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Md Shah MN, Azman RR, Chan WY, Ng KH. Opportunistic Extraction of Quantitative CT Biomarkers: Turning the Incidental Into Prognostic Information. Can Assoc Radiol J 2024; 75:92-97. [PMID: 37075322 DOI: 10.1177/08465371231171700] [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] [Indexed: 04/21/2023] Open
Abstract
The past two decades have seen a significant increase in the use of CT, with a corresponding rise in the mean population radiation dose. This rise in CT use has caused improved diagnostic certainty in conditions that were not previously routinely evaluated using CT, such as headaches, back pain, and chest pain. Unused data, unrelated to the primary diagnosis, embedded within these scans have the potential to provide organ-specific measurements that can be used to prognosticate or risk-profile patients for a wide variety of conditions. The recent increased availability of computing power, expertise and software for automated segmentation and measurements, assisted by artificial intelligence, provides a conducive environment for the deployment of these analyses into routine use. Data gathering from CT has the potential to add value to examinations and help offset the public perception of harm from radiation exposure. We review the potential for the collection of these data and propose the incorporation of this strategy into routine clinical practice.
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Affiliation(s)
- Mohammad Nazri Md Shah
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Raja Rizal Azman
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Faculty of Medicine and Health Sciences, UCSI University, Springhill, Negri Sembilan, Malaysia
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Li Z, Wu L, Zhang J, Huang X, Thabane L, Li G. Effect of Vitamin D Supplementation on Risk of Breast Cancer: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Front Nutr 2021; 8:655727. [PMID: 33869269 PMCID: PMC8049142 DOI: 10.3389/fnut.2021.655727] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 02/23/2021] [Indexed: 12/18/2022] Open
Abstract
Objective: Laboratory findings indicated that vitamin D might have a potent protective effect on breast cancer, but epidemiology studies reported conflicting results. The aim of the study was to conduct a systematic review and meta-analysis to clarify the efficacy of vitamin D supplementation on risk of breast cancer. Methods: MEDLINE, EMBASE, The Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and abstracts of three major conferences were searched (up to December 8, 2020). Parallel randomized controlled trials (RCTs) examining the efficacy of vitamin D supplementation on risk of breast cancer or change of mammography compared with placebo in females were included. Data were meta-analyzed using a random-effects model. Bayesian meta-analysis was conducted to synthesize the results using data from observational studies as priors. Results: Seven RCTs were identified for effect of vitamin D on risk of breast cancer, with 19,137 females included for meta-analysis. No statistically significant effect of vitamin D on risk of breast cancer was found in classical random-effects meta-analysis (risk ratio = 1.04, 95% confidence interval: 0.84–1.28, p = 0.71). When Bayesian meta-analyses were conducted, results remained non-significant. There was no statistically significant effect of vitamin D on mammography density observed: mean difference = 0.46, 95% confidence interval: −2.06 to 2.98, p = 0.72. Conclusion: There is insufficient evidence to support the efficacy of vitamin D supplementation in breast cancer risk and change of mammography density. The protective effect of vitamin D on risk of breast cancer from previous observational studies may be overestimated. Systematic Review Registration: PROSPERO, identifier CRD42019138718.
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Affiliation(s)
- Ziyi Li
- Centre for Clinical Epidemiology and Methodology, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Liangzhi Wu
- Department of Gynecology, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Junguo Zhang
- Centre for Clinical Epidemiology and Methodology, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Xin Huang
- Centre for Clinical Epidemiology and Methodology, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Lehana Thabane
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.,St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Guowei Li
- Centre for Clinical Epidemiology and Methodology, Guangdong Second Provincial General Hospital, Guangzhou, China.,Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
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Chang JF, Huang CS, Chang RF. Automated whole breast segmentation for hand-held ultrasound with position information: Application to breast density estimation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105727. [PMID: 32916544 DOI: 10.1016/j.cmpb.2020.105727] [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: 04/17/2020] [Accepted: 08/23/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Women with higher breast densities have a relatively higher risk to be diagnosed with breast cancer. Hand-held ultrasound (HHUS) can provide precise screening results and detect masses in dense breasts. However, its lack of position information and automatic extraction of breast area hinder the implementation of density estimation. To facilitate reliable breast density evaluation, this study proposed an upgraded version of our whole-breast ultrasound (WBUS) system, which not only can provide precise position information, but also can extract precise breast area automatically based on deep learning method. METHODS WBUS images with probe position information were collected from 117 women. For each case, an automatic breast region segmentation by DeepResUnet was conducted, then fibroglandular tissues were extracted from breast region using fuzzy c-mean (FCM) classifier. Finally, the percentage of breast density and breast area of the DeepResUnet predicted region and the breast region of the ground truth were calculated and compared. RESULTS The average and standard deviation of each breast case for DeepResUnet predicted breast region of 10-fold in Accuracy (ACC) was 0.963±0.054. Sensitivity (SENS) was 0.928±0.11. Specificity (SPEC) was 0.967±0.054. Dice coefficient (Dice) was 0.916±0.98. Region intersection over union (IoU) was 0.856±0.134. Significant and very high correlations of breast density, fibroglandular tissue area and breast area (R = 0.843, R= 0.822 and R = 0.984, all p values < 0.001) were found between the ground truth and the result of the proposed method for ultrasound images. CONCLUSIONS Breast density, fibroglandular tissue, and breast volume evaluated based on the proposed method and WBUS system have significant correlations with ground truth, indicating that the proposed method and WBUS system has the potential to be an alternative modality for breast screening and density estimation in clinical use.
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Affiliation(s)
- Jie-Fan Chang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Chiun-Sheng Huang
- Department of Surgery, National Taiwan University Hospital, Taipei 100, Taiwan.
| | - Ruey-Feng Chang
- Department of Computer Science and Information Engineering, Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, and MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei 10617, Taiwan.
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Volumetric breast density estimation on MRI using explainable deep learning regression. Sci Rep 2020; 10:18095. [PMID: 33093572 PMCID: PMC7581772 DOI: 10.1038/s41598-020-75167-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 10/12/2020] [Indexed: 01/10/2023] Open
Abstract
To purpose of this paper was to assess the feasibility of volumetric breast density estimations on MRI without segmentations accompanied with an explainability step. A total of 615 patients with breast cancer were included for volumetric breast density estimation. A 3-dimensional regression convolutional neural network (CNN) was used to estimate the volumetric breast density. Patients were split in training (N = 400), validation (N = 50), and hold-out test set (N = 165). Hyperparameters were optimized using Neural Network Intelligence and augmentations consisted of translations and rotations. The estimated densities were evaluated to the ground truth using Spearman’s correlation and Bland–Altman plots. The output of the CNN was visually analyzed using SHapley Additive exPlanations (SHAP). Spearman’s correlation between estimated and ground truth density was ρ = 0.81 (N = 165, P < 0.001) in the hold-out test set. The estimated density had a median bias of 0.70% (95% limits of agreement = − 6.8% to 5.0%) to the ground truth. SHAP showed that in correct density estimations, the algorithm based its decision on fibroglandular and fatty tissue. In incorrect estimations, other structures such as the pectoral muscle or the heart were included. To conclude, it is feasible to automatically estimate volumetric breast density on MRI without segmentations, and to provide accompanying explanations.
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Sak M, Littrup P, Brem R, Duric N. Whole Breast Sound Speed Measurement from US Tomography Correlates Strongly with Volumetric Breast Density from Mammography. JOURNAL OF BREAST IMAGING 2020; 2:443-451. [PMID: 33015618 DOI: 10.1093/jbi/wbaa052] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Indexed: 11/14/2022]
Abstract
Objective To assess the feasibility of using tissue sound speed as a quantitative marker of breast density. Methods This study was carried out under an Institutional Review Board-approved protocol (written consent required). Imaging data were selected retrospectively based on the availability of US tomography (UST) exams, screening mammograms with volumetric breast density data, patient age of 18 to 80 years, and weight less than 300 lbs. Sound speed images from the UST exams were used to measure the volume of dense tissue, the volume averaged sound speed (VASS), and the percent of high sound speed tissue (PHSST). The mammographic breast density and volume of dense tissue were estimated with three-dimensional (3D) software. Differences in volumes were assessed with paired t-tests. Spearman correlation coefficients were calculated to determine the strength of the correlations between the mammographic and UST assessments of breast density. Results A total of 100 UST and 3D mammographic data sets met the selection criteria. The resulting measurements showed that UST measured a more than 2-fold larger volume of dense tissue compared to mammography. The differences were statistically significant (P < 0.001). A strong correlation of rS = 0.85 (95% CI: 0.79-0.90) between 3D mammographic breast density (BD) and the VASS was noted. This correlation is significantly stronger than those reported in previous two-dimensional studies (rS = 0.85 vs rS = 0.71). A similar correlation was found for PHSST and mammographic BD with rS = 0.86 (95% CI: 0.80-0.90). Conclusion The strong correlations between UST parameters and 3D mammographic BD suggest that breast sound speed should be further studied as a potential new marker for inclusion in clinical risk models.
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Affiliation(s)
- Mark Sak
- Delphinus Medical Technologies, Inc, Novi, MI
| | | | - Rachel Brem
- George Washington University, Department of Radiology, Washington, DC
| | - Neb Duric
- Delphinus Medical Technologies, Inc, Novi, MI.,Wayne State University, Barbara Ann Karmanos Cancer Institute, Department of Oncology, Detroit, MI
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Ghieh D, Saade C, Najem E, El Zeghondi R, Rawashdeh MA, Berjawi G. Staying abreast of imaging - Current status of breast cancer detection in high density breast. Radiography (Lond) 2020; 27:229-235. [PMID: 32611494 DOI: 10.1016/j.radi.2020.06.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 05/26/2020] [Accepted: 06/08/2020] [Indexed: 11/18/2022]
Abstract
OBJECTIVES The aim of this paper is to illustrate the current status of imaging in high breast density as we enter a new decade of advancing medicine and technology to diagnose breast lesions. KEY FINDINGS Early detection of breast cancer has become the chief focus of research from governments to individuals. However, with varying breast densities across the globe, the explosion of breast density information related to imaging, phenotypes, diet, computer aided diagnosis and artificial intelligence has witnessed a dramatic shift in new screening recommendations in mammography, physical examination, screening younger women and women with comorbid conditions, screening women at high risk, and new screening technologies. Breast density is well known to be a risk factor in patients with suspected/known breast neoplasia. Extensive research in the field of qualitative and quantitative analysis on different tissue characteristics of the breast has rapidly become the chief focus of breast imaging. A summary of the available guidelines and modalities of breast imaging, as well as new emerging techniques under study that can potentially provide an augmentation or even a replacement of those currently available. CONCLUSION Despite all the advances in technology and all the research directed towards breast cancer, detection of breast cancer in dense breasts remains a dilemma. IMPLICATIONS FOR PRACTICE It is of utmost importance to develop highly sensitive screening modalities for early detection of breast cancer.
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Affiliation(s)
- D Ghieh
- Diagnostic Radiology Department, American University of Beirut Medical Center, P.O.Box: 11-0236, Riad El-Solh, Beirut, 1107 2020, Lebanon.
| | - C Saade
- Department of Medical Imaging Sciences, Faculty of Health Sciences, American University of Beirut, P.O.Box: 11-0236, Riad El-Solh, Beirut, 1107 2020, Lebanon.
| | - E Najem
- Diagnostic Radiology Department, American University of Beirut Medical Center, P.O.Box: 11-0236, Riad El-Solh, Beirut, 1107 2020, Lebanon.
| | - R El Zeghondi
- Department of Medical Imaging Sciences, Faculty of Health Sciences, American University of Beirut, P.O.Box: 11-0236, Riad El-Solh, Beirut, 1107 2020, Lebanon.
| | - M A Rawashdeh
- Department of Allied Medical Sciences, Jordan University of Science and Technology, P.O.Box: 3030, Irbid 22110, Jordan.
| | - G Berjawi
- Diagnostic Radiology Department, American University of Beirut Medical Center, P.O.Box: 11-0236, Riad El-Solh, Beirut, 1107 2020, Lebanon.
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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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Sindi R, Sá Dos Reis C, Bennett C, Stevenson G, Sun Z. Quantitative Measurements of Breast Density Using Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis. J Clin Med 2019; 8:jcm8050745. [PMID: 31137728 PMCID: PMC6571752 DOI: 10.3390/jcm8050745] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Accepted: 05/22/2019] [Indexed: 02/06/2023] Open
Abstract
Breast density, a measure of dense fibroglandular tissue relative to non-dense fatty tissue, is confirmed as an independent risk factor of breast cancer. Although there has been an increasing interest in the quantitative assessment of breast density, no research has investigated the optimal technical approach of breast MRI in this aspect. Therefore, we performed a systematic review and meta-analysis to analyze the current studies on quantitative assessment of breast density using MRI and to determine the most appropriate technical/operational protocol. Databases (PubMed, EMBASE, ScienceDirect, and Web of Science) were searched systematically for eligible studies. Single arm meta-analysis was conducted to determine quantitative values of MRI in breast density assessments. Combined means with their 95% confidence interval (CI) were calculated using a fixed-effect model. In addition, subgroup meta-analyses were performed with stratification by breast density segmentation/measurement method. Furthermore, alternative groupings based on statistical similarities were identified via a cluster analysis employing study means and standard deviations in a Nearest Neighbor/Single Linkage. A total of 38 studies matched the inclusion criteria for this systematic review. Twenty-one of these studies were judged to be eligible for meta-analysis. The results indicated, generally, high levels of heterogeneity between study means within groups and high levels of heterogeneity between study variances within groups. The studies in two main clusters identified by the cluster analysis were also subjected to meta-analyses. The review confirmed high levels of heterogeneity within the breast density studies, considered to be due mainly to the applications of MR breast-imaging protocols and the use of breast density segmentation/measurement methods. Further research should be performed to determine the most appropriate protocol and method for quantifying breast density using MRI.
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Affiliation(s)
- Rooa Sindi
- Discipline of Medical Radiation Sciences, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia 6845, Australia.
| | - Cláudia Sá Dos Reis
- Discipline of Medical Radiation Sciences, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia 6845, Australia.
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland (HES-SO), Av. de Beaumont 21, 1011 Lausanne, Switzerland.
- CISP-Centro de Investigação em Saúde Pública, Escola Nacional de Saúde Pública, Universidade NOVA de Lisboa, 1600-560 Lisboa, Portugal.
| | - Colleen Bennett
- Discipline of Medical Radiation Sciences, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia 6845, Australia.
| | | | - Zhonghua Sun
- Discipline of Medical Radiation Sciences, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia 6845, Australia.
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Wiskin J, Malik B, Natesan R, Lenox M. Quantitative assessment of breast density using transmission ultrasound tomography. Med Phys 2019; 46:2610-2620. [PMID: 30893476 PMCID: PMC6618090 DOI: 10.1002/mp.13503] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 03/07/2019] [Accepted: 03/07/2019] [Indexed: 02/06/2023] Open
Abstract
Purpose Breast density is important in the evaluation of breast cancer risk. At present, breast density is evaluated using two‐dimensional projections from mammography with or without tomosynthesis using either (a) subjective assessment or (b) a computer‐aided approach. The purpose of this work is twofold: (a) to establish an algorithm for quantitative assessment of breast density using quantitative three‐dimensional transmission ultrasound imaging; and (b) to determine how these quantitative assessments compare with both subjective and objective mammographic assessments of breast density. Methods We described and verified a threshold‐based segmentation algorithm to give a quantitative breast density (QBD) on ultrasound tomography images of phantoms of known geometric forms. We also used the algorithm and transmission ultrasound tomography to quantitatively determine breast density by separating fibroglandular tissue from fat and skin, based on imaged, quantitative tissue characteristics, and compared the quantitative tomography segmentation results with subjective and objective mammographic assessments. Results Quantitative breast density (QBD) measured in phantoms demonstrates high quantitative accuracy with respect to geometric volumes with average difference of less than 0.1% of the total phantom volumes. There is a strong correlation between QBD and both subjective mammographic assessments of Breast Imaging ‐ Reporting and Data System (BI‐RADS) breast composition categories and Volpara density scores — the Spearman correlation coefficients for the two comparisons were calculated to be 0.90 (95% CI: 0.71–0.96) and 0.96 (95% CI: 0.92–0.98), respectively. Conclusions The calculation of breast density using ultrasound tomography and the described segmentation algorithm is quantitatively accurate in phantoms and highly correlated with both subjective and Food and Drug Administration (FDA)‐cleared objective assessments of breast density.
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