrepancy was highest at higher breast densities, with Volpara slightly underestimating and Quantra slightly overestimating PBD compared to MRI. The mean discrepancy for both Volpara and Quantra for total breast volume was not significantly different from MRI (p = 0.89, 0.35, respectively). Volpara tended to underestimate, whereas Quantra tended to overestimate fibroglandular volume, with the highest discrepancy at higher breast volumes. CONCLUSIONS Both Volpara and Quantra tend to underestimate PBD, which is most pronounced at higher densities. PBD can be accurately measured using automated volumetric software programs, but values should not be used interchangeably between vendors.
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Affiliation(s)
- Kareem Rahbar
- Roper Radiologists, P.A., Charleston, South Carolina
| | - Albert Gubern-Merida
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - James T Patrie
- Department of Biostatistics, University of Virginia Health System, Charlottesville, Virginia
| | - Jennifer A Harvey
- Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA 22908.
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Breast compression parameters and mammographic density in the Norwegian Breast Cancer Screening Programme. Eur Radiol 2017; 28:1662-1672. [DOI: 10.1007/s00330-017-5104-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 08/30/2017] [Accepted: 09/28/2017] [Indexed: 10/18/2022]
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RUIZ JESSICA, NOUIZI FAROUK, CHO JAEDU, ZHENG JIE, LI YIFAN, CHEN JEONHOR, SU MINYING, GULSEN GULTEKIN. Breast density quantification using structured-light-based diffuse optical tomography simulations. APPLIED OPTICS 2017; 56:7146-7157. [PMID: 29047975 PMCID: PMC6691974 DOI: 10.1364/ao.56.007146] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Accepted: 07/26/2017] [Indexed: 05/08/2023]
Abstract
We present the feasibility of structured-light-based diffuse optical tomography (DOT) to quantify the breast density with an extensive simulation study. This study is performed on multiple numerical breast phantoms built from magnetic resonance imaging (MRI) images. These phantoms represent realistic tissue morphologies and are given typical breast optical properties. First, synthetic data are simulated at five wavelengths using our structured-light-based DOT forward problem. Afterwards, the inverse problem is solved to obtain the absorption images and subsequently the chromophore concentration maps. Parameters, such as segmented volumes and mean concentrations, are extracted from these maps and used in a regression model to estimate the percent breast densities. These estimations are correlated with the true values from MRI, r=0.97, showing that our new technique is promising in measuring breast density.
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Affiliation(s)
- JESSICA RUIZ
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697, USA
| | - FAROUK NOUIZI
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697, USA
| | - JAEDU CHO
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697, USA
| | - JIE ZHENG
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697, USA
| | - YIFAN LI
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697, USA
| | - JEON-HOR CHEN
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697, USA
- E-Da Hospital and I-Shou University, Kaohsiung, Taiwan
| | - MIN-YING SU
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697, USA
| | - GULTEKIN GULSEN
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697, USA
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Fibroglandular Tissue Quantification in Mammography by Optimized Fuzzy C-Means with Variable Compactness. Ing Rech Biomed 2017. [DOI: 10.1016/j.irbm.2017.05.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Castillo-García M, Chevalier M, Garayoa J, Rodriguez-Ruiz A, García-Pinto D, Valverde J. Automated Breast Density Computation in Digital Mammography and Digital Breast Tomosynthesis: Influence on Mean Glandular Dose and BIRADS Density Categorization. Acad Radiol 2017; 24:802-810. [PMID: 28214227 DOI: 10.1016/j.acra.2017.01.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2016] [Revised: 12/16/2016] [Accepted: 01/08/2017] [Indexed: 12/20/2022]
Abstract
RATIONALE AND OBJECTIVES The study aimed to compare the breast density estimates from two algorithms on full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) and to analyze the clinical implications. MATERIALS AND METHODS We selected 561 FFDM and DBT examinations from patients without breast pathologies. Two versions of a commercial software (Quantra 2D and Quantra 3D) calculated the volumetric breast density automatically in FFDM and DBT, respectively. Other parameters such as area breast density and total breast volume were evaluated. We compared the results from both algorithms using the Mann-Whitney U non-parametric test and the Spearman's rank coefficient for data correlation analysis. Mean glandular dose (MGD) was calculated following the methodology proposed by Dance et al. RESULTS Measurements with both algorithms are well correlated (r ≥ 0.77). However, there are statistically significant differences between the medians (P < 0.05) of most parameters. The volumetric and area breast density median values from FFDM are, respectively, 8% and 77% higher than DBT estimations. Both algorithms classify 35% and 55% of breasts into BIRADS (Breast Imaging-Reporting and Data System) b and c categories, respectively. There are no significant differences between the MGD calculated using the breast density from each algorithm. DBT delivers higher MGD than FFDM, with a lower difference (5%) for breasts in the BIRADS d category. MGD is, on average, 6% higher than values obtained with the breast glandularity proposed by Dance et al. CONCLUSIONS Breast density measurements from both algorithms lead to equivalent BIRADS classification and MGD values, hence showing no difference in clinical outcomes. The median MGD values of FFDM and DBT examinations are similar for dense breasts (BIRADS d category).
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Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography. Med Phys 2017; 44:3726-3738. [DOI: 10.1002/mp.12316] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Revised: 04/11/2017] [Accepted: 04/26/2017] [Indexed: 11/07/2022] Open
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O'Flynn EA, Fromageau J, Ledger AE, Messa A, D'Aquino A, Schoemaker MJ, Schmidt M, Duric N, Swerdlow AJ, Bamber JC. Ultrasound Tomography Evaluation of Breast Density: A Comparison With Noncontrast Magnetic Resonance Imaging. Invest Radiol 2017; 52:343-348. [PMID: 28121639 PMCID: PMC5417582 DOI: 10.1097/rli.0000000000000347] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Indexed: 01/17/2023]
Abstract
OBJECTIVES Ultrasound tomography (UST) is an emerging whole-breast 3-dimensional imaging technique that obtains quantitative tomograms of speed of sound of the entire breast. The imaged parameter is the speed of sound which is used as a surrogate measure of density at each voxel and holds promise as a method to evaluate breast density without ionizing radiation. This study evaluated the technique of UST and compared whole-breast volume averaged speed of sound (VASS) with MR percent water content from noncontrast magnetic resonance imaging (MRI). MATERIALS AND METHODS Forty-three healthy female volunteers (median age, 40 years; range, 29-59 years) underwent bilateral breast UST and MRI using a 2-point Dixon technique. Reproducibility of VASS was evaluated using Bland-Altman analysis. Volume averaged speed of sound and MR percent water were evaluated and compared using Pearson correlation coefficient. RESULTS The mean ± standard deviation VASS measurement was 1463 ± 29 m s (range, 1434-1542 m s). There was high similarity between right (1464 ± 30 m s) and left (1462 ± 28 m s) breasts (P = 0.113) (intraclass correlation coefficient, 0.98). Mean MR percent water content was 35.7% ± 14.7% (range, 13.2%-75.3%), with small but significant differences between right and left breasts (36.3% ± 14.9% and 35.1% ± 14.7%, respectively; P = 0.004). There was a very strong correlation between VASS and MR percent water density (r = 0.96, P < 0.0001). CONCLUSIONS Ultrasound tomography holds promise as a reliable and reproducible 3-dimensional technique to provide a surrogate measure of breast density and correlates strongly with MR percent water content.
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Affiliation(s)
- Elizabeth A.M. O'Flynn
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Jeremie Fromageau
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Araminta E. Ledger
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Alessandro Messa
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Ashley D'Aquino
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Minouk J. Schoemaker
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Maria Schmidt
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Neb Duric
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Anthony J. Swerdlow
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Jeffrey C. Bamber
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
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Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad. Diagnostics (Basel) 2017; 7:diagnostics7020030. [PMID: 28561776 PMCID: PMC5489950 DOI: 10.3390/diagnostics7020030] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 05/22/2017] [Accepted: 05/24/2017] [Indexed: 12/14/2022] Open
Abstract
Mammographic breast density (MBD) has been proven to be an important risk factor for breast cancer and an important determinant of mammographic screening performance. The measurement of density has changed dramatically since its inception. Initial qualitative measurement methods have been found to have limited consistency between readers, and in regards to breast cancer risk. Following the introduction of full-field digital mammography, more sophisticated measurement methodology is now possible. Automated computer-based density measurements can provide consistent, reproducible, and objective results. In this review paper, we describe various methods currently available to assess MBD, and provide a discussion on the clinical utility of such methods for breast cancer screening.
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García E, Diaz O, Martí R, Diez Y, Gubern-Mérida A, Sentís M, Martí J, Oliver A. Local breast density assessment using reacquired mammographic images. Eur J Radiol 2017; 93:121-127. [PMID: 28668405 DOI: 10.1016/j.ejrad.2017.05.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 05/19/2017] [Accepted: 05/23/2017] [Indexed: 11/17/2022]
Abstract
PURPOSE The aim of this paper is to evaluate the spatial glandular volumetric tissue distribution as well as the density measures provided by Volpara™ using a dataset composed of repeated pairs of mammograms, where each pair was acquired in a short time frame and in a slightly changed position of the breast. MATERIALS AND METHODS We conducted a retrospective analysis of 99 pairs of repeatedly acquired full-field digital mammograms from 99 different patients. The commercial software Volpara™ Density Maps (Volpara Solutions, Wellington, New Zealand) is used to estimate both the global and the local glandular tissue distribution in each image. The global measures provided by Volpara™, such as breast volume, volume of glandular tissue, and volumetric breast density are compared between the two acquisitions. The evaluation of the local glandular information is performed using histogram similarity metrics, such as intersection and correlation, and local measures, such as statistics from the difference image and local gradient correlation measures. RESULTS Global measures showed a high correlation (breast volume R=0.99, volume of glandular tissue R=0.94, and volumetric breast density R=0.96) regardless the anode/filter material. Similarly, histogram intersection and correlation metric showed that, for each pair, the images share a high degree of information. Regarding the local distribution of glandular tissue, small changes in the angle of view do not yield significant differences in the glandular pattern, whilst changes in the breast thickness between both acquisition affect the spatial parenchymal distribution. CONCLUSIONS This study indicates that Volpara™ Density Maps is reliable in estimating the local glandular tissue distribution and can be used for its assessment and follow-up. Volpara™ Density Maps is robust to small variations of the acquisition angle and to the beam energy, although divergences arise due to different breast compression conditions.
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Affiliation(s)
- Eloy García
- Computer Vision and Robotics Institute, University of Girona, Spain
| | - Oliver Diaz
- Computer Vision and Robotics Institute, University of Girona, Spain
| | - Robert Martí
- Computer Vision and Robotics Institute, University of Girona, Spain
| | - Yago Diez
- Tokuyama Laboratory GSIS, Tohoku University, Sendai, Japan
| | | | - Melcior Sentís
- UDIAT - Centre Diagnòstic, Corporació Parc Taulí, Sabadell, Spain
| | - Joan Martí
- Computer Vision and Robotics Institute, University of Girona, Spain
| | - Arnau Oliver
- Computer Vision and Robotics Institute, University of Girona, Spain.
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Johansson H, von Tiedemann M, Erhard K, Heese H, Ding H, Molloi S, Fredenberg E. Breast-density measurement using photon-counting spectral mammography. Med Phys 2017; 44:3579-3593. [DOI: 10.1002/mp.12279] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Revised: 03/12/2017] [Accepted: 03/23/2017] [Indexed: 11/09/2022] Open
Affiliation(s)
- Henrik Johansson
- Philips Health Systems; Mammography Solutions; Torshamnsgatan 30A 164 40 Kista Sweden
| | - Miriam von Tiedemann
- Philips Health Systems; Mammography Solutions; Torshamnsgatan 30A 164 40 Kista Sweden
| | - Klaus Erhard
- Philips Research; Röntgenstrasse 24-26 22335 Hamburg Germany
| | - Harald Heese
- Philips Research; Röntgenstrasse 24-26 22335 Hamburg Germany
| | - Huanjun Ding
- Department of Radiological Sciences; University of California; Irvine CA 92697 USA
| | - Sabee Molloi
- Department of Radiological Sciences; University of California; Irvine CA 92697 USA
| | - Erik Fredenberg
- Philips Health Systems; Mammography Solutions; Torshamnsgatan 30A 164 40 Kista Sweden
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Holland K, Gubern-Mérida A, Mann RM, Karssemeijer N. Optimization of volumetric breast density estimation in digital mammograms. Phys Med Biol 2017; 62:3779-3797. [DOI: 10.1088/1361-6560/aa628f] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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