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Foongkajornkiat S, Sokolowski K, Stephenson J, Lloyd T, Hugo HJ, Thompson EW, Momot KI. Quantitative measurement of mammographic density in breast-tissue explants using portable NMR: Precision and accuracy. Magn Reson Med 2024; 92:374-388. [PMID: 38380719 DOI: 10.1002/mrm.30040] [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: 07/26/2023] [Revised: 12/20/2023] [Accepted: 01/18/2024] [Indexed: 02/22/2024]
Abstract
PURPOSE Single-sided portable NMR (pNMR) has previously been demonstrated to be suitable for quantification of mammographic density (MD) in excised breast tissue samples. Here we investigate the precision and accuracy of pNMR measurements of MD ex vivo as compared with the gold standards. METHODS Forty-five breast-tissue explants from 9 prophylactic mastectomy patients were measured. The relative tissue water content was taken as the MD-equivalent quantity. In each sample, the water content was measured using some combination of three pNMR techniques (apparent T2, diffusion, and T1 measurements) and two gold-standard techniques (computed microtomography [μCT] and hematoxylin and eosin [H&E] histology). Pairwise correlation plots and Bland-Altman analysis were used to quantify the degree of agreement between pNMR techniques and the gold standards. RESULTS Relative water content measured from both apparent T2 relaxation spectra, and diffusion decays exhibited strong correlation with the H&E and μCT results. Bland-Altman analysis yielded average bias values of -0.4, -2.6, 2.6, and 2.8 water percentage points (pp) and 95% confidence intervals of 13.1, 7.5, 11.2, and 11.8 pp for the H&E - T2, μCT - T2, H&E - diffusion, and μCT - diffusion comparison pairs, respectively. T1-based measurements were found to be less reliable, with the Bland-Altman confidence intervals of 27.7 and 33.0 pp when compared with H&E and μCT, respectively. CONCLUSION Apparent T2-based and diffusion-based pNMR measurements enable quantification of MD in breast-tissue explants with the precision of approximately 10 pp and accuracy of approximately 3 pp or better, making pNMR a promising measurement modality for radiation-free quantification of MD.
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Affiliation(s)
- Satcha Foongkajornkiat
- School of Chemistry and Physics, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kamil Sokolowski
- Preclincal Imaging Facility, Translational Research Institute, Woolloongabba, Queensland, Australia
| | - James Stephenson
- Department of Breast and Endocrine Surgery, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia
- Translational Research Institute, Woolloongabba, Queensland, Australia
| | - Thomas Lloyd
- Department of Diagnostic Radiology, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia
| | - Honor J Hugo
- School of Health and Behavioural Science, University of the Sunshine Coast, Sippy Downs, Queensland, Australia
- School of Medicine and Dentistry, Griffith University Sunshine Coast, Birtinya, Queensland, Australia
| | - Erik W Thompson
- Translational Research Institute, Woolloongabba, Queensland, Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Konstantin I Momot
- School of Chemistry and Physics, Queensland University of Technology, Brisbane, Queensland, Australia
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Jiménez T, Domínguez-Castillo A, Fernández de Larrea-Baz N, Lucas P, Sierra MÁ, Salas-Trejo D, Llobet R, Martínez I, Pino MN, Martínez-Cortés M, Pérez-Gómez B, Pollán M, Lope V, García-Pérez J. Residential exposure to traffic pollution and mammographic density in premenopausal women. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 928:172463. [PMID: 38615764 DOI: 10.1016/j.scitotenv.2024.172463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/11/2024] [Accepted: 04/11/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND Mammographic density (MD) is the most important breast cancer biomarker. Ambient pollution is a carcinogen, and its relationship with MD is unclear. This study aims to explore the association between exposure to traffic pollution and MD in premenopausal women. METHODOLOGY This Spanish cross-sectional study involved 769 women attending gynecological examinations in Madrid. Annual Average Daily Traffic (AADT), extracted from 1944 measurement road points provided by the City Council of Madrid, was weighted by distances (d) between road points and women's addresses to develop a Weighted Traffic Exposure Index (WTEI). Three methods were employed: method-1 (1dAADT), method-2 (1dAADT), and method-3 (e1dAADT). Multiple linear regression models, considering both log-transformed percentage of MD and untransformed MD, were used to estimate MD differences by WTEI quartiles, through two strategies: "exposed (exposure buffers between 50 and 200 m) vs. not exposed (>200 m)"; and "degree of traffic exposure". RESULTS Results showed no association between MD and traffic pollution according to buffers of exposure to the WTEI (first strategy) for the three methods. The highest reductions in MD, although not statistically significant, were detected in the quartile with the highest traffic exposure. For instance, method-3 revealed a suggestive inverse trend (eβQ1 = 1.23, eβQ2 = 0.96, eβQ3 = 0.85, eβQ4 = 0.85, p-trend = 0.099) in the case of 75 m buffer. Similar non-statistically significant trends were observed with Methods-1 and -2. When we examined the effect of traffic exposure considering all the 1944 measurement road points in every participant (second strategy), results showed no association for any of the three methods. A slightly decreased MD, although not significant, was observed only in the quartile with the highest traffic exposure: eβQ4 = 0.98 (method-1), and eβQ4 = 0.95 (methods-2 and -3). CONCLUSIONS Our results showed no association between exposure to traffic pollution and MD in premenopausal women. Further research is needed to validate these findings.
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Affiliation(s)
- Tamara Jiménez
- Department of Preventive Medicine, Public Health and Microbiology, Universidad Autónoma de Madrid (UAM), Madrid, Spain; HM CINAC (Centro Integral de Neurociencias AC), Hospital Universitario Puerta del Sur, Fundación HM Hospitales, Móstoles, Spain
| | - Alejandro Domínguez-Castillo
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain.
| | - Nerea Fernández de Larrea-Baz
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Pilar Lucas
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain.
| | - María Ángeles Sierra
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Dolores Salas-Trejo
- Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain; Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain; Center for Public Health Research CSISP, FISABIO, Valencia, Spain.
| | - Rafael Llobet
- Institute of Computer Technology, Universitat Politècnica de València, Valencia, Spain.
| | - Inmaculada Martínez
- Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain; Center for Public Health Research CSISP, FISABIO, Valencia, Spain.
| | - Marina Nieves Pino
- Servicio de Prevención y Promoción de la Salud, Madrid Salud, Ayuntamiento de Madrid, Madrid, Spain.
| | - Mercedes Martínez-Cortés
- Servicio de Prevención y Promoción de la Salud, Madrid Salud, Ayuntamiento de Madrid, Madrid, Spain.
| | - Beatriz Pérez-Gómez
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Marina Pollán
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Virginia Lope
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Javier García-Pérez
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
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Pires T, Rohini A. 3D tomosynthesis evaluation of breast parenchymal density and its association with malignant lesions and menopausal status. J Med Imaging Radiat Sci 2024; 55:197-202. [PMID: 38402135 DOI: 10.1016/j.jmir.2024.01.011] [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: 07/28/2023] [Revised: 01/18/2024] [Accepted: 01/25/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND Breast cancer that has a high mortality rate is now known to decrease due to early detection with the advent of digital breast tomosynthesis (DBT or 3D tomosynthesis) screening, especially in those with dense breasts. The risk of breast cancer related to 'changes' in breast density over time remains controversial as breast density and age have an inverse relationship. Breast density as an independent risk factor for breast cancer is known, but its association with menopausal status, if any, has not been studied thoroughly. METHOD All patients referred for 3D mammography with breast lesions from June 2022 to January 2023 were considered. Patients were categorized as pre-, peri, and post-menopausal, and each category was further sub-classified based on the breast density as either dense or non-dense and the lesion type, whether benign or malignant. The Statistical analysis was performed using a chi-square test to evaluate whether any association exists between malignancy and menopausal status. RESULT A total of 60 patients, with 20 in each category of menopausal stage, were imaged and evaluated. 35% of women had non-dense breasts, while 65% had dense breast parenchyma. Breast density and lesion type were associated significantly (p-value = 0.05) where, out of the 23 benign lesions, 48% occurred in dense women, and 52% in non-dense women respectively. In our study, both benign (N = 7) and malignant (N = 13) lesions occurred in equal numbers in the pre-and peri‑ menopausal women, whereas the number of benign and malignant lesions in the post-menopausal women were 9 (45%) and 11 (55%), respectively. Even though no statistically significant association was found between menopausal status and malignancy in our study, out of the 37 malignant lesions, a majority (76%) of lesions occurred in those having dense breasts (N = 28). CONCLUSION Earlier, the notion was that older women had a higher risk of breast cancer compared to younger, but this study has shown that malignancy and menopausal status have a p-value of 0.754, which is not statistically significant. However, both malignant and benign lesions were found more in women having high breast density, in keeping with previous literature. Hence, precaution and care should be taken during pre-, peri, and post-menopausal phases, especially in those patients with high breast density. Apart from breast density, many other risk factors for breast cancer exist, therefore breast density alone is not sufficient to govern the need for screening in women.
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Affiliation(s)
- Tancia Pires
- K.S. Hegde Medical Academy, Nitte Deemed to be University, Mangalore, Karnataka, India
| | - Avantsa Rohini
- MNR Medical College and Hospital, Sangareddy, Telangana State, India.
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Jiang J, Yang Y, Wang F, Mao W, Wang Z, Liu Z. Quercetin inhibits breast cancer cell proliferation and survival by targeting Akt/mTOR/PTEN signaling pathway. Chem Biol Drug Des 2024; 103:e14557. [PMID: 38825578 DOI: 10.1111/cbdd.14557] [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: 11/16/2023] [Revised: 03/16/2024] [Accepted: 05/13/2024] [Indexed: 06/04/2024]
Abstract
Recently, natural compounds such as quercetin have gained an increasing amount of attention in treating breast cancer. However, the exact mechanisms responsible for the antiproliferative functions of quercetin are not completely understood. Therefore, we aimed to examine quercetin impacts on breast cancer cell proliferation and survival and the involvement of PI3K/Akt/mTOR pathway. Breast cancer MDA-MB-231 and MCF-7 cells were exposed to quercetin, and cell proliferation was assessed by MTT assay. ELISA was applied to evaluate cell apoptosis. The expression levels of apoptotic mediators such as caspase-3, Bcl-2, Bax and PI3K, Akt, mTOR, and PTEN were assessed via qRT-PCR and western blot. We found that quercetin suppressed dose dependently cell growth capacity in MDA-MB-231 and MCF-7 cells. In addition, quercetin treatment increase apoptosis in both cells lines via modulating the pro- and antiapoptotic markers. Quercetin upregulated PTEN and downregulated PI3K, Akt, and mTOR, hence suppressing this signaling pathway in cells. In conclusion, we showed antiproliferative and pro-apoptotic function of quercetin in breast cancer cell lines, which is mediated by targeting and suppressing PI3K/Akt/mTOR signal transduction.
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Affiliation(s)
- Ji Jiang
- Department of General Surgery, 920 Hospital of Joint Logistic Support Force, Kunming, China
| | - Yan Yang
- Department of General Surgery, 920 Hospital of Joint Logistic Support Force, Kunming, China
| | - Fuhuan Wang
- Department of General Surgery, 920 Hospital of Joint Logistic Support Force, Kunming, China
| | - Wei Mao
- Department of General Surgery, 920 Hospital of Joint Logistic Support Force, Kunming, China
| | - Zhongjun Wang
- Department of General Surgery, 920 Hospital of Joint Logistic Support Force, Kunming, China
| | - Zegang Liu
- Department of General Surgery, 920 Hospital of Joint Logistic Support Force, Kunming, China
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Yu LF, Dai CC, Zhu LX, Xu XJ, Yan HJ, Jiang CX, Bao LY. Detection and diagnosis of automated breast ultrasound in patients with BI-RADS category 4 microcalcifications: a retrospective study. BMC Med Imaging 2024; 24:126. [PMID: 38807064 PMCID: PMC11134699 DOI: 10.1186/s12880-024-01287-4] [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: 09/04/2023] [Accepted: 04/30/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND Automated Breast Ultrasound (AB US) has shown good application value and prospects in breast disease screening and diagnosis. The aim of the study was to explore the ability of AB US to detect and diagnose mammographically Breast Imaging Reporting and Data System (BI-RADS) category 4 microcalcifications. METHODS 575 pathologically confirmed mammographically BI-RADS category 4 microcalcifications from January 2017 to June 2021 were included. All patients also completed AB US examinations. Based on the final pathological results, analyzed and summarized the AB US image features, and compared the evaluation results with mammography, to explore the detection and diagnostic ability of AB US for these suspicious microcalcifications. RESULTS 250 were finally confirmed as malignant and 325 were benign. Mammographic findings including microcalcifications morphology (61/80 with amorphous, coarse heterogeneous and fine pleomorphic, 13/14 with fine-linear or branching), calcification distribution (189/346 with grouped, 40/67 with linear and segmental), associated features (70/96 with asymmetric shadow), higher BI-RADS category with 4B (88/120) and 4 C (73/38) showed higher incidence in malignant lesions, and were the independent factors associated with malignant microcalcifications. 477 (477/575, 83.0%) microcalcifications were detected by AB US, including 223 malignant and 254 benign, with a significantly higher detection rate for malignant lesions (x2 = 12.20, P < 0.001). Logistic regression analysis showed microcalcifications with architectural distortion (odds ratio [OR] = 0.30, P = 0.014), with amorphous, coarse heterogeneous and fine pleomorphic morphology (OR = 3.15, P = 0.037), grouped (OR = 1.90, P = 0.017), liner and segmental distribution (OR = 8.93, P = 0.004) were the independent factors which could affect the detectability of AB US for microcalcifications. In AB US, malignant calcification was more frequent in a mass (104/154) or intraductal (20/32), and with ductal changes (30/41) or architectural distortion (58/68), especially with the both (12/12). BI-RADS category results also showed that AB US had higher sensitivity to malignant calcification than mammography (64.8% vs. 46.8%). CONCLUSIONS AB US has good detectability for mammographically BI-RADS category 4 microcalcifications, especially for malignant lesions. Malignant calcification is more common in a mass and intraductal in AB US, and tend to associated with architectural distortion or duct changes. Also, AB US has higher sensitivity than mammography to malignant microcalcification, which is expected to become an effective supplementary examination method for breast microcalcifications, especially in dense breasts.
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Affiliation(s)
- Li-Fang Yu
- Department of Ultrasound, Hangzhou First People's Hospital, No.261 Huansha Road, Hangzhou, 310006, Zhejiang Province, China
| | - Chao-Chao Dai
- Department of Ultrasound, Hangzhou First People's Hospital, No.261 Huansha Road, Hangzhou, 310006, Zhejiang Province, China
| | - Luo-Xi Zhu
- Department of Ultrasound, Hangzhou First People's Hospital, No.261 Huansha Road, Hangzhou, 310006, Zhejiang Province, China
| | - Xiao-Jing Xu
- Department of Ultrasound, Hangzhou First People's Hospital, No.261 Huansha Road, Hangzhou, 310006, Zhejiang Province, China
| | - Hong-Ju Yan
- Department of Ultrasound, Hangzhou First People's Hospital, No.261 Huansha Road, Hangzhou, 310006, Zhejiang Province, China
| | - Chen-Xiang Jiang
- Department of Ultrasound, Hangzhou First People's Hospital, No.261 Huansha Road, Hangzhou, 310006, Zhejiang Province, China
| | - Ling-Yun Bao
- Department of Ultrasound, Hangzhou First People's Hospital, No.261 Huansha Road, Hangzhou, 310006, Zhejiang Province, China.
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Grażyńska A, Niewiadomska A, Owczarek AJ, Winder M, Hołda J, Zwolińska O, Barczyk-Gutkowska A, Modlińska S, Lorek A, Kuźbińska A, Steinhof-Radwańska K. Comparison of the effectiveness of contrast-enhanced mammography in detecting malignant lesions in patients with extremely dense breasts compared to the all-densities population. Pol J Radiol 2024; 89:e240-e248. [PMID: 38938658 PMCID: PMC11210381 DOI: 10.5114/pjr/186180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 03/17/2024] [Indexed: 06/29/2024] Open
Abstract
Purpose To assess the effectiveness of contrast-enhanced mammography (CEM) recombinant images in detecting malignant lesions in patients with extremely dense breasts compared to the all-densities population. Material and methods 792 patients with 808 breast lesions, in whom the final decision on core-needle biopsy was made based on CEM, and who received the result of histopathological examination, were qualified for a single-centre, retrospective study. Patient electronic records and imaging examinations were reviewed to establish demographics, clinical and imaging findings, and histopathology results. The CEM images were reassessed and assigned to the appropriate American College of Radiology (ACR) density categories. Results Extremely dense breasts were present in 86 (10.9%) patients. Histopathological examination confirmed the presence of malignant lesions in 52.6% of cases in the entire group of patients and 43% in the group of extremely dense breasts. CEM incorrectly classified the lesion as false negative in 16/425 (3.8%) cases for the whole group, and in 1/37 (2.7%) cases for extremely dense breasts. The sensitivity of CEM for the group of all patients was 96.2%, specificity - 60%, positive predictive values (PPV) - 72.8%, and negative predictive values (NPV) - 93.5%. In the group of patients with extremely dense breasts, the sensitivity of the method was 97.3%, specificity - 59.2%, PPV - 64.3%, and NPV - 96.7%. Conclusions CEM is characterised by high sensitivity and NPV in detecting malignant lesions regardless of the type of breast density. In patients with extremely dense breasts, CEM could serve as a complementary or additional examination in the absence or low availability of MRI.
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Affiliation(s)
- Anna Grażyńska
- Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland
| | - Agnieszka Niewiadomska
- Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland
| | - Aleksander J. Owczarek
- Health Promotion and Obesity Management Unit, Department of Pathophysiology, Medical University of Silesia, Katowice, Poland
| | - Mateusz Winder
- Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland
| | - Jakub Hołda
- Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland
- Department of Anatomy, Jagiellonian University Medical College, Cracow, Poland
| | - Olga Zwolińska
- Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland
| | - Anna Barczyk-Gutkowska
- Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland
| | - Sandra Modlińska
- Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland
| | - Andrzej Lorek
- Department of Oncological Surgery, Prof. Kornel Gibiński Independent Public Central Clinical Hospital, Katowice, Poland
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Vabistsevits M, Davey Smith G, Richardson TG, Richmond RC, Sieh W, Rothstein JH, Habel LA, Alexeeff SE, Lloyd-Lewis B, Sanderson E. Mammographic density mediates the protective effect of early-life body size on breast cancer risk. Nat Commun 2024; 15:4021. [PMID: 38740751 DOI: 10.1038/s41467-024-48105-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 04/17/2024] [Indexed: 05/16/2024] Open
Abstract
The unexplained protective effect of childhood adiposity on breast cancer risk may be mediated via mammographic density (MD). Here, we investigate a complex relationship between adiposity in childhood and adulthood, puberty onset, MD phenotypes (dense area (DA), non-dense area (NDA), percent density (PD)), and their effects on breast cancer. We use Mendelian randomization (MR) and multivariable MR to estimate the total and direct effects of adiposity and age at menarche on MD phenotypes. Childhood adiposity has a decreasing effect on DA, while adulthood adiposity increases NDA. Later menarche increases DA/PD, but when accounting for childhood adiposity, this effect is attenuated. Next, we examine the effect of MD on breast cancer risk. DA/PD have a risk-increasing effect on breast cancer across all subtypes. The MD SNPs estimates are heterogeneous, and additional analyses suggest that different mechanisms may be linking MD and breast cancer. Finally, we evaluate the role of MD in the protective effect of childhood adiposity on breast cancer. Mediation MR analysis shows that 56% (95% CIs [32%-79%]) of this effect is mediated via DA. Our finding suggests that higher childhood adiposity decreases mammographic DA, subsequently reducing breast cancer risk. Understanding this mechanism is important for identifying potential intervention targets.
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Affiliation(s)
- Marina Vabistsevits
- University of Bristol, MRC Integrative Epidemiology Unit, Bristol, UK.
- University of Bristol, Population Health Sciences, Bristol, UK.
| | - George Davey Smith
- University of Bristol, MRC Integrative Epidemiology Unit, Bristol, UK
- University of Bristol, Population Health Sciences, Bristol, UK
| | - Tom G Richardson
- University of Bristol, MRC Integrative Epidemiology Unit, Bristol, UK
- University of Bristol, Population Health Sciences, Bristol, UK
| | - Rebecca C Richmond
- University of Bristol, MRC Integrative Epidemiology Unit, Bristol, UK
- University of Bristol, Population Health Sciences, Bristol, UK
| | - Weiva Sieh
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences, Department of Population Health Science and Policy, New York, NY, USA
- University of Texas MD Anderson Cancer Center, Department of Epidemiology, Houston, TX, USA
| | - Joseph H Rothstein
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences, Department of Population Health Science and Policy, New York, NY, USA
- University of Texas MD Anderson Cancer Center, Department of Epidemiology, Houston, TX, USA
| | - Laurel A Habel
- Kaiser Permanente Northern California, Division of Research, Oakland, CA, USA
| | - Stacey E Alexeeff
- Kaiser Permanente Northern California, Division of Research, Oakland, CA, USA
| | - Bethan Lloyd-Lewis
- University of Bristol, School of Cellular and Molecular Medicine, Bristol, UK
| | - Eleanor Sanderson
- University of Bristol, MRC Integrative Epidemiology Unit, Bristol, UK
- University of Bristol, Population Health Sciences, Bristol, UK
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Kopp A, Dong S, Kwon H, Wang T, Desai AA, Linderman JJ, Tessier P, Thurber GM. In vivo Auto-tuning of Antibody-Drug Conjugate Delivery for Effective Immunotherapy using High-Avidity, Low-Affinity Antibodies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.06.588433. [PMID: 38645231 PMCID: PMC11030390 DOI: 10.1101/2024.04.06.588433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Antibody-drug conjugates (ADCs) have experienced a surge in clinical approvals in the past five years. Despite this success, a major limitation to ADC efficacy in solid tumors is poor tumor penetration, which leaves many cancer cells untargeted. Increasing antibody doses or co-administering ADC with an unconjugated antibody can improve tumor penetration and increase efficacy when target receptor expression is high. However, it can also reduce efficacy in low-expression tumors where ADC delivery is limited by cellular uptake. This creates an intrinsic problem because many patients express different levels of target between tumors and even within the same tumor. Here, we generated High-Avidity, Low-Affinity (HALA) antibodies that can automatically tune the cellular ADC delivery to match the local expression level. Using HER2 ADCs as a model, HALA antibodies were identified with the desired HER2 expression-dependent competitive binding with ADCs in vitro. Multi-scale distribution of trastuzumab emtansine and trastuzumab deruxtecan co-administered with the HALA antibody were analyzed in vivo, revealing that the HALA antibody increased ADC tumor penetration in high-expression systems with minimal reduction in ADC uptake in low-expression tumors. This translated to greater ADC efficacy in immunodeficient mouse models across a range of HER2 expression levels. Furthermore, Fc-enhanced HALA antibodies showed improved Fc-effector function at both high and low expression levels and elicited a strong response in an immunocompetent mouse model. These results demonstrate that HALA antibodies can expand treatment ranges beyond high expression targets and leverage strong immune responses.
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Affiliation(s)
- Anna Kopp
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109
| | - Shujun Dong
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109
| | - Hyeyoung Kwon
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109
| | - Tiexin Wang
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109
| | - Alec A Desai
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109
| | - Jennifer J Linderman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109
| | - Peter Tessier
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109
| | - Greg M Thurber
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109
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Mohammadi S, Ghaderi S, Mohammadi M, Ghaznavi H, Mohammadian K. Breast percent density changes in digital mammography pre- and post-radiotherapy. J Med Radiat Sci 2024. [PMID: 38571377 DOI: 10.1002/jmrs.788] [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: 10/07/2023] [Accepted: 03/20/2024] [Indexed: 04/05/2024] Open
Abstract
INTRODUCTION Breast cancer (BC), the most frequently diagnosed malignancy among women worldwide, presents a public health challenge and affects mortality rates. Breast-conserving therapy (BCT) is a common treatment, but the risk from residual disease necessitates radiotherapy. Digital mammography monitors treatment response by identifying post-operative and radiotherapy tissue alterations, but accurate assessment of mammographic density remains a challenge. This study used OpenBreast to measure percent density (PD), offering insights into changes in mammographic density before and after BCT with radiation therapy. METHODS This retrospective analysis included 92 female patients with BC who underwent BCT, chemotherapy, and radiotherapy, excluding those who received hormonal therapy or bilateral BCT. Percent/percentage density measurements were extracted using OpenBreast, an automated software that applies computational techniques to density analyses. Data were analysed at baseline, 3 months, and 15 months post-treatment using standardised mean difference (SMD) with Cohen's d, chi-square, and paired sample t-tests. The predictive power of PD changes for BC was measured based on the receiver operating characteristic (ROC) curve analysis. RESULTS The mean age was 53.2 years. There were no significant differences in PD between the periods. Standardised mean difference analysis revealed no significant changes in the SMD for PD before treatment compared with 3- and 15-months post-treatment. Although PD increased numerically after radiotherapy, ROC analysis revealed optimal sensitivity at 15 months post-treatment for detecting changes in breast density. CONCLUSIONS This study utilised an automated breast density segmentation tool to assess the changes in mammographic density before and after BC treatment. No significant differences in the density were observed during the short-term follow-up period. However, the results suggest that quantitative density assessment could be valuable for long-term monitoring of treatment effects. The study underscores the necessity for larger and longitudinal studies to accurately measure and validate the effectiveness of quantitative methods in clinical BC management.
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Affiliation(s)
- Sana Mohammadi
- Department of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Sadegh Ghaderi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahdi Mohammadi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid Ghaznavi
- Department of Radiology, Faculty of Paramedical Sciences, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Kamal Mohammadian
- Department of Radiation Oncology, Hamadan University of Medical Sciences, Mahdieh Center, Hamadan, Iran
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10
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Wang H, H M van der Velden B, Verburg E, Bakker MF, Pijnappel RM, Veldhuis WB, van Gils CH, Gilhuijs KGA. Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial. Eur J Radiol 2024; 175:111442. [PMID: 38583349 DOI: 10.1016/j.ejrad.2024.111442] [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: 10/24/2023] [Revised: 02/06/2024] [Accepted: 03/21/2024] [Indexed: 04/09/2024]
Abstract
OBJECTIVES Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI (DCE-MRI) as rated by radiologists is subject to inter- and intrareader variability. We aim to automate BPE category from DCE-MRI. METHODS This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. 4553 women with extremely dense breasts who received supplemental breast MRI screening in eight hospitals were included. Minimal, mild, moderate and marked BPE rated by radiologists were used as reference. Fifteen quantitative MRI features of the fibroglandular tissue were extracted to predict BPE using Random Forest, Naïve Bayes, and KNN classifiers. Majority voting was used to combine the predictions. Internal-external validation was used for training and validation. The inverse-variance weighted mean accuracy was used to express mean performance across the eight hospitals. Cox regression was used to verify non inferiority of the association between automated rating and breast cancer occurrence compared to the association for manual rating. RESULTS The accuracy of majority voting ranged between 0.56 and 0.84 across the eight hospitals. The weighted mean prediction accuracy for the four BPE categories was 0.76. The hazard ratio (HR) of BPE for breast cancer occurrence was comparable between automated rating and manual rating (HR = 2.12 versus HR = 1.97, P = 0.65 for mild/moderate/marked BPE relative to minimal BPE). CONCLUSION It is feasible to rate BPE automatically in DCE-MRI of women with extremely dense breasts without compromising the underlying association between BPE and breast cancer occurrence. The accuracy for minimal BPE is superior to that for other BPE categories.
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Affiliation(s)
- Hui Wang
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Erik Verburg
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Marije F Bakker
- Julius Center for Health Sciences and Primary Care, Utrecht, The Netherlands
| | - Ruud M Pijnappel
- Department of Radiology, University Medical Center Utrecht, The Netherlands
| | - Wouter B Veldhuis
- Department of Radiology, University Medical Center Utrecht, The Netherlands
| | - Carla H van Gils
- Julius Center for Health Sciences and Primary Care, Utrecht, The Netherlands
| | - Kenneth G A Gilhuijs
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
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11
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Lew CO, Harouni M, Kirksey ER, Kang EJ, Dong H, Gu H, Grimm LJ, Walsh R, Lowell DA, Mazurowski MA. A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI. Sci Rep 2024; 14:5383. [PMID: 38443410 PMCID: PMC10915139 DOI: 10.1038/s41598-024-54048-2] [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: 09/23/2022] [Accepted: 02/08/2024] [Indexed: 03/07/2024] Open
Abstract
Breast density, or the amount of fibroglandular tissue (FGT) relative to the overall breast volume, increases the risk of developing breast cancer. Although previous studies have utilized deep learning to assess breast density, the limited public availability of data and quantitative tools hinders the development of better assessment tools. Our objective was to (1) create and share a large dataset of pixel-wise annotations according to well-defined criteria, and (2) develop, evaluate, and share an automated segmentation method for breast, FGT, and blood vessels using convolutional neural networks. We used the Duke Breast Cancer MRI dataset to randomly select 100 MRI studies and manually annotated the breast, FGT, and blood vessels for each study. Model performance was evaluated using the Dice similarity coefficient (DSC). The model achieved DSC values of 0.92 for breast, 0.86 for FGT, and 0.65 for blood vessels on the test set. The correlation between our model's predicted breast density and the manually generated masks was 0.95. The correlation between the predicted breast density and qualitative radiologist assessment was 0.75. Our automated models can accurately segment breast, FGT, and blood vessels using pre-contrast breast MRI data. The data and the models were made publicly available.
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Affiliation(s)
- Christopher O Lew
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA.
| | - Majid Harouni
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Ella R Kirksey
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Elianne J Kang
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Haoyu Dong
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Hanxue Gu
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Lars J Grimm
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Ruth Walsh
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Dorothy A Lowell
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Maciej A Mazurowski
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
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12
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Freitas V, Li X, Scaranelo A, Au F, Kulkarni S, Ghai S, Taeb S, Bubon O, Baldassi B, Komarov B, Parker S, Macsemchuk CA, Waterston M, Olsen KO, Reznik A. Breast Cancer Detection Using a Low-Dose Positron Emission Digital Mammography System. Radiol Imaging Cancer 2024; 6:e230020. [PMID: 38334470 PMCID: PMC10988332 DOI: 10.1148/rycan.230020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 02/10/2024]
Abstract
Purpose To investigate the feasibility of low-dose positron emission mammography (PEM) concurrently to MRI to identify breast cancer and determine its local extent. Materials and Methods In this research ethics board-approved prospective study, participants newly diagnosed with breast cancer with concurrent breast MRI acquisitions were assigned independently of breast density, tumor size, and histopathologic cancer subtype to undergo low-dose PEM with up to 185 MBq of fluorine 18-labeled fluorodeoxyglucose (18F-FDG). Two breast radiologists, unaware of the cancer location, reviewed PEM images taken 1 and 4 hours following 18F-FDG injection. Findings were correlated with histopathologic results. Detection accuracy and participant details were examined using logistic regression and summary statistics, and a comparative analysis assessed the efficacy of PEM and MRI additional lesions detection (ClinicalTrials.gov: NCT03520218). Results Twenty-five female participants (median age, 52 years; range, 32-85 years) comprised the cohort. Twenty-four of 25 (96%) cancers (19 invasive cancers and five in situ diseases) were identified with PEM from 100 sets of bilateral images, showcasing comparable performance even after 3 hours of radiotracer uptake. The median invasive cancer size was 31 mm (range, 10-120). Three additional in situ grade 2 lesions were missed at PEM. While not significant, PEM detected fewer false-positive additional lesions compared with MRI (one of six [16%] vs eight of 13 [62%]; P = .14). Conclusion This study suggests the feasibility of a low-dose PEM system in helping to detect invasive breast cancer. Though large-scale clinical trials are essential to confirm these preliminary results, this study underscores the potential of this low-dose PEM system as a promising imaging tool in breast cancer diagnosis. ClinicalTrials.gov registration no. NCT03520218 Keywords: Positron Emission Digital Mammography, Invasive Breast Cancer, Oncology, MRI Supplemental material is available for this article. © RSNA, 2024 See also commentary by Barreto and Rapelyea in this issue.
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Affiliation(s)
- Vivianne Freitas
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Xuan Li
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Anabel Scaranelo
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Frederick Au
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Supriya Kulkarni
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Sandeep Ghai
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Samira Taeb
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Oleksandr Bubon
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Brandon Baldassi
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Borys Komarov
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Shayna Parker
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Craig A. Macsemchuk
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Michael Waterston
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Kenneth O. Olsen
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Alla Reznik
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
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13
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Bae SJ, Kim HJ, Kim HA, Ryu JM, Park S, Lee EG, Im SA, Jung Y, Park MH, Park KH, Kang SH, Park E, Kim SY, Lee MH, Kim LS, Lee A, Noh WC, Gwark S, Kim S, Jeong J. Breast density reduction as a predictor for prognosis in premenopausal women with estrogen receptor-positive breast cancer: an exploratory analysis of the updated ASTRRA study. Int J Surg 2024; 110:934-942. [PMID: 38000057 PMCID: PMC10871609 DOI: 10.1097/js9.0000000000000907] [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: 07/05/2023] [Accepted: 11/02/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND While the relationship between mammographic breast density reduction (MDR) and endocrine therapy efficacy has been reported in estrogen receptor (ER)-positive breast cancer, it is still unclear in premenopausal women, especially in the case of adding ovarian function suppression (OFS) to antihormone therapy. The authors investigated the impact of MDR on prognosis stratified by treatment based on the updated results of the ASTRRA trial. MATERIALS AND METHODS The ASTRRA trial, a randomized phase III study, showed that adding OFS to tamoxifen (TAM) improved survival in premenopausal women with estrogen receptor-positive breast cancer after chemotherapy. The authors updated survival outcomes and assessed mammography before treatment and the annual follow-up mammography for up to 5 years after treatment initiation. Mammographic density (MD) was classified into four categories based on the Breast Imaging-Reporting and Data System. MDR-positivity was defined as a downgrade in MD grade on follow-up mammography up to 2 years after randomization, with pretreatment MD grade as a reference. RESULTS The authors evaluated MDR in 944 of the 1282 patients from the trial, and 813 (86.2%) had grade III or IV MD. There was no difference in the MDR-positivity rate between the two treatment groups [TAM-only group (106/476 (22.3%)) vs. TAM+OFS group (89/468 (19.0%)); P =0.217). MDR-positivity was significantly associated with better disease-free survival (DFS) in the TAM+OFS group (estimated 8-year DFS: 93.1% in MDR-positive vs. 82.0% in MDR-negative patients; HR: 0.37; 95% CI: 0.16-0.85; P =0.019), but not in the TAM-only group ( Pinteraction =0.039). MDR-positive patients who received TAM+OFS had a favorable DFS compared to MDR-negative patients who received only TAM (HR: 0.30; 95% CI: 0.13-0.70; P =0.005). CONCLUSION Although the proportion of MDR-positive patients was comparable between both treatment groups, MDR-positivity was independently associated with favorable outcomes only in the TAM+OFS group.
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Affiliation(s)
- Soong June Bae
- Department of Surgery, Gangnam Severance Hospital
- Institute for Breast Cancer Precision Medicine, Yonsei University College of Medicine
| | - Hee Jeong Kim
- Division of Breast, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine
| | - Hyun-Ah Kim
- Department of Surgery, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences
| | - Jai Min Ryu
- Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine
| | - Seho Park
- Division of Breast Surgery, Department of Surgery, Yonsei Cancer Center, Yonsei University College of Medicine
| | - Eun-Gyeong Lee
- Center for Breast Cancer, Research Institute and Hospital, National Cancer Center, Goyang, South Korea
| | - Seock-Ah Im
- Seoul National University Hospital, Cancer Research Institute, Seoul National University, College of Medicine
| | - Yongsik Jung
- Department of Surgery, Ajou University, School of Medicine, Suwon
| | - Min Ho Park
- Department of Surgery, Chonnam National University Medical School and Chonnam National University Hwasun Hospital, Gwangju
| | - Kyong Hwa Park
- Korea University Anam Hospital, Department of internal medicine, Division of Medical Oncology/Hematology
| | | | - Eunhwa Park
- Department of Surgery, Dong-A University Hospital, Dong-A University College of Medicine, Busan
| | - Sung Yong Kim
- Department of Surgery, Soonchunhyang University Cheonan Hospital, Cheonan
| | - Min Hyuk Lee
- Department of Surgery, Soonchunhyang University Hospital, Seoul
| | - Lee Su Kim
- Department of Surgery, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong
| | - Anbok Lee
- Department of Surgery, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong
| | - Woo Chul Noh
- Department of Surgery, Konkuk Universitiy Medical Center
| | - Sungchan Gwark
- Department of Surgery, Ewha Womans University College of Medicine, Ewha Womans University Mokdong Hospital
| | - Seonok Kim
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Center
| | - Joon Jeong
- Department of Surgery, Gangnam Severance Hospital
- Institute for Breast Cancer Precision Medicine, Yonsei University College of Medicine
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14
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Hirsch L, Huang Y, Makse HA, Martinez DF, Hughes M, Eskreis-Winkler S, Pinker K, Morris E, Parra LC, Sutton EJ. [WITHDRAWN] Predicting breast cancer with AI for individual risk-adjusted MRI screening and early detection. ARXIV 2024:arXiv:2312.00067v2. [PMID: 38076513 PMCID: PMC10705586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
This paper has been withdrawn by Lukas Hirsch. Major revisions and rewriting in progress.
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15
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Lee SE, Yoon JH, Son NH, Han K, Moon HJ. Screening in Patients With Dense Breasts: Comparison of Mammography, Artificial Intelligence, and Supplementary Ultrasound. AJR Am J Roentgenol 2024; 222:e2329655. [PMID: 37493324 DOI: 10.2214/ajr.23.29655] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
BACKGROUND. Screening mammography has decreased performance in patients with dense breasts. Supplementary screening ultrasound is a recommended option in such patients, although it has yielded mixed results in prior investigations. OBJECTIVE. The purpose of this article is to compare the performance characteristics of screening mammography alone, standalone artificial intelligence (AI), ultrasound alone, and mammography in combination with AI and/or ultrasound in patients with dense breasts. METHODS. This retrospective study included 1325 women (mean age, 53 years) with dense breasts who underwent both screening mammography and supplementary breast ultrasound within a 1-month interval from January 2017 to December 2017; prior mammography and prior ultrasound examinations were available for comparison in 91.2% and 91.8%, respectively. Mammography and ultrasound examinations were interpreted by one of 15 radiologists (five staff; 10 fellows); clinical reports were used for the present analysis. A commercial AI tool was used to retrospectively evaluate mammographic examinations for presence of cancer. Screening performances were compared among mammography, AI, ultrasound, and test combinations, using generalized estimating equations. Benign diagnoses required 24 months or longer of imaging stability. RESULTS. Twelve cancers (six invasive ductal carcinoma; six ductal carcinoma in situ) were diagnosed. Mammography, standalone AI, and ultrasound showed cancer detection rates (per 1000 patients) of 6.0, 6.8, and 6.0 (all p > .05); recall rates of 4.4%, 11.9%, and 9.2% (all p < .05); sensitivity of 66.7%, 75.0%, and 66.7% (all p > .05); specificity of 96.2%, 88.7%, and 91.3% (all p < .05); and accuracy of 95.9%, 88.5%, and 91.1% (all p < .05). Mammography with AI, mammography with ultrasound, and mammography with both ultrasound and AI showed cancer detection rates of 7.5, 9.1, and 9.1 (all p > .05); recall rates of 14.9, 11.7, and 21.4 (all p < .05); sensitivity of 83.3%, 100.0%, and 100.0% (all p > .05); specificity of 85.8%, 89.1%, and 79.4% (all p < .05); and accuracy of 85.7%, 89.2%, and 79.5% (all p < .05). CONCLUSION. Mammography with supplementary ultrasound showed higher accuracy, higher specificity, and lower recall rate in comparison with mammography with AI and in comparison with mammography with both ultrasound and AI. CLINICAL IMPACT. The findings fail to show benefit of AI with respect to screening mammography performed with supplementary breast ultrasound in patients with dense breasts.
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Affiliation(s)
- Si Eun Lee
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
| | - Jung Hyun Yoon
- Department of Radiology, Research Institute of Radiologic Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Nak-Hoon Son
- Department of Statistics, Keimyung University, Daegu, South Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiologic Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hee Jung Moon
- Department of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju 220-701, Korea
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16
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Landén AH, Chin K, Kovács A, Holmberg E, Molnar E, Stenmark Tullberg A, Wärnberg F, Karlsson P. Evaluation of tumor-infiltrating lymphocytes and mammographic density as predictors of response to neoadjuvant systemic therapy in breast cancer. Acta Oncol 2023; 62:1862-1872. [PMID: 37934084 DOI: 10.1080/0284186x.2023.2274483] [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: 06/16/2023] [Accepted: 10/19/2023] [Indexed: 11/08/2023]
Abstract
BACKGROUND Response rates vary among breast cancer patients treated with neoadjuvant systemic therapy (NAST). Thus, there is a need for reliable treatment predictors. Evidence suggests tumor-infiltrating lymphocytes (TILs) predict NAST response. Still, TILs are seldom used clinically as a treatment determinant. Mammographic density (MD) is another potential marker for NAST benefit and its relationship with TILs is unknown. Our aims were to investigate TILs and MD as predictors of NAST response and to study the unexplored relationship between TILs and MD. MATERIAL AND METHODS We studied 315 invasive breast carcinomas treated with NAST between 2013 and 2020. Clinicopathological data were retrieved from medical records. The endpoint was defined as pathological complete response (pCR) in the breast. TILs were evaluated in pre-treatment core biopsies and categorized as high (≥10%) or low (<10%). MD was scored (a-d) according to the breast imaging reporting and data system (BI-RADS) fifth edition. Binary logistic regression and Spearman's test of correlation were performed using SPSS. RESULTS Out of 315 carcinomas, 136 achieved pCR. 94 carcinomas had high TILs and 215 had low TILs. Six carcinomas had no available TIL data. The number of carcinomas in each BI-RADS category were 37, 122, 112, and 44 for a, b, c, and d, respectively. High TILs were independently associated with pCR (OR: 2.95; 95% CI: 1.59-5.46) compared to low TILs. In the univariable analysis, MD (BI-RADS d vs. a) showed a tendency of higher likelihood for pCR (OR: 2.43; 95% CI: 0.99-5.98). However, the association was non-significant, which is consistent with the result of the multivariable analysis (OR: 2.51; 95% CI: 0.78-8.04). We found no correlation between TILs and MD (0.02; p = .80). CONCLUSION TILs significantly predicted NAST response. We could not define MD as a significant predictor of NAST response. These findings should be further replicated.
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Affiliation(s)
- Amalia H Landén
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Kian Chin
- Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anikó Kovács
- Department of Clinical Pathology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Erik Holmberg
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Eva Molnar
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Axel Stenmark Tullberg
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Fredrik Wärnberg
- Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Per Karlsson
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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17
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Heine J, Fowler EEE, Weinfurtner RJ, Hume E, Tworoger SS. Breast density analysis of digital breast tomosynthesis. Sci Rep 2023; 13:18760. [PMID: 37907569 PMCID: PMC10618274 DOI: 10.1038/s41598-023-45402-x] [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: 08/24/2023] [Accepted: 10/19/2023] [Indexed: 11/02/2023] Open
Abstract
Mammography shifted to digital breast tomosynthesis (DBT) in the US. An automated percentage of breast density (PD) technique designed for two-dimensional (2D) applications was evaluated with DBT using several breast cancer risk prediction measures: normalized-volumetric; dense volume; applied to the volume slices and averaged (slice-mean); and applied to synthetic 2D images. Volumetric measures were derived theoretically. PD was modeled as a function of compressed breast thickness (CBT). The mean and standard deviation of the pixel values were investigated. A matched case-control (CC) study (n = 426 pairs) was evaluated. Odd ratios (ORs) were estimated with 95% confidence intervals. ORs were significant for PD: identical for volumetric and slice-mean measures [OR = 1.43 (1.18, 1.72)] and [OR = 1.44 (1.18, 1.75)] for synthetic images. A 2nd degree polynomial (concave-down) was used to model PD as a function of CBT: location of the maximum PD value was similar across CCs, occurring at 0.41 × CBT, and PD was significant [OR = 1.47 (1.21, 1.78)]. The means from the volume and synthetic images were also significant [ORs ~ 1.31 (1.09, 1.57)]. An alternative standardized 2D synthetic image was constructed, where each pixel value represents the percentage of breast density above its location. Several measures were significant and an alternative method for constructing a standardized 2D synthetic image was produced.
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Affiliation(s)
- John Heine
- Cancer Epidemiology Department, Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA.
| | - Erin E E Fowler
- Cancer Epidemiology Department, Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA
| | - R Jared Weinfurtner
- Diagnostic Imaging and Interventional Radiology, Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA
| | - Emma Hume
- Cancer Epidemiology Department, Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA
| | - Shelley S Tworoger
- Cancer Epidemiology Department, Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA
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Mahdi AF, Nolan J, O’Connor RÍ, Lowery AJ, Allardyce JM, Kiely PA, McGourty K. Collagen-I influences the post-translational regulation, binding partners and role of Annexin A2 in breast cancer progression. Front Oncol 2023; 13:1270436. [PMID: 37941562 PMCID: PMC10628465 DOI: 10.3389/fonc.2023.1270436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 10/11/2023] [Indexed: 11/10/2023] Open
Abstract
Introduction The extracellular matrix (ECM) has been heavily implicated in the development and progression of cancer. We have previously shown that Annexin A2 is integral in the migration and invasion of breast cancer cells and in the clinical progression of ER-negative breast cancer, processes which are highly influenced by the surrounding tumor microenvironment and ECM. Methods We investigated how modulations of the ECM may affect the role of Annexin A2 in MDA-MB-231 breast cancer cells using western blotting, immunofluorescent confocal microscopy and immuno-precipitation mass spectrometry techniques. Results We have shown that the presence of collagen-I, the main constituent of the ECM, increases the post-translational phosphorylation of Annexin A2 and subsequently causes the translocation of Annexin A2 to the extracellular surface. In the presence of collagen-I, we identified fibronectin as a novel interactor of Annexin A2, using mass spectrometry analysis. We then demonstrated that reducing Annexin A2 expression decreases the degradation of fibronectin by cancer cells and this effect on fibronectin turnover is increased according to collagen-I abundance. Discussion Our results suggest that Annexin A2's role in promoting cancer progression is mediated by collagen-I and Annexin A2 maybe a therapeutic target in the bi-directional cross-talk between cancer cells and ECM remodeling that supports metastatic cancer progression.
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Affiliation(s)
- Amira F. Mahdi
- School of Medicine, University of Limerick, Limerick, Ireland
- Health Research Institute, University of Limerick, Limerick, Ireland
| | - Joanne Nolan
- School of Medicine, University of Limerick, Limerick, Ireland
- Health Research Institute, University of Limerick, Limerick, Ireland
| | - Ruth Í. O’Connor
- School of Medicine, University of Limerick, Limerick, Ireland
- Health Research Institute, University of Limerick, Limerick, Ireland
| | - Aoife J. Lowery
- Lambe Institute for Translational Research, University of Galway, Galway, Ireland
| | - Joanna M. Allardyce
- Health Research Institute, University of Limerick, Limerick, Ireland
- School of Allied Health, University of Limerick, Limerick, Ireland
| | - Patrick A. Kiely
- School of Medicine, University of Limerick, Limerick, Ireland
- Health Research Institute, University of Limerick, Limerick, Ireland
| | - Kieran McGourty
- Health Research Institute, University of Limerick, Limerick, Ireland
- Science Foundation Ireland Research Centre in Pharmaceuticals (SSPC), University of Limerick, Limerick, Ireland
- Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick, Ireland
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19
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Park HL, Ziogas A, Feig SA, Kirmizi RL, Lee CJ, Alvarez A, Lucia RM, Goodman D, Larsen KM, Kelly R, Anton-Culver H. Factors Associated with Longitudinal Changes in Mammographic Density in a Multiethnic Breast Screening Cohort of Postmenopausal Women. Breast J 2023; 2023:2794603. [PMID: 37881237 PMCID: PMC10597735 DOI: 10.1155/2023/2794603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 07/19/2023] [Accepted: 10/04/2023] [Indexed: 10/27/2023]
Abstract
Background Breast density is an important risk factor for breast cancer and is known to be associated with characteristics such as age, race, and hormone levels; however, it is unclear what factors contribute to changes in breast density in postmenopausal women over time. Understanding factors associated with density changes may enable a better understanding of breast cancer risk and facilitate potential strategies for prevention. Methods This study investigated potential associations between personal factors and changes in mammographic density in a cohort of 3,392 postmenopausal women with no personal history of breast cancer between 2011 and 2017. Self-reported information on demographics, breast and reproductive history, and lifestyle factors, including body mass index (BMI), alcohol intake, smoking, and physical activity, was collected by an electronic intake form, and breast imaging reporting and database system (BI-RADS) mammographic density scores were obtained from electronic medical records. Factors associated with a longitudinal increase or decrease in mammographic density were identified using Fisher's exact test and multivariate conditional logistic regression. Results 7.9% of women exhibited a longitudinal decrease in mammographic density, 6.7% exhibited an increase, and 85.4% exhibited no change. Longitudinal changes in mammographic density were correlated with age, race/ethnicity, and age at menopause in the univariate analysis. In the multivariate analysis, Asian women were more likely to exhibit a longitudinal increase in mammographic density and less likely to exhibit a decrease compared to White women. On the other hand, obese women were less likely to exhibit an increase and more likely to exhibit a decrease compared to normal weight women. Women who underwent menopause at age 55 years or older were less likely to exhibit a decrease in mammographic density compared to women who underwent menopause at a younger age. Besides obesity, lifestyle factors (alcohol intake, smoking, and physical activity) were not associated with longitudinal changes in mammographic density. Conclusions The associations we observed between Asian race/obesity and longitudinal changes in BI-RADS density in postmenopausal women are paradoxical in that breast cancer risk is lower in Asian women and higher in obese women. However, the association between later age at menopause and a decreased likelihood of decreasing in BI-RADS density over time is consistent with later age at menopause being a risk factor for breast cancer and suggests a potential relationship between greater cumulative lifetime estrogen exposure and relative stability in breast density after menopause. Our findings support the complexity of the relationships between breast density, BMI, hormone exposure, and breast cancer risk.
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Affiliation(s)
- Hannah Lui Park
- Department of Pathology and Laboratory Medicine, University of California, Irvine, CA, USA
- Department of Epidemiology and Biostatistics, University of California, Irvine, CA, USA
| | - Argyrios Ziogas
- Department of Medicine, University of California, Irvine, CA, USA
| | - Stephen A. Feig
- Department of Radiological Sciences, University of California, Irvine, CA, USA
| | - Roza Lorin Kirmizi
- Department of Biological Sciences, University of California, Irvine, CA, USA
| | - Christie Jiwon Lee
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, USA
| | - Andrea Alvarez
- Department of Medicine, University of California, Irvine, CA, USA
| | | | - Deborah Goodman
- Department of Epidemiology and Biostatistics, University of California, Irvine, CA, USA
| | - Kathryn M. Larsen
- Department of Family Medicine, University of California, Irvine, CA, USA
| | - Richard Kelly
- Department of Clinical Informatics, University of California, Irvine, CA, USA
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20
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Ho PJ, Lim EH, Hartman M, Wong FY, Li J. Breast cancer risk stratification using genetic and non-genetic risk assessment tools for 246,142 women in the UK Biobank. Genet Med 2023; 25:100917. [PMID: 37334786 DOI: 10.1016/j.gim.2023.100917] [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: 01/31/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 06/20/2023] Open
Abstract
PURPOSE The benefit of using individual risk prediction tools to identify high-risk individuals for breast cancer (BC) screening is uncertain, despite the personalized approach of risk-based screening. METHODS We studied the overlap of predicted high-risk individuals among 246,142 women enrolled in the UK Biobank. Risk predictors assessed include the Gail model (Gail), BC family history (FH, binary), BC polygenic risk score (PRS), and presence of loss-of-function (LoF) variants in BC predisposition genes. Youden J-index was used to select optimal thresholds for defining high-risk. RESULTS In total, 147,399 were considered at high risk for developing BC within the next 2 years by at least 1 of the 4 risk prediction tools examined (Gail2-year > 0.5%: 47%, PRS2-yea r > 0.7%: 30%, FH: 6%, and LoF: 1%); 92,851 (38%) were flagged by only 1 risk predictor. The overlap between individuals flagged as high-risk because of genetic (PRS) and Gail model risk factors was 30%. The best-performing combinatorial model comprises a union of high-risk women identified by PRS, FH, and, LoF (AUC2-year [95% CI]: 62.2 [60.8 to 63.6]). Assigning individual weights to each risk prediction tool increased discriminatory ability. CONCLUSION Risk-based BC screening may require a multipronged approach that includes PRS, predisposition genes, FH, and other recognized risk factors.
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Affiliation(s)
- Peh Joo Ho
- Laboratory of Women's Health and Genetics, Genome Institute of Singapore, A∗STAR Research Entities, Singapore; Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Elaine H Lim
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Mikael Hartman
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
| | - Fuh Yong Wong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Jingmei Li
- Laboratory of Women's Health and Genetics, Genome Institute of Singapore, A∗STAR Research Entities, Singapore; Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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Letson J, Furuta S. Reduced S-nitrosylation of TGFβ1 elevates its binding affinity towards the receptor and promotes fibrogenic signaling in the breast. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.07.556714. [PMID: 37745487 PMCID: PMC10515751 DOI: 10.1101/2023.09.07.556714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Transforming Growth Factor β (TGFβ) is a pleiotropic cytokine closely linked to tumors. TGFβ is often elevated in precancerous breast lesions in association with epithelial-to-mesenchymal transition (EMT), indicating its contribution to precancerous progression. We previously reported that basal nitric oxide (NO) levels declined along with breast cancer progression. We then pharmacologically inhibited NO production in healthy mammary glands of wild-type mice and found that this induced precancerous progression accompanied by desmoplasia and upregulation of TGFβ activity. In the present study, we tested our hypothesis that NO directly S-nitrosylates (forms an NO-adduct at a cysteine residue) TGFβ to inhibit the activity, whereas the reduction of NO denitrosylates TGFβ and de-represses the activity. We introduced mutations to three C-terminal cysteines of TGFβ1 which were predicted to be S-nitrosylated. We found that these mutations indeed impaired S-nitrosylation of TGFβ1 and shifted the binding affinity towards the receptor from the latent complex. Furthermore, in silico structural analyses predicted that these S-nitrosylation-defective mutations strengthen the dimerization of mature protein, whereas S-nitrosylation-mimetic mutations weaken the dimerization. Such differences in dimerization dynamics of TGFβ1 by denitrosylation/S-nitrosylation likely account for the shift of the binding affinities towards the receptor vs. latent complex. Our findings, for the first time, unravel a novel mode of TGFβ regulation based on S-nitrosylation or denitrosylation of the protein.
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Affiliation(s)
- Joshua Letson
- Department of Cell & Cancer Biology, College of Medicine and Life Sciences, University of Toledo Health Science Campus, 3000 Arlington Ave. Toledo, OH 43614, USA
- Department of Orthopaedic Surgery, College of Medicine and Life Sciences, University of Toledo Health Science Campus, 3000 Arlington Ave. Toledo, OH 43614, USA
| | - Saori Furuta
- Department of Cell & Cancer Biology, College of Medicine and Life Sciences, University of Toledo Health Science Campus, 3000 Arlington Ave. Toledo, OH 43614, USA
- MetroHealth Medical Center, Case Western Reserve University School of Medicine, Case Comprehensive Cancer Center, 2500 MetroHealth Drive, Cleveland, OH 44109
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Vabistsevits M, Smith GD, Richardson TG, Richmond RC, Sieh W, Rothstein JH, Habel LA, Alexeeff SE, Lloyd-Lewis B, Sanderson E. The mediating role of mammographic density in the protective effect of early-life adiposity on breast cancer risk: a multivariable Mendelian randomization study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.01.23294765. [PMID: 37693539 PMCID: PMC10491349 DOI: 10.1101/2023.09.01.23294765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Observational studies suggest that mammographic density (MD) may have a role in the unexplained protective effect of childhood adiposity on breast cancer risk. Here, we investigated a complex and interlinked relationship between puberty onset, adiposity, MD, and their effects on breast cancer using Mendelian randomization (MR). We estimated the effects of childhood and adulthood adiposity, and age at menarche on MD phenotypes (dense area (DA), non-dense area (NDA), percent density (PD)) using MR and multivariable MR (MVMR), allowing us to disentangle their total and direct effects. Next, we examined the effect of MD on breast cancer risk, including risk of molecular subtypes, and accounting for genetic pleiotropy. Finally, we used MVMR to evaluate whether the protective effect of childhood adiposity on breast cancer was mediated by MD. Childhood adiposity had a strong inverse effect on mammographic DA, while adulthood adiposity increased NDA. Later menarche had an effect of increasing DA and PD, but when accounting for childhood adiposity, this effect attenuated to the null. DA and PD had a risk-increasing effect on breast cancer across all subtypes. The MD single-nucleotide polymorphism (SNP) estimates were extremely heterogeneous, and examination of the SNPs suggested different mechanisms may be linking MD and breast cancer. Finally, MR mediation analysis estimated that 56% (95% CIs [32% - 79%]) of the childhood adiposity effect on breast cancer risk was mediated via DA. In this work, we sought to disentangle the relationship between factors affecting MD and breast cancer. We showed that higher childhood adiposity decreases mammographic DA, which subsequently leads to reduced breast cancer risk. Understanding this mechanism is of great importance for identifying potential targets of intervention, since advocating weight gain in childhood would not be recommended.
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Affiliation(s)
- Marina Vabistsevits
- University of Bristol, MRC Integrative Epidemiology Unit, Bristol, United Kingdom
- University of Bristol, Population Health Sciences, Bristol, United Kingdom
| | - George Davey Smith
- University of Bristol, MRC Integrative Epidemiology Unit, Bristol, United Kingdom
- University of Bristol, Population Health Sciences, Bristol, United Kingdom
| | - Tom G. Richardson
- University of Bristol, MRC Integrative Epidemiology Unit, Bristol, United Kingdom
- University of Bristol, Population Health Sciences, Bristol, United Kingdom
| | - Rebecca C. Richmond
- University of Bristol, MRC Integrative Epidemiology Unit, Bristol, United Kingdom
- University of Bristol, Population Health Sciences, Bristol, United Kingdom
| | - Weiva Sieh
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences, Department of Population Health Science and Policy, New York, NY, United States
- University of Texas MD Anderson Cancer Center, Department of Epidemiology, Houston, TX, United States
| | - Joseph H. Rothstein
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences, Department of Population Health Science and Policy, New York, NY, United States
- University of Texas MD Anderson Cancer Center, Department of Epidemiology, Houston, TX, United States
| | - Laurel A. Habel
- Kaiser Permanente Northern California, Division of Research, Oakland, CA, United States
| | - Stacey E. Alexeeff
- Kaiser Permanente Northern California, Division of Research, Oakland, CA, United States
| | - Bethan Lloyd-Lewis
- University of Bristol, School of Cellular and Molecular Medicine, Bristol, United Kingdom
| | - Eleanor Sanderson
- University of Bristol, MRC Integrative Epidemiology Unit, Bristol, United Kingdom
- University of Bristol, Population Health Sciences, Bristol, United Kingdom
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23
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Boutas I, Kontogeorgi A, Koufopoulos NI, Pouliakis A, Dimitrakakis C, Dimas DT, Sitara K, Kalantaridou S, Durmusoglu F. The Correlation Between Progesterone and Mammographic Density in Postmenopausal Women: A Systematic Review of the Literature and Meta-Analysis. Cureus 2023; 15:e45597. [PMID: 37868563 PMCID: PMC10588543 DOI: 10.7759/cureus.45597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/19/2023] [Indexed: 10/24/2023] Open
Abstract
Higher mammographic breast density in premenopausal and postmenopausal women is related to a higher breast cancer risk. In this review, we analyze the correlation between estrogen, progesterone, and mammographic density in postmenopausal women and clarify whether these findings are consistent across different types of mammographic breast density. We extracted data concerning mammographic density increases in the populations treated with estrogen-only hormone replacement therapy and those treated with estrogen and progestin hormone replacement therapy. Postmenopausal women treated with estrogen and progesterone regimens had a statistically significant lesser mammographic density increase than estrogen-only hormone replacement therapy regimens.
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Affiliation(s)
| | - Adamantia Kontogeorgi
- Third Department of Obstetrics and Gynecology, National and Kapodistrian University of Athens, Athens, GRC
| | | | - Abraham Pouliakis
- Second Department of Pathology, National and Kapodistrian University of Athens, Athens, GRC
| | - Constantine Dimitrakakis
- Breast Unit, First Department of Obstetrics and Gynecology, National and Kapodistrian University of Athens, Athens, GRC
| | | | - Kyparissia Sitara
- Department of Internal Medicine, "Elpis" General Hospital of Athens, Athens, GRC
| | - Sophia Kalantaridou
- Third Department of Obstetrics and Gynecology, National and Kapodistrian University of Athens, Athens, GRC
| | - Fatih Durmusoglu
- Department of Obstetrics and Gynecology, Istanbul Medipol International School of Medicine, Istanbul, TUR
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Ambinder EB, Lee E, Nguyen DL, Gong AJ, Haken OJ, Visvanathan K. Interval Breast Cancers Versus Screen Detected Breast Cancers: A Retrospective Cohort Study. Acad Radiol 2023; 30 Suppl 2:S154-S160. [PMID: 36739227 DOI: 10.1016/j.acra.2023.01.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/28/2022] [Accepted: 01/05/2023] [Indexed: 02/05/2023]
Abstract
RATIONALE AND OBJECTIVE Mammographic screening detects most breast cancers but there are still women diagnosed with breast cancer between annual mammograms. We aim to identify features that differentiate screen detected breast cancers from interval breast cancer. MATERIALS AND METHODS All screening mammograms (n = 211,517) performed 7/1/2013-6/30/2020 at our institution were reviewed. Patients with breast cancer diagnosed within one year of screening were included and divided into two distinct groups: screen detected cancer group and interval cancer group. Characteristics in these groups were compared using the chi square test, fisher test, and student's T test. RESULTS A total of 1,232 patients were included (mean age 64 +/- 11). Sensitivity of screening mammography was 92% (1,136 screen detected cancers, 96 interval cancers). Patient age, race, and personal history of breast cancer were similar between the groups (p > 0.05). Patients with interval cancers more often had dense breast tissue (75/96 = 78% versus 694/1136 = 61%, p < 0.001). Compared to screen detected cancers, interval cancers were more often primary tumor stage two or higher (41/96 = 43% versus 139/1136 = 12%, p < 0.001) and regional lymph node stage one or higher (21/96 = 22% versus 132/1136 = 12%, p = 0.003). Interval cancers were more often triple negative (16/77 = 21% versus [48/813 = 6%], p < 0.001) with high Ki67 proliferation indices (28/45 = 62% versus 188/492 = 38%, p = 0.002). CONCLUSION Mammographic screening had high sensitivity for breast cancer detection (92%). Interval cancers were associated with dense breast tissue and had higher stage with less favorable molecular features compared to screen detected cancers.
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Affiliation(s)
- Emily B Ambinder
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, 601 N. Caroline St., Baltimore, Maryland, 21287; Johns Hopkins Sidney Kimmel Cancer Center, Baltimore MD.
| | - Emerson Lee
- Johns Hopkins School of Medicine, Baltimore MD
| | | | - Anna J Gong
- Johns Hopkins School of Medicine, Baltimore MD
| | - Orli J Haken
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, 601 N. Caroline St., Baltimore, Maryland, 21287
| | - Kala Visvanathan
- Johns Hopkins Sidney Kimmel Cancer Center, Baltimore MD; Departments of Epidemiology and Oncology, Johns Hopkins Bloomberg School of Public Health and Kimmel Cancer Center, Baltimore, MD
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25
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Goodburn R, Kousi E, Sanders C, Macdonald A, Scurr E, Bunce C, Khabra K, Reddy M, Wilkinson L, O'Flynn E, Allen S, Schmidt MA. Quantitative background parenchymal enhancement and fibro-glandular density at breast MRI: Association with BRCA status. Eur Radiol 2023; 33:6204-6212. [PMID: 37017702 PMCID: PMC10415521 DOI: 10.1007/s00330-023-09592-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 02/20/2023] [Accepted: 02/22/2023] [Indexed: 04/06/2023]
Abstract
OBJECTIVES To investigate whether MRI-based measurements of fibro-glandular tissue volume, breast density (MRBD), and background parenchymal enhancement (BPE) could be used to stratify two cohorts of healthy women: BRCA carriers and women at population risk of breast cancer. METHODS Pre-menopausal women aged 40-50 years old were scanned at 3 T, employing a standard breast protocol including a DCE-MRI (35 and 30 participants in high- and low-risk groups, respectively). The dynamic range of the DCE protocol was characterised and both breasts were masked and segmented with minimal user input to produce measurements of fibro-glandular tissue volume, MRBD, and voxelwise BPE. Statistical tests were performed to determine inter- and intra-user repeatability, evaluate the symmetry between metrics derived from left and right breasts, and investigate MRBD and BPE differences between the high- and low-risk cohorts. RESULTS Intra- and inter-user reproducibility in estimates of fibro-glandular tissue volume, MRBD, and median BPE estimations were good, with coefficients of variation < 15%. Coefficients of variation between left and right breasts were also low (< 25%). There were no significant correlations between fibro-glandular tissue volume, MRBD, and BPE for either risk group. However, the high-risk group had higher BPE kurtosis, although linear regression analysis did not reveal significant associations between BPE kurtosis and breast cancer risk. CONCLUSIONS This study found no significant differences or correlations in fibro-glandular tissue volume, MRBD, or BPE metrics between the two groups of women with different levels of breast cancer risk. However, the results support further investigation into the heterogeneity of parenchymal enhancement. KEY POINTS • A semi-automated method enabled quantitative measurements of fibro-glandular tissue volume, breast density, and background parenchymal enhancement with minimal user intervention. • Background parenchymal enhancement was quantified over the entire parenchyma, segmented in pre-contrast images, thus avoiding region selection. • No significant differences and correlations in fibro-glandular tissue volume, breast density, and breast background parenchymal enhancement were found between two cohorts of women at high and low levels of breast cancer risk.
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Affiliation(s)
- Rosie Goodburn
- CRUK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden Foundation Trust, London, UK.
- The Royal Marsden NHS Foundation Trust, Sutton, UK.
| | - Evanthia Kousi
- CRUK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden Foundation Trust, London, UK
- The Royal Marsden NHS Foundation Trust, Sutton, UK
| | | | | | - Erica Scurr
- The Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Catey Bunce
- The Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Komel Khabra
- The Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Mamatha Reddy
- St Georges University Hospitals NHS Foundation Trust, London, UK
| | | | | | - Steven Allen
- The Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Maria Angélica Schmidt
- CRUK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden Foundation Trust, London, UK
- The Royal Marsden NHS Foundation Trust, Sutton, UK
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26
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Peppa M, Manta A, Mavroeidi I, Nastos C, Pikoulis E, Syrigos K, Bamias A. Dietary Approach of Patients with Hormone-Related Cancer Based on the Glycemic Index and Glycemic Load Estimates. Nutrients 2023; 15:3810. [PMID: 37686842 PMCID: PMC10490329 DOI: 10.3390/nu15173810] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 08/21/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023] Open
Abstract
Hormone-related cancers, namely breast, endometrial, cervical, prostate, testicular, and thyroid, constitute a specific group of cancers dependent on hormone levels that play an essential role in cancer growth. In addition to the traditional risk factors, diet seems to be an important environmental factor that partially explains the steadily increased prevalence of this group of cancer. The composition of food, the dietary patterns, the endocrine-disrupting chemicals, and the way of food processing and preparation related to dietary advanced glycation end-product formation are all related to cancer. However, it remains unclear which specific dietary components mediate this relationship. Carbohydrates seem to be a risk factor for cancer in general and hormone-related cancers, in particular, with a difference between simple and complex carbohydrates. Glycemic index and glycemic load estimates reflect the effect of dietary carbohydrates on postprandial glucose concentrations. Several studies have investigated the relationship between the dietary glycemic index and glycemic load estimates with the natural course of cancer and, more specifically, hormone-related cancers. High glycemic index and glycemic load diets are associated with cancer development and worse prognosis, partially explained by the adverse effects on insulin metabolism, causing hyperinsulinemia and insulin resistance, and also by inflammation and oxidative stress induction. Herein, we review the existing data on the effect of diets focusing on the glycemic index and glycemic load estimates on hormone-related cancers.
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Affiliation(s)
- Melpomeni Peppa
- Endocrine Unit, 2nd Propaedeutic Department of Internal Medicine, Research Institute and Diabetes Center, Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, 12641 Athens, Greece; (A.M.); (I.M.)
| | - Aspasia Manta
- Endocrine Unit, 2nd Propaedeutic Department of Internal Medicine, Research Institute and Diabetes Center, Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, 12641 Athens, Greece; (A.M.); (I.M.)
| | - Ioanna Mavroeidi
- Endocrine Unit, 2nd Propaedeutic Department of Internal Medicine, Research Institute and Diabetes Center, Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, 12641 Athens, Greece; (A.M.); (I.M.)
| | - Constantinos Nastos
- 3rd Department of Surgery, Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, 12641 Athens, Greece; (C.N.); (E.P.)
| | - Emmanouil Pikoulis
- 3rd Department of Surgery, Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, 12641 Athens, Greece; (C.N.); (E.P.)
| | - Konstantinos Syrigos
- 3rd Department of Internal Medicine, Sotiria Hospital, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece;
| | - Aristotelis Bamias
- 2nd Propaedeutic Department of Internal Medicine, Research Institute and Diabetes Center, Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, 12641 Athens, Greece;
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27
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Lippey J, Keogh L, Campbell I, Mann GB, Forrest LE. Impact of a risk based breast screening decision aid on understanding, acceptance and decision making. NPJ Breast Cancer 2023; 9:65. [PMID: 37553371 PMCID: PMC10409718 DOI: 10.1038/s41523-023-00569-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 07/21/2023] [Indexed: 08/10/2023] Open
Abstract
Internationally, population breast cancer screening is moving towards a risk-stratified approach and requires engagement and acceptance from current and future screening clients. A decision aid ( www.defineau.org ) was developed based on women's views, values, and knowledge regarding risk-stratified breast cancer screening. This study aims to evaluate the impact of the decision aid on women's knowledge, risk perception, acceptance of risk assessment and change of screening frequency, and decision-making. Here we report the results of a pre and post-survey in which women who are clients of BreastScreen Victoria were invited to complete an online questionnaire before and after viewing the decision aid. 3200 potential participants were invited, 242 responded with 127 participants completing both surveys. After reviewing the decision aid there was a significant change in knowledge, acceptance of risk-stratified breast cancer screening and of decreased frequency screening for lower risk. High levels of acceptance of risk stratification, genetic testing and broad support for tailored screening persisted pre and post review. The DEFINE decision aid has a positive impact on acceptance of lower frequency screening, a major barrier to the success of a risk-stratified program and may contribute to facilitating change to the population breast screening program in Australia.
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Affiliation(s)
- Jocelyn Lippey
- Sir Peter MacCallum Department of Oncology, Melbourne, Australia
- University of Melbourne, Department of Surgery, Melbourne, Australia
- St. Vincent's Hospital, Department of Surgery, Fitzroy, Australia
| | - Louise Keogh
- University of Melbourne, Melbourne School of Population and Global Health, Melbourne, Australia
| | - Ian Campbell
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Gregory Bruce Mann
- Department of Surgery, The University of Melbourne, Melbourne, Australia
- Breast Service, The Royal Melbourne Hospital, Melbourne, Australia
| | - Laura Elenor Forrest
- Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre, Melbourne, Australia.
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28
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Wang H, van der Velden BHM, Verburg E, Bakker MF, Pijnappel RM, Veldhuis WB, van Gils CH, Gilhuijs KGA. Assessing Quantitative Parenchymal Features at Baseline Dynamic Contrast-enhanced MRI and Cancer Occurrence in Women with Extremely Dense Breasts. Radiology 2023; 308:e222841. [PMID: 37552061 DOI: 10.1148/radiol.222841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Background Automated identification of quantitative breast parenchymal enhancement features on dynamic contrast-enhanced (DCE) MRI scans could provide added value in assessment of breast cancer risk in women with extremely dense breasts. Purpose To automatically identify quantitative properties of the breast parenchyma on baseline DCE MRI scans and assess their association with breast cancer occurrence in women with extremely dense breasts. Materials and Methods This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. MRI was performed in eight hospitals between December 2011 and January 2016. After segmentation of fibroglandular tissue, quantitative features (including volumetric density, volumetric morphology, and enhancement characteristics) of the parenchyma were extracted from baseline MRI scans. Principal component analysis was used to identify parenchymal measures with the greatest variance. Multivariable Cox proportional hazards regression was applied to assess the association between breast cancer occurrence and quantitative parenchymal features, followed by stratification of significant features into tertiles. Results A total of 4553 women (mean age, 55.7 years ± 6 [SD]) with extremely dense breasts were included; of these women, 122 (3%) were diagnosed with breast cancer. Five principal components representing 96% of the variance were identified, and the component explaining the greatest independent variance (42%) consisted of MRI features relating to volume of enhancing parenchyma. Multivariable analysis showed that volume of enhancing parenchyma was associated with breast cancer occurrence (hazard ratio [HR], 1.09; 95% CI: 1.01, 1.18; P = .02). Additionally, women in the high tertile of volume of enhancing parenchyma showed a breast cancer occurrence twice that of women in the low tertile (HR, 2.09; 95% CI: 1.25, 3.61; P = .005). Conclusion In women with extremely dense breasts, a high volume of enhancing parenchyma on baseline DCE MRI scans was associated with increased occurrence of breast cancer as compared with a low volume of enhancing parenchyma. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Grimm in this issue.
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Affiliation(s)
- Hui Wang
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Bas H M van der Velden
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Erik Verburg
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Marije F Bakker
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Ruud M Pijnappel
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Wouter B Veldhuis
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Carla H van Gils
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Kenneth G A Gilhuijs
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
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29
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Avard RC, Broad ML, Zandkarimi F, Devanny AJ, Hammer JL, Yu K, Guzman A, Kaufman LJ. DISC-3D: dual-hydrogel system enhances optical imaging and enables correlative mass spectrometry imaging of invading multicellular tumor spheroids. Sci Rep 2023; 13:12383. [PMID: 37524722 PMCID: PMC10390472 DOI: 10.1038/s41598-023-38699-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 07/13/2023] [Indexed: 08/02/2023] Open
Abstract
Multicellular tumor spheroids embedded in collagen I matrices are common in vitro systems for the study of solid tumors that reflect the physiological environment and complexities of the in vivo environment. While collagen I environments are physiologically relevant and permissive of cell invasion, studying spheroids in such hydrogels presents challenges to key analytical assays and to a wide array of imaging modalities. While this is largely due to the thickness of the 3D hydrogels that in other samples can typically be overcome by sectioning, because of their highly porous nature, collagen I hydrogels are very challenging to section, especially in a manner that preserves the hydrogel network including cell invasion patterns. Here, we describe a novel method for preparing and cryosectioning invasive spheroids in a two-component (collagen I and gelatin) matrix, a technique we term dual-hydrogel in vitro spheroid cryosectioning of three-dimensional samples (DISC-3D). DISC-3D does not require cell fixation, preserves the architecture of invasive spheroids and their surroundings, eliminates imaging challenges, and allows for use of techniques that have infrequently been applied in three-dimensional spheroid analysis, including super-resolution microscopy and mass spectrometry imaging.
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Affiliation(s)
- Rachel C Avard
- Department of Chemistry, Columbia University, New York, NY, 10027, USA
| | - Megan L Broad
- Department of Chemistry, Columbia University, New York, NY, 10027, USA
- Department of Chemistry, Cardiff University, Cardiff, CF10 3AT, Wales, UK
| | | | | | - Joseph L Hammer
- Department of Chemistry, Columbia University, New York, NY, 10027, USA
| | - Karen Yu
- Department of Chemistry, Columbia University, New York, NY, 10027, USA
- Department of Physics, Columbia University, New York, NY, 10027, USA
| | - Asja Guzman
- Department of Biological Sciences, Columbia University, New York, NY, 10027, USA
| | - Laura J Kaufman
- Department of Chemistry, Columbia University, New York, NY, 10027, USA.
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30
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Safaei S, Sajed R, Shariftabrizi A, Dorafshan S, Saeednejad Zanjani L, Dehghan Manshadi M, Madjd Z, Ghods R. Tumor matrix stiffness provides fertile soil for cancer stem cells. Cancer Cell Int 2023; 23:143. [PMID: 37468874 DOI: 10.1186/s12935-023-02992-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 07/12/2023] [Indexed: 07/21/2023] Open
Abstract
Matrix stiffness is a mechanical characteristic of the extracellular matrix (ECM) that increases from the tumor core to the tumor periphery in a gradient pattern in a variety of solid tumors and can promote proliferation, invasion, metastasis, drug resistance, and recurrence. Cancer stem cells (CSCs) are a rare subpopulation of tumor cells with self-renewal, asymmetric cell division, and differentiation capabilities. CSCs are thought to be responsible for metastasis, tumor recurrence, chemotherapy resistance, and consequently poor clinical outcomes. Evidence suggests that matrix stiffness can activate receptors and mechanosensor/mechanoregulator proteins such as integrin, FAK, and YAP, modulating the characteristics of tumor cells as well as CSCs through different molecular signaling pathways. A deeper understanding of the effect of matrix stiffness on CSCs characteristics could lead to development of innovative cancer therapies. In this review, we discuss how the stiffness of the ECM is sensed by the cells and how the cells respond to this environmental change as well as the effect of matrix stiffness on CSCs characteristics and also the key malignant processes such as proliferation and EMT. Then, we specifically focus on how increased matrix stiffness affects CSCs in breast, lung, liver, pancreatic, and colorectal cancers. We also discuss how the molecules responsible for increased matrix stiffness and the signaling pathways activated by the enhanced stiffness can be manipulated as a therapeutic strategy for cancer.
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Affiliation(s)
- Sadegh Safaei
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Hemmat Street (Highway), Next to Milad Tower, Tehran, 14496-14530, Iran
- Oncopathology Research Center, Iran University of Medical Sciences (IUMS), Hemmat Street (Highway), Next to Milad Tower, Tehran, 14496-14530, Iran
| | - Roya Sajed
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Hemmat Street (Highway), Next to Milad Tower, Tehran, 14496-14530, Iran
- Oncopathology Research Center, Iran University of Medical Sciences (IUMS), Hemmat Street (Highway), Next to Milad Tower, Tehran, 14496-14530, Iran
| | - Ahmad Shariftabrizi
- Division of Nuclear Medicine, Department of Radiology, University of Iowa Carver College of Medicine, Iowa City, IA, USA
- Division of Nuclear Medicine, Department of Radiology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Shima Dorafshan
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Hemmat Street (Highway), Next to Milad Tower, Tehran, 14496-14530, Iran
- Oncopathology Research Center, Iran University of Medical Sciences (IUMS), Hemmat Street (Highway), Next to Milad Tower, Tehran, 14496-14530, Iran
| | - Leili Saeednejad Zanjani
- Oncopathology Research Center, Iran University of Medical Sciences (IUMS), Hemmat Street (Highway), Next to Milad Tower, Tehran, 14496-14530, Iran
- Department of Pathology and Genomic Medicine, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Masoumeh Dehghan Manshadi
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Hemmat Street (Highway), Next to Milad Tower, Tehran, 14496-14530, Iran
- Oncopathology Research Center, Iran University of Medical Sciences (IUMS), Hemmat Street (Highway), Next to Milad Tower, Tehran, 14496-14530, Iran
| | - Zahra Madjd
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Hemmat Street (Highway), Next to Milad Tower, Tehran, 14496-14530, Iran.
- Oncopathology Research Center, Iran University of Medical Sciences (IUMS), Hemmat Street (Highway), Next to Milad Tower, Tehran, 14496-14530, Iran.
| | - Roya Ghods
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Hemmat Street (Highway), Next to Milad Tower, Tehran, 14496-14530, Iran.
- Oncopathology Research Center, Iran University of Medical Sciences (IUMS), Hemmat Street (Highway), Next to Milad Tower, Tehran, 14496-14530, Iran.
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31
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Kim H, Lim J, Kim HG, Lim Y, Seo BK, Bae MS. Deep Learning Analysis of Mammography for Breast Cancer Risk Prediction in Asian Women. Diagnostics (Basel) 2023; 13:2247. [PMID: 37443642 DOI: 10.3390/diagnostics13132247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 06/24/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
The purpose of this study was to develop a mammography-based deep learning (DL) model for predicting the risk of breast cancer in Asian women. This retrospective study included 287 examinations in 153 women in the cancer group and 736 examinations in 447 women in the negative group, obtained from the databases of two tertiary hospitals between November 2012 and March 2022. All examinations were labeled as either dense breast or nondense breast, and then randomly assigned to either training, validation, or test sets. DL models, referred to as image-level and examination-level models, were developed. Both models were trained to predict whether or not the breast would develop breast cancer with two datasets: the whole dataset and the dense-only dataset. The performance of DL models was evaluated using the accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). On a test set, performance metrics for the four scenarios were obtained: image-level model with whole dataset, image-level model with dense-only dataset, examination-level model with whole dataset, and examination-level model with dense-only dataset with AUCs of 0.71, 0.75, 0.66, and 0.67, respectively. Our DL models using mammograms have the potential to predict breast cancer risk in Asian women.
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Affiliation(s)
- Hayoung Kim
- Department of Radiology, College of Medicine, Inha University Hospital, Inhang-ro 27, Jung-gu, Incheon 22332, Republic of Korea
| | - Jihe Lim
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si 18450, Gyeonggi-do, Republic of Korea
| | - Hyug-Gi Kim
- Department of Radiology, Kyung Hee University Hospital, Seoul 02447, Republic of Korea
| | - Yunji Lim
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si 18450, Gyeonggi-do, Republic of Korea
| | - Bo Kyoung Seo
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan-si 15355, Gyeonggi-do, Republic of Korea
| | - Min Sun Bae
- Department of Radiology, College of Medicine, Inha University Hospital, Inhang-ro 27, Jung-gu, Incheon 22332, Republic of Korea
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32
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Zdanowski A, Sartor H, Feldt M, Skarping I. Mammographic density in relation to breast cancer recurrence and survival in women receiving neoadjuvant chemotherapy. Front Oncol 2023; 13:1177310. [PMID: 37388229 PMCID: PMC10304818 DOI: 10.3389/fonc.2023.1177310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/30/2023] [Indexed: 07/01/2023] Open
Abstract
Objective The association between mammographic density (MD) and breast cancer (BC) recurrence and survival remains unclear. Patients receiving neoadjuvant chemotherapy (NACT) are in a vulnerable situation with the tumor within the breast during treatment. This study evaluated the association between MD and recurrence/survival in BC patients treated with NACT. Methods Patients with BC treated with NACT in Sweden (2005-2016) were retrospectively included (N=302). Associations between MD (Breast Imaging-Reporting and Data System (BI-RADS) 5th Edition) and recurrence-free/BC-specific survival at follow-up (Q1 2022) were addressed. Hazard ratios (HRs) for recurrence/BC-specific survival (BI-RADS a/b/c vs. d) were estimated using Cox regression analysis and adjusted for age, estrogen receptor status, human epidermal growth factor receptor 2 status, axillary lymph node status, tumor size, and complete pathological response. Results A total of 86 recurrences and 64 deaths were recorded. The adjusted models showed that patients with BI-RADS d vs. BI-RADS a/b/c had an increased risk of recurrence (HR 1.96 (95% confidence interval (CI) 0.98-3.92)) and an increased risk of BC-specific death (HR 2.94 (95% CI 1.43-6.06)). Conclusion These findings raise questions regarding personalized follow-up for BC patients with extremely dense breasts (BI-RADS d) pre-NACT. More extensive studies are required to confirm our findings.
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Affiliation(s)
| | - Hanna Sartor
- Department of Translational Medicine, Diagnostic Radiology, Skåne University Hospital, Lund University, Lund/Malmö, Sweden
| | - Maria Feldt
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
- Department of Oncology, Skåne University Hospital, Lund, Sweden
| | - Ida Skarping
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
- Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Lund, Sweden
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Hiatt RA, Worden L, Rehkopf D, Engmann N, Troester M, Witte JS, Balke K, Jackson C, Barlow J, Fenton SE, Gehlert S, Hammond RA, Kaplan G, Kornak J, Nishioka K, McKone T, Smith MT, Trasande L, Porco TC. A complex systems model of breast cancer etiology: The Paradigm II Model. PLoS One 2023; 18:e0282878. [PMID: 37205649 PMCID: PMC10198497 DOI: 10.1371/journal.pone.0282878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 02/24/2023] [Indexed: 05/21/2023] Open
Abstract
BACKGROUND Complex systems models of breast cancer have previously focused on prediction of prognosis and clinical events for individual women. There is a need for understanding breast cancer at the population level for public health decision-making, for identifying gaps in epidemiologic knowledge and for the education of the public as to the complexity of this most common of cancers. METHODS AND FINDINGS We developed an agent-based model of breast cancer for the women of the state of California using data from the U.S. Census, the California Health Interview Survey, the California Cancer Registry, the National Health and Nutrition Examination Survey and the literature. The model was implemented in the Julia programming language and R computing environment. The Paradigm II model development followed a transdisciplinary process with expertise from multiple relevant disciplinary experts from genetics to epidemiology and sociology with the goal of exploring both upstream determinants at the population level and pathophysiologic etiologic factors at the biologic level. The resulting model reproduces in a reasonable manner the overall age-specific incidence curve for the years 2008-2012 and incidence and relative risks due to specific risk factors such as BRCA1, polygenic risk, alcohol consumption, hormone therapy, breastfeeding, oral contraceptive use and scenarios for environmental toxin exposures. CONCLUSIONS The Paradigm II model illustrates the role of multiple etiologic factors in breast cancer from domains of biology, behavior and the environment. The value of the model is in providing a virtual laboratory to evaluate a wide range of potential interventions into the social, environmental and behavioral determinants of breast cancer at the population level.
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Affiliation(s)
- Robert A. Hiatt
- Department of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco, San Francisco, California, United States of America
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, United States of America
| | - Lee Worden
- Francis I. Proctor Foundation for Research in Ophthalmology, University of California San Francisco, San Francisco, California, United States of America
| | - David Rehkopf
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, United States of America
| | - Natalie Engmann
- Genentech, Inc. South San Francisco, San Francisco, California, United States of America
| | - Melissa Troester
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - John S. Witte
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, United States of America
| | - Kaya Balke
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, United States of America
| | - Christian Jackson
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, United States of America
| | - Janice Barlow
- Zero Breast Cancer (retired), San Rafael, California, United States of America
| | - Suzanne E. Fenton
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, North Carolina, United States of America
| | - Sarah Gehlert
- Suzanne Dworak-Peck School, University of Southern California, Los Angeles, United States of America
| | - Ross A. Hammond
- Brown School, Washington University, St Louis, Missouri, United States of America
| | - George Kaplan
- University of Michigan (retired), Ann Arbor, Michigan, United States of America
| | - John Kornak
- Department of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Krisida Nishioka
- School of Law, University of California, Berkeley, Berkeley, California, United States of America
| | - Thomas McKone
- School of Public Health, University of California, Berkeley, (Emeritus), Berkeley, California, United States of America
| | - Martyn T. Smith
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
| | - Leonardo Trasande
- Department of Pediatrics, NYU Grossman School of Medicine, New York City, New York, United States of America
| | - Travis C. Porco
- Department of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco, San Francisco, California, United States of America
- Francis I. Proctor Foundation for Research in Ophthalmology, University of California San Francisco, San Francisco, California, United States of America
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Eriksson M, Czene K, Vachon C, Conant EF, Hall P. Long-Term Performance of an Image-Based Short-Term Risk Model for Breast Cancer. J Clin Oncol 2023; 41:2536-2545. [PMID: 36930854 PMCID: PMC10414699 DOI: 10.1200/jco.22.01564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 02/09/2023] [Indexed: 03/19/2023] Open
Abstract
PURPOSE Image-derived artificial intelligence-based short-term risk models for breast cancer have shown high discriminatory performance compared with traditional lifestyle/familial-based risk models. The long-term performance of image-derived risk models has not been investigated. METHODS We performed a case-cohort study of 8,604 randomly selected women within a mammography screening cohort initiated in 2010 in Sweden for women age 40-74 years. Mammograms, age, lifestyle, and familial risk factors were collected at study entry. In all, 2,028 incident breast cancers were identified through register matching in May 2022 (206 incident breast cancers were found in the subcohort). The image-based model extracted mammographic features (density, microcalcifications, masses, and left-right breast asymmetries of these features) and age from study entry mammograms. The Tyrer-Cuzick v8 risk model incorporates self-reported lifestyle and familial risk factors and mammographic density to estimate risk. Absolute risks were estimated, and age-adjusted AUC model performances (aAUCs) were compared across the 10-year period. RESULTS The aAUCs of the image-based risk model ranged from 0.74 (95% CI, 0.70 to 0.78) to 0.65 (95% CI, 0.63 to 0.66) for breast cancers developed 1-10 years after study entry; the corresponding Tyrer-Cuzick aAUCs were 0.62 (95% CI, 0.56 to 0.67) to 0.60 (95% CI, 0.58 to 0.61). For symptomatic cancers, the aAUCs for the image-based model were ≥0.75 during the first 3 years. Women with high and low mammographic density showed similar aAUCs. Throughout the 10-year follow-up, 20% of all women with breast cancers were deemed high-risk at study entry by the image-based risk model compared with 7.1% using the lifestyle familial-based model (P < .01). CONCLUSION The image-based risk model outperformed the Tyrer-Cuzick v8 model for both short-term and long-term risk assessment and could be used to identify women who may benefit from supplemental screening and risk reduction strategies.
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Affiliation(s)
- Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - Emily F. Conant
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset University Hospital, Stockholm, Sweden
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Brisson BK, Dekky B, Berger AC, Mauldin EA, Loebel C, Yen W, Stewart DC, Gillette D, Assenmacher CA, Cukierman E, Burdick JA, Borges VF, Volk SW. Tumor-restrictive type III collagen in the breast cancer microenvironment: prognostic and therapeutic implications. RESEARCH SQUARE 2023:rs.3.rs-2631314. [PMID: 37090621 PMCID: PMC10120781 DOI: 10.21203/rs.3.rs-2631314/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Collagen plays a critical role in regulating breast cancer progression and therapeutic resistance. An improved understanding of both the features and drivers of tumor-permissive and -restrictive collagen matrices are critical to improve prognostication and develop more effective therapeutic strategies. In this study, using a combination of in vitro, in vivo and in silico experiments, we show that type III collagen (Col3) plays a tumor-restrictive role in human breast cancer. We demonstrate that Col3-deficient, human fibroblasts produce tumor-permissive collagen matrices that drive cell proliferation and suppress apoptosis in noninvasive and invasive breast cancer cell lines. In human TNBC biopsy samples, we demonstrate elevated deposition of Col3 relative to type I collagen (Col1) in noninvasive compared to invasive regions. Similarly, in silico analyses of over 1000 breast cancer patient biopsies from The Cancer Genome Atlas BRCA cohort revealed that patients with higher Col3:Col1 bulk tumor expression had improved overall, disease-free and progression-free survival relative to those with higher Col1:Col3 expression. Using an established 3D culture model, we show that Col3 increases spheroid formation and induces formation of lumen-like structures that resemble non-neoplastic mammary acini. Finally, our in vivo study shows co-injection of murine breast cancer cells (4T1) with rhCol3-supplemented hydrogels limits tumor growth and decreases pulmonary metastatic burden compared to controls. Taken together, these data collectively support a tumor-suppressive role for Col3 in human breast cancer and suggest that strategies that increase Col3 may provide a safe and effective modality to limit recurrence in breast cancer patients.
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Affiliation(s)
- Becky K. Brisson
- Department of Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Bassil Dekky
- Department of Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ashton C. Berger
- Department of Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Elizabeth A. Mauldin
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Claudia Loebel
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Materials Science & Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - William Yen
- Department of Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Daniel C. Stewart
- Department of Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Deborah Gillette
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Charles-Antoine Assenmacher
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Edna Cukierman
- Cancer Signaling and Microenvironment Program, The Martin and Concetta Greenberg Pancreatic Cancer Institute, Fox Chase Cancer Center, Temple University Lewis Katz School of Medicine, Philadelphia, Pennsylvania, USA
| | - Jason A. Burdick
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- BioFrontiers Institute and Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado, USA
| | - Virginia F. Borges
- Department of Medicine, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- University of Colorado Cancer Center, Aurora, Colorado, USA
- Young Women’s Breast Cancer Translational Program, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Susan W. Volk
- Department of Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Tari DU, Santonastaso R, De Lucia DR, Santarsiere M, Pinto F. Breast Density Evaluation According to BI-RADS 5th Edition on Digital Breast Tomosynthesis: AI Automated Assessment Versus Human Visual Assessment. J Pers Med 2023; 13:jpm13040609. [PMID: 37108994 PMCID: PMC10146726 DOI: 10.3390/jpm13040609] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/15/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023] Open
Abstract
Background: The assessment of breast density is one of the main goals of radiologists because the masking effect of dense fibroglandular tissue may affect the mammographic identification of lesions. The BI-RADS 5th Edition has revised the mammographic breast density categories, focusing on a qualitative evaluation rather than a quantitative one. Our purpose is to compare the concordance of the automatic classification of breast density with the visual assessment according to the latest available classification. Methods: A sample of 1075 digital breast tomosynthesis images from women aged between 40 and 86 years (58 ± 7.1) was retrospectively analyzed by three independent readers according to the BI-RADS 5th Edition. Automated breast density assessment was performed on digital breast tomosynthesis images with the Quantra software version 2.2.3. Interobserver agreement was assessed with kappa statistics. The distributions of breast density categories were compared and correlated with age. Results: The agreement on breast density categories was substantial to almost perfect between radiologists (κ = 0.63–0.83), moderate to substantial between radiologists and the Quantra software (κ = 0.44–0.78), and the consensus of radiologists and the Quantra software (κ = 0.60–0.77). Comparing the assessment for dense and non-dense breasts, the agreement was almost perfect in the screening age range without a statistically significant difference between concordant and discordant cases when compared by age. Conclusions: The categorization proposed by the Quantra software has shown a good agreement with the radiological evaluations, even though it did not completely reflect the visual assessment. Thus, clinical decisions regarding supplemental screening should be based on the radiologist’s perceived masking effect rather than the data produced exclusively by the Quantra software.
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Aref MH, El-Gohary M, Elrewainy A, Mahmoud A, Aboughaleb IH, Hussein AA, El-Ghaffar SA, Mahran A, El-Sharkawy YH. Emerging Technology for Intraoperative Margin and Assisting in Post-Surgery tissue diagnostic for Future Breast-Conserving. Photodiagnosis Photodyn Ther 2023; 42:103507. [PMID: 36940788 DOI: 10.1016/j.pdpdt.2023.103507] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 02/13/2023] [Accepted: 03/07/2023] [Indexed: 03/23/2023]
Abstract
INTRODUCTION Tissue-preserving surgery is utilized progressively in cancer therapy, where a clear surgical margin is critical to avoid cancer recurrence, specifically in breast cancer (BC) surgery. The Intraoperative pathologic approaches that rely on tissue segmenting and staining have been recognized as the ground truth for BC diagnosis. Nevertheless, these methods are constrained by its complication and timewasting for tissue preparation. OBJECTIVE We present a non-invasive optical imaging system incorporating a hyperspectral (HS) camera to discriminate between cancerous and non-cancerous tissues in ex-vivo breast specimens, which could be an intraoperative diagnostic technique to aid surgeons during surgery and later a valuable tool to assist pathologists. METHODS We have established a hyperspectral Imaging (HSI) system comprising a push-broom HS camera at wavelength 380∼1050 nm with source light 390∼980 nm. We have measured the investigated samples' diffuse reflectance (Rd), fixed on slides from 30 distinct patients incorporating mutually normal and ductal carcinoma tissue. The samples were divided into two groups, stained tissues during the surgery (control group) and unstained samples (test group), both captured with the HSI system in the visible and near-infrared (VIS-NIR) range. Then, to address the problem of the spectral nonuniformity of the illumination device and the influence of the dark current, the radiance data were normalized to yield the radiance of the specimen and neutralize the intensity effect to focus on the spectral reflectance shift for each tissue. The selection of the threshold window from the measured Rd is carried out by exploiting the statistical analysis by calculating each region's mean and standard deviation. Afterward, we selected the optimum spectral images from the HS data cube to apply a custom K-means algorithm and contour delineation to identify the regular districts from the BC regions. RESULTS We noticed that the measured spectral Rd for the malignant tissues of the investigated case studies versus the reference source light varies regarding the cancer stage, as sometimes the Rd is higher for the tumor or vice versa for the normal tissue. Later, from the analysis of the whole samples, we found that the most appropriate wavelength for the BC tissues was 447 nm, which was highly reflected versus the normal tissue. However, the most convenient one for the normal tissue was at 545 nm with high reflection versus the BC tissue. Finally, we implement a moving average filter for noise reduction and a custom K-means clustering algorithm on the selected two spectral images (447, 551 nm) to identify the various regions and effectively-identified spectral tissue variations with a sensitivity of 98.95%, and specificity of 98.44%. A pathologist later confirmed these outcomes as the ground truth for the tissue sample investigations. CONCLUSIONS The proposed system could help the surgeon and the pathologist identify the cancerous tissue margins from the non-cancerous tissue with a non-invasive, rapid, and minimum time method achieving high sensitivity up to 98.95%.
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Affiliation(s)
| | - Mohamed El-Gohary
- Demonstrator, Communications Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
| | - Ahmed Elrewainy
- Avionics Department, Electrical Engineering Branch, Military Technical College, Cairo, Egypt.
| | - Alaaeldin Mahmoud
- Optoelectronics and advanced control systems Department, Military Technical College, Cairo, Egypt.
| | | | | | | | - Ashraf Mahran
- Avionics Department, Military Technical College, Cairo, Egypt.
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Yun C, Eom B, Park S, Kim C, Kim D, Jabeen F, Kim WH, Kim HJ, Kim J. A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography. SENSORS (BASEL, SWITZERLAND) 2023; 23:2864. [PMID: 36905074 PMCID: PMC10007509 DOI: 10.3390/s23052864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/04/2023] [Accepted: 01/12/2023] [Indexed: 06/18/2023]
Abstract
In the medical field, it is delicate to anticipate good performance in using deep learning due to the lack of large-scale training data and class imbalance. In particular, ultrasound, which is a key breast cancer diagnosis method, is delicate to diagnose accurately as the quality and interpretation of images can vary depending on the operator's experience and proficiency. Therefore, computer-aided diagnosis technology can facilitate diagnosis by visualizing abnormal information such as tumors and masses in ultrasound images. In this study, we implemented deep learning-based anomaly detection methods for breast ultrasound images and validated their effectiveness in detecting abnormal regions. Herein, we specifically compared the sliced-Wasserstein autoencoder with two representative unsupervised learning models autoencoder and variational autoencoder. The anomalous region detection performance is estimated with the normal region labels. Our experimental results showed that the sliced-Wasserstein autoencoder model outperformed the anomaly detection performance of others. However, anomaly detection using the reconstruction-based approach may not be effective because of the occurrence of numerous false-positive values. In the following studies, reducing these false positives becomes an important challenge.
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Affiliation(s)
- Changhee Yun
- National Information Society Agency, Daegu 41068, Republic of Korea
| | - Bomi Eom
- National Information Society Agency, Daegu 41068, Republic of Korea
| | - Sungjun Park
- School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Chanho Kim
- School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Dohwan Kim
- Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Farah Jabeen
- School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Won Hwa Kim
- Department of Radiology, Kyungpook National University Chilgok Hospital, Kyungpook National University, Daegu 41404, Republic of Korea
| | - Hye Jung Kim
- Department of Radiology, Kyungpook National University Chilgok Hospital, Kyungpook National University, Daegu 41404, Republic of Korea
| | - Jaeil Kim
- School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
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Hussein H, Abbas E, Keshavarzi S, Fazelzad R, Bukhanov K, Kulkarni S, Au F, Ghai S, Alabousi A, Freitas V. Supplemental Breast Cancer Screening in Women with Dense Breasts and Negative Mammography: A Systematic Review and Meta-Analysis. Radiology 2023; 306:e221785. [PMID: 36719288 DOI: 10.1148/radiol.221785] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Background The best supplemental breast cancer screening modality in women at average risk or intermediate risk for breast cancer with dense breast and negative mammogram remains to be determined. Purpose To conduct systematic review and meta-analysis comparing clinical outcomes of the most common available supplemental screening modalities in women at average risk or intermediate risk for breast cancer in patients with dense breasts and mammography with negative findings. Materials and Methods A comprehensive search was conducted until March 12, 2020, in Medline, Epub Ahead of Print and In-Process and Other Non-Indexed Citations; Embase Classic and Embase; Cochrane Central Register of Controlled Trials; and Cochrane Database of Systematic Reviews, for Randomized Controlled Trials and Prospective Observational Studies. Incremental cancer detection rate (CDR); positive predictive value of recall (PPV1); positive predictive value of biopsies performed (PPV3); and interval CDRs of supplemental imaging modalities, digital breast tomosynthesis, handheld US, automated breast US, and MRI in non-high-risk patients with dense breasts and mammography negative for cancer were reviewed. Data metrics and risk of bias were assessed. Random-effects meta-analysis and two-sided metaregression analyses comparing each imaging modality metrics were performed (PROSPERO; CRD42018080402). Results Twenty-two studies reporting 261 233 screened patients were included. Of 132 166 screened patients with dense breast and mammography negative for cancer who met inclusion criteria, a total of 541 cancers missed at mammography were detected with these supplemental modalities. Metaregression models showed that MRI was superior to other supplemental modalities in CDR (incremental CDR, 1.52 per 1000 screenings; 95% CI: 0.74, 2.33; P < .001), including invasive CDR (invasive CDR, 1.31 per 1000 screenings; 95% CI: 0.57, 2.06; P < .001), and in situ disease (rate of ductal carcinoma in situ, 1.91 per 1000 screenings; 95% CI: 0.10, 3.72; P < .04). No differences in PPV1 and PPV3 were identified. The limited number of studies prevented assessment of interval cancer metrics. Excluding MRI, no statistically significant difference in any metrics were identified among the remaining imaging modalities. Conclusion The pooled data showed that MRI was the best supplemental imaging modality in women at average risk or intermediate risk for breast cancer with dense breasts and mammography negative for cancer. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Hooley and Butler in this issue.
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Affiliation(s)
- Heba Hussein
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Engy Abbas
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Sareh Keshavarzi
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Rouhi Fazelzad
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Karina Bukhanov
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Supriya Kulkarni
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Frederick Au
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Sandeep Ghai
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Abdullah Alabousi
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Vivianne Freitas
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
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Heine J, Fowler EE, Weinfurtner RJ, Hume E, Tworoger SS. Breast Density Analysis Using Digital Breast Tomosynthesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.10.527911. [PMID: 36824710 PMCID: PMC9948963 DOI: 10.1101/2023.02.10.527911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
We evaluated an automated percentage of breast density (BD) technique (PDa) with digital breast tomosynthesis (DBT) data. The approach is based on the wavelet expansion followed by analyzing signal dependent noise. Several measures were investigated as risk factors: normalized volumetric; total dense volume; average of the DBT slices (slice-mean); a two-dimensional (2D) metric applied to the synthetic images; and the mean and standard deviations of the pixel values. Volumetric measures were derived theoretically, and PDa was modeled as a function of compressed breast thickness. An alternative method for constructing synthetic 2D mammograms was investigated using the volume results. A matched case-control study (n = 426 pairs) was analyzed. Conditional logistic regression modeling, controlling body mass index and ethnicity, was used to estimate odds ratios (ORs) for each measure with 95% confidence intervals provided parenthetically. There were several significant findings: volumetric measure [OR = 1.43 (1.18, 1.72)], which produced an identical OR as the slice-mean measure as predicted; [OR =1.44 (1.18, 1.75)] when applied to the synthetic images; and mean of the pixel values (volume or 2D synthetic) [ORs ~ 1.31 (1.09, 1.57)]. PDa was modeled as 2nd degree polynomial (concave-down): its maximum value occurred at 0.41×(compressed breast thickness), which was similar across case-control groups, and was significant from this position [OR = 1.47 (1.21, 1.78)]. A standardized 2D synthetic image was produced, where each pixel value represents the percentage of BD above its location. The significant findings indicate the validity of the technique. Derivations supported by empirical analyses produced a new synthetic 2D standardized image technique. Ancillary to the objectives, the results provide evidence for understanding the percentage of BD measure applied to 2D mammograms. Notwithstanding the findings, the study design provides a template for investigating other measures such as texture.
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Mendes J, Matela N, Garcia N. Avoiding Tissue Overlap in 2D Images: Single-Slice DBT Classification Using Convolutional Neural Networks. Tomography 2023; 9:398-412. [PMID: 36828384 PMCID: PMC9962912 DOI: 10.3390/tomography9010032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/08/2023] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
Breast cancer was the most diagnosed cancer around the world in 2020. Screening programs, based on mammography, aim to achieve early diagnosis which is of extreme importance when it comes to cancer. There are several flaws associated with mammography, with one of the most important being tissue overlapping that can result in both lesion masking and fake-lesion appearance. To overcome this, digital breast tomosynthesis takes images (slices) at different angles that are later reconstructed into a 3D image. Having in mind that the slices are planar images where tissue overlapping does not occur, the goal of the work done here was to develop a deep learning model that could, based on the said slices, classify lesions as benign or malignant. The developed model was based on the work done by Muduli et. al, with a slight change in the fully connected layers and in the regularization done. In total, 77 DBT volumes-39 benign and 38 malignant-were available. From each volume, nine slices were taken, one where the lesion was most visible and four above/below. To increase the quantity and the variability of the data, common data augmentation techniques (rotation, translation, mirroring) were applied to the original images three times. Therefore, 2772 images were used for training. Data augmentation techniques were then applied two more times-one set used for validation and one set used for testing. Our model achieved, on the testing set, an accuracy of 93.2% while the values of sensitivity, specificity, precision, F1-score, and Cohen's kappa were 92%, 94%, 94%, 94%, and 0.86, respectively. Given these results, the work done here suggests that the use of single-slice DBT can compare to state-of-the-art studies and gives a hint that with more data, better augmentation techniques and the use of transfer learning might overcome the use of mammograms in this type of studies.
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Affiliation(s)
- João Mendes
- Faculdade de Ciências, Instituto de Biofísica e Engenharia Biomédica, Universidade de Lisboa, 1749-016 Lisboa, Portugal
- Faculdade de Ciências, LASIGE, Universidade de Lisboa, 1749-016 Lisboa, Portugal
| | - Nuno Matela
- Faculdade de Ciências, Instituto de Biofísica e Engenharia Biomédica, Universidade de Lisboa, 1749-016 Lisboa, Portugal
- Correspondence:
| | - Nuno Garcia
- Faculdade de Ciências, LASIGE, Universidade de Lisboa, 1749-016 Lisboa, Portugal
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Qu H, Connolly JJ, Kraft P, Long J, Pereira A, Flatley C, Turman C, Prins B, Mentch F, Lotufo PA, Magnus P, Stampfer MJ, Tamimi R, Eliassen AH, Zheng W, Knudsen GPS, Helgeland O, Butterworth AS, Hakonarson H, Sleiman PM. Trans-ethnic Polygenic Risk Scores for Body Mass Index: An International Hundred K+ Cohorts Consortium Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.17.23284675. [PMID: 36712066 PMCID: PMC9882470 DOI: 10.1101/2023.01.17.23284675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Background While polygenic risk scores hold significant promise in estimating an individual's risk of developing a complex trait such as obesity, their application in the clinic has, to date, been limited by a lack of data from non-European populations. As a collaboration model of the International Hundred K+ Cohorts Consortium (IHCC), we endeavored to develop a globally applicable trans-ethnic PRS for body mass index (BMI) through this relatively new international effort. Methods The PRS model was developed trained and tested at the Center for Applied Genomics (CAG) of The Children's Hospital of Philadelphia (CHOP) based on a BMI meta-analysis from the GIANT consortium. The validated PRS models were subsequently disseminated to the participating sites. Scores were generated by each site locally on their cohorts and summary statistics returned to CAG for final analysis. Results We show that in the absence of a well powered trans-ethnic GWAS from which to derive SNPs and effect estimates, trans-ethnic scores can be generated from European ancestry GWAS using Bayesian approaches such as LDpred to adjust the summary statistics using trans-ethnic linkage disequilibrium reference panels. The ported trans-ethnic scores outperform population specific-PRS across all non-European ancestry populations investigated including East Asians and three-way admixed Brazilian cohort. Conclusions Widespread use of PRS in the clinic is hampered by a lack of genotyping data in individuals of non-European ancestry for the vast majority of traits. Here we show that for a truly polygenic trait such as BMI adjusting the summary statistics of a well powered European ancestry study using trans-ethnic LD reference results in a score that is predictive across a range of ancestries including East Asians and three-way admixed Brazilians.
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Sansone M, Fusco R, Grassi F, Gatta G, Belfiore MP, Angelone F, Ricciardi C, Ponsiglione AM, Amato F, Galdiero R, Grassi R, Granata V, Grassi R. Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography. Curr Oncol 2023; 30:839-853. [PMID: 36661713 PMCID: PMC9858566 DOI: 10.3390/curroncol30010064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/31/2022] [Accepted: 01/06/2023] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND breast cancer (BC) is the world's most prevalent cancer in the female population, with 2.3 million new cases diagnosed worldwide in 2020. The great efforts made to set screening campaigns, early detection programs, and increasingly targeted treatments led to significant improvement in patients' survival. The Full-Field Digital Mammograph (FFDM) is considered the gold standard method for the early diagnosis of BC. From several previous studies, it has emerged that breast density (BD) is a risk factor in the development of BC, affecting the periodicity of screening plans present today at an international level. OBJECTIVE in this study, the focus is the development of mammographic image processing techniques that allow the extraction of indicators derived from textural patterns of the mammary parenchyma indicative of BD risk factors. METHODS a total of 168 patients were enrolled in the internal training and test set while a total of 51 patients were enrolled to compose the external validation cohort. Different Machine Learning (ML) techniques have been employed to classify breasts based on the values of the tissue density. Textural features were extracted only from breast parenchyma with which to train classifiers, thanks to the aid of ML algorithms. RESULTS the accuracy of different tested classifiers varied between 74.15% and 93.55%. The best results were reached by a Support Vector Machine (accuracy of 93.55% and a percentage of true positives and negatives equal to TPP = 94.44% and TNP = 92.31%). The best accuracy was not influenced by the choice of the features selection approach. Considering the external validation cohort, the SVM, as the best classifier with the 7 features selected by a wrapper method, showed an accuracy of 0.95, a sensitivity of 0.96, and a specificity of 0.90. CONCLUSIONS our preliminary results showed that the Radiomics analysis and ML approach allow us to objectively identify BD.
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Affiliation(s)
- Mario Sansone
- Department of Electrical Engineering Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
| | - Francesca Grassi
- Department of Precision Medicine, Division of Radiology, University of Campania Luigi Vanvitelli, 80127 Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Gianluca Gatta
- Department of Precision Medicine, Division of Radiology, University of Campania Luigi Vanvitelli, 80127 Naples, Italy
| | - Maria Paola Belfiore
- Department of Precision Medicine, Division of Radiology, University of Campania Luigi Vanvitelli, 80127 Naples, Italy
| | - Francesca Angelone
- Department of Electrical Engineering Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Carlo Ricciardi
- Department of Electrical Engineering Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Alfonso Maria Ponsiglione
- Department of Electrical Engineering Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Francesco Amato
- Department of Electrical Engineering Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Roberta Galdiero
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Grassi
- Department of Precision Medicine, Division of Radiology, University of Campania Luigi Vanvitelli, 80127 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberto Grassi
- Department of Precision Medicine, Division of Radiology, University of Campania Luigi Vanvitelli, 80127 Naples, Italy
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Shia WC, Lin LS, Wu HK, Chen CJ, Chen DR. Mammographic Density Reduction is Associated to the Prognosis in Asian Breast Cancer Patients Receiving Hormone Therapy. Cancer Control 2023; 30:10732748231160991. [PMID: 36866691 PMCID: PMC9989438 DOI: 10.1177/10732748231160991] [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: 03/04/2023] Open
Abstract
INTRODUCTION Using mammographic density as a significant biomarker for predicting prognosis in adjuvant hormone therapy patients is controversial due to the conflicting results of recent studies. This study aimed to evaluate hormone therapy-induced mammographic density reduction and its association with prognosis in Taiwanese patients. METHODS In this retrospective study, 1941 patients with breast cancer were screened, and 399 patients with estrogen receptor-positive breast cancer who received adjuvant hormone therapy were enrolled. The mammographic density was measured using a fully automatic estimation procedure based on full-field digital mammography. The prognosis included relapse and metastasis during treatment follow-up. The Kaplan-Meier method and Cox proportional hazards model were used for disease-free survival analysis. RESULTS A mammographic density reduction rate >20.8%, measured preoperatively and after receiving hormone therapy from 12-18 months, was a significant threshold for predicting prognosis in patients with breast cancer. The disease-free survival rate was significantly higher in patients whose mammographic density reduction rate was >20.8% (P = .048). CONCLUSION This study's findings could help estimate the prognosis for patients with breast cancer and may improve the quality of adjuvant hormone therapy after enlarging the study cohort in the future.
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Affiliation(s)
- Wei-Chung Shia
- Molecular Medicine Laboratory, Department of Research, Changhua Christian Hospital, Changhua, Taiwan
| | - Li-Sheng Lin
- Department of Breast Surgery, 117821The Affiliated Hospital (Group) of Putian University, Putian, Fujian, China
| | - Hwa-Koon Wu
- Department of Medical Imaging, Changhua Christian Hospital, Changhua, Taiwan
| | - Chih-Jung Chen
- Department of Pathology and Laboratory Medicine, 40293Taichung Veterans General Hospital, Taichung, Taiwan.,Department of General Surgery, Changhua Christian Hospital, Changhua, Taiwan
| | - Dar-Ren Chen
- Department of General Surgery, Changhua Christian Hospital, Changhua, Taiwan
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Manning M, Lucas T, Purrington K, Thompson H, Albrecht TL, Penner L. Moderators of the effects of perceived racism and discrimination on cancer-related health behaviors among two samples of African Americans. Soc Sci Med 2023; 316:114982. [PMID: 35484000 DOI: 10.1016/j.socscimed.2022.114982] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 04/02/2022] [Accepted: 04/15/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Assumptions regarding within-race variation in the associations between measures of discrimination racism and health-related behaviors among African Americans have been largely unexplored. METHODS We conducted secondary analyses of two studies to examine support for a model which describes several theoretical moderators of the effects of discrimination and racism on health behaviors. The first study examined the effects of group-based behavioral information and racial identity on the association between perceived racism and requests for at home colorectal cancer screening tests among a sample of 205 geographically diverse African Americans who participated in an online experiment from 2019 to 2020. RESULTS Group-based behavioral information attenuated the association between perceived racism and requests for at-home screening kit. In the absence of group-based behavioral information, perceived racism was positively associated with screening kit requests for African Americans with weaker racial identity and negatively associated with requests for African Americans with stronger racial identity. The second study examined the influence of personal and group-based perceived discrimination, and behavior-relevant affective information related to a breast cancer risk notification, on 89 Michigan dwelling African American women's self-reported physician communication from 2015 to 2016. Results showed that perceived group-based discrimination was positively associated with physician communication in the absence of negative affective information, and perceived personal discrimination was negatively associated with physician communication as positive affective information increased. CONCLUSIONS Together, these results support our theoretical model highlighting variation in the effects of discrimination and racism on health behaviors among African Americans, and indicates group-relevant behavioral information, racial identity, behavior relevant affective information, and target of discrimination as moderators of the effect. Implications for conceptualizing the effects of racism and discrimination and for examining racially targeted interventions are discussed.
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Affiliation(s)
- Mark Manning
- Department of Psychology, Oakland University, USA
| | - Todd Lucas
- Division of Public Health, Michigan State University, USA
| | | | - Hayley Thompson
- Department of Oncology, Wayne State University School of Medicine, USA
| | | | - Louis Penner
- Department of Oncology, Wayne State University School of Medicine, USA
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Association of body composition fat parameters and breast density in mammography by menopausal status. Sci Rep 2022; 12:22224. [PMID: 36564447 PMCID: PMC9789058 DOI: 10.1038/s41598-022-26839-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 12/21/2022] [Indexed: 12/24/2022] Open
Abstract
We investigated the relationship between body fat-driven obesity and breast fat density in mammography according to menopausal status. We retrospectively analyzed 8537 women (premenopausal, n = 4351; postmenopausal, n = 4186). Body fat parameters included BMI (body mass index), waist circumference (WC), waist-hip ratio (WHR), fat mass index (FMI), Percentage of body fat (PBF), and visceral fat area (VFA). Body fat-driven obesity was defined as follows: overall obesity, BMI ≥ 25 kg/m2; central obesity, WC > 85 cm; abdominal obesity, WHR > 0.85; excessive FMI, the highest quartile (Q4) of FMI; excessive PBF, the highest quartile (Q4) of VFA; visceral obesity, and the highest quartile (Q4) of VFA). Breast density was classified according to BI-RADS (grade a, b, c, and d), which defined as an ordinal scale (grade a = 1, grade b = 2, grade c = 3, and grade d = 4). All body fat-driven obesity parameters were negatively associated with the grade of breast density in both groups of women (p < 0.001): The more fatty parameters are, the less dense breast is. In multivariable binary logistic regression, all body fat-driven obesity parameters also showed a negative association with grade d density (vs. grade a, b, or c). In premenopausal women, BMI was a more associated parameter with grade d density than those of the other fat-driven parameters (OR 0.265, CI 0.204-0.344). In postmenopausal women, WC was more associated with grade d density than the others (OR 0.315, CI 0.239-0.416). We found that BMI, WC, WHR, FMI, PBF and VFA were negatively correlated with dense breast, and the association degree pattern between body fat-driven obesity and dense breast differs according to menopausal status.
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Dorgan JF, Baer HJ, Bertrand KA, LeBlanc ES, Jung S, Magder LS, Snetselaar LG, Stevens VJ, Zhang Y, Van Horn L. Childhood adiposity, serum metabolites and breast density in young women. Breast Cancer Res 2022; 24:91. [PMID: 36536390 PMCID: PMC9764542 DOI: 10.1186/s13058-022-01588-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Childhood adiposity is inversely associated with young adult percent dense breast volume (%DBV) and absolute dense breast volume (ADBV), which could contribute to its protective effect for breast cancer later in life. The objective of this study was to identify metabolites in childhood serum that may mediate the inverse association between childhood adiposity and young adult breast density. METHODS Longitudinal data from 182 female participants in the Dietary Intervention Study in Children (DISC) and the DISC 2006 (DISC06) Follow-Up Study were analyzed. Childhood adiposity was assessed by anthropometry at the DISC visit with serum available that occurred closest to menarche and expressed as a body mass index (BMI) z-score. Serum metabolites were measured by untargeted metabolomics using ultra-high-performance liquid chromatography-tandem mass spectrometry. %DBV and ADBV were measured by magnetic resonance imaging at the DISC06 visit when participants were 25-29 years old. Robust mixed effects linear regression was used to identify serum metabolites associated with childhood BMI z-scores and breast density, and the R package mediation was used to quantify mediation. RESULTS Of the 115 metabolites associated with BMI z-scores (FDR < 0.20), 4 were significantly associated with %DBV and 6 with ADBV before, though not after, adjustment for multiple comparisons. Mediation analysis identified 2 unnamed metabolites, X-16576 and X-24588, as potential mediators of the inverse association between childhood adiposity and dense breast volume. X-16576 mediated 14% (95% confidence interval (CI) = 0.002, 0.46; P = 0.04) of the association of childhood adiposity with %DBV and 11% (95% CI = 0.01, 0.26; P = 0.02) of its association with ADBV. X-24588 also mediated 7% (95% CI = 0.001, 0.18; P = 0.05) of the association of childhood adiposity with ADBV. None of the other metabolites examined contributed to mediation of the childhood adiposity-%DBV association, though there was some support for contributions of lysine, valine and 7-methylguanine to mediation of the inverse association of childhood adiposity with ADBV. CONCLUSIONS Additional large longitudinal studies are needed to identify metabolites and other biomarkers that mediate the inverse association of childhood adiposity with breast density and possibly breast cancer risk.
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Affiliation(s)
- Joanne F Dorgan
- Division of Cancer Epidemiology, Department of Epidemiology and Public Health, University of Maryland School of Medicine, 660 West Redwood St., Howard Hall, Room 102E, Baltimore, MD, 21201, USA.
| | - Heather J Baer
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Kimberly A Bertrand
- Slone Epidemiology Center, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Erin S LeBlanc
- Kaiser Permanente Center for Health Research, Portland, OR, 97227, USA
| | - Seungyoun Jung
- Department of Nutritional Science and Food Management, Ewha Womans University, Seoul, South Korea
- Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, South Korea
| | - Laurence S Magder
- Division of Cancer Epidemiology, Department of Epidemiology and Public Health, University of Maryland School of Medicine, 660 West Redwood St., Howard Hall, Room 102E, Baltimore, MD, 21201, USA
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, 21201, USA
| | - Linda G Snetselaar
- Department of Epidemiology, University of Iowa College of Public Health, Iowa City, IA, 52242, USA
| | - Victor J Stevens
- Kaiser Permanente Center for Health Research, Portland, OR, 97227, USA
| | - Yuji Zhang
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, 21201, USA
| | - Linda Van Horn
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
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MicroRNAs: A Link between Mammary Gland Development and Breast Cancer. Int J Mol Sci 2022; 23:ijms232415978. [PMID: 36555616 PMCID: PMC9786715 DOI: 10.3390/ijms232415978] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/13/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Breast cancer is among the most common cancers in women, second to skin cancer. Mammary gland development can influence breast cancer development in later life. Processes such as proliferation, invasion, and migration during mammary gland development can often mirror processes found in breast cancer. MicroRNAs (miRNAs), small, non-coding RNAs, can repress post-transcriptional RNA expression and can regulate up to 80% of all genes. Expression of miRNAs play a key role in mammary gland development, and aberrant expression can initiate or promote breast cancer. Here, we review the role of miRNAs in mammary development and breast cancer, and potential parallel roles. A total of 32 miRNAs were found to be expressed in both mammary gland development and breast cancer. These miRNAs are involved in proliferation, metastasis, invasion, and apoptosis in both processes. Some miRNAs were found to have contradictory roles, possibly due to their ability to target many genes at once. Investigation of miRNAs and their role in mammary gland development may inform about their role in breast cancer. In particular, by studying miRNA in development, mechanisms and potential targets for breast cancer treatment may be elucidated.
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Ali KAS, Fateh SM. Mammographic breast density status in women aged more than 40 years in Sulaimaniyah, Iraq: a cross-sectional study. J Int Med Res 2022; 50:3000605221139712. [DOI: 10.1177/03000605221139712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Objective Mammography is the gold standard screening procedure for the early diagnosis of breast cancer. This study aimed to determine the distribution of breast density among women older than 40 years in Sulaimaniyah, Iraq, and to examine the correlations between breast density and various risk factors. Methods This cross-sectional study included 750 women who received routine mammographic breast screening at Sulaimaniyah Breast Center. Bilateral standard two-view mammographic images (craniocaudal and mediolateral oblique projections) were acquired and reported using a picture archiving and communication system. American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) assessment categories C and D were considered as dense. Results A total of 54.3% of breasts were classified as dense, with ACR-BI-RADS categories C or D. Breast density was significantly associated with age, body mass index, a family history of breast cancer, and pre-menopause, and women with no history of breastfeeding were more likely to have dense breasts than those with partial or complete breastfeeding. Conclusions This study revealed that women from Sulaimaniyah with a distinct breast-density profile at mammographic screening may have a significantly increased risk of breast cancer.
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Affiliation(s)
- Kalthum Abdullah Sofi Ali
- Department of Radiology/Surgery, College of Medicine, University of Sulaimani, 46001 Sulaimaniyah, Iraq
| | - Salah Muhammed Fateh
- Department of Radiology/Surgery, College of Medicine, University of Sulaimani, 46001 Sulaimaniyah, Iraq
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Li T, Li J, Heard R, Gandomkar Z, Ren J, Dai M, Brennan P. Understanding mammographic breast density profile in China: A Sino-Australian comparative study of breast density using real-world data from cancer screening programs. Asia Pac J Clin Oncol 2022; 18:696-705. [PMID: 35238173 PMCID: PMC9790382 DOI: 10.1111/ajco.13763] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 01/27/2022] [Indexed: 12/30/2022]
Abstract
AIM This study aims at understanding mammographic density profile in China by comparing the density between women in China and Australia. METHODS Data of 3250 women aged 45-69 were obtained from the Cancer Screening Program in Urban China and data of 1384 Australian counterparts at same age range were gathered from the Lifepool project. Demographic and reproductive details and mammograms for each cohort were collected. Mammographic density was assessed using AutoDensity, and two metrics, percentage density (PD) and dense area (DA), were applied. T-tests were used to compare the means of mammographic density between two populations of all, premenopausal, and postmenopausal women. Two-way ANOVA was conducted to examine interactions of population (Chinese/Australian) and each variable of interest upon mammographic density. RESULTS Chinese women had 9.61%, 8.20%, and 9.28% higher PD than their Australian counterparts in all, premenopausal, and postmenopausal women, respectively (all p < 0.001). The mean differences in DA between two population were 1.81 cm2 (p < 0.001), 0.55 cm2 (p = 0.472), and 1.76 cm2 (p = 0.003) for all, premenopausal, and postmenopausal women, respectively. There were significant interactions between population and age (F[4, 4624] = 4.12, p = 0.003), BMI (F[2, 4628] = 3.92, p = 0.020), age at first birth (F[1, 4250] = 11.69, p < 0.001), breastfeeding history (F[1, 4479] = 17.79, p < 0.001), and breastfeeding duration (F[1, 3526] = 66.90, p < 0.001) upon PD. Interaction was only found for breastfeeding history (F[1, 4479] = 4.79, p = 0.029) and breastfeeding duration (F[1, 3526] = 17.72, p < 0.001) for DA. CONCLUSIONS Both PD and DA were found to be higher in Chinese women compared to Australian women. The density difference by menopause status was shown and breastfeeding history affected breast density differently in both populations.
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Affiliation(s)
- Tong Li
- Medical Imaging Science, School of Health Sciences, Faculty of Medicine and HealthThe University of SydneyCamperdownNew South WalesAustralia
| | - Jing Li
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Rob Heard
- School of Health Sciences, Faculty of Medicine and HealthThe University of SydneyCamperdownNew South WalesAustralia
| | - Ziba Gandomkar
- Medical Imaging Science, School of Health Sciences, Faculty of Medicine and HealthThe University of SydneyCamperdownNew South WalesAustralia
| | - Jiansong Ren
- Office of Cancer ScreeningNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Min Dai
- Office of Cancer ScreeningNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Patrick Brennan
- Medical Imaging Science, School of Health Sciences, Faculty of Medicine and HealthThe University of SydneyCamperdownNew South WalesAustralia
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