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Henderson JT, Webber EM, Weyrich MS, Miller M, Melnikow J. Screening for Breast Cancer: Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA 2024; 331:1931-1946. [PMID: 38687490 DOI: 10.1001/jama.2023.25844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Importance Breast cancer is a leading cause of cancer mortality for US women. Trials have established that screening mammography can reduce mortality risk, but optimal screening ages, intervals, and modalities for population screening guidelines remain unclear. Objective To review studies comparing different breast cancer screening strategies for the US Preventive Services Task Force. Data Sources MEDLINE, Cochrane Library through August 22, 2022; literature surveillance through March 2024. Study Selection English-language publications; randomized clinical trials and nonrandomized studies comparing screening strategies; expanded criteria for screening harms. Data Extraction and Synthesis Two reviewers independently assessed study eligibility and quality; data extracted from fair- and good-quality studies. Main Outcomes and Measures Mortality, morbidity, progression to advanced cancer, interval cancers, screening harms. Results Seven randomized clinical trials and 13 nonrandomized studies were included; 2 nonrandomized studies reported mortality outcomes. A nonrandomized trial emulation study estimated no mortality difference for screening beyond age 74 years (adjusted hazard ratio, 1.00 [95% CI, 0.83 to 1.19]). Advanced cancer detection did not differ following annual or biennial screening intervals in a nonrandomized study. Three trials compared digital breast tomosynthesis (DBT) mammography screening with digital mammography alone. With DBT, more invasive cancers were detected at the first screening round than with digital mammography, but there were no statistically significant differences in interval cancers (pooled relative risk, 0.87 [95% CI, 0.64-1.17]; 3 studies [n = 130 196]; I2 = 0%). Risk of advanced cancer (stage II or higher) at the subsequent screening round was not statistically significant for DBT vs digital mammography in the individual trials. Limited evidence from trials and nonrandomized studies suggested lower recall rates with DBT. An RCT randomizing individuals with dense breasts to invitations for supplemental screening with magnetic resonance imaging reported reduced interval cancer risk (relative risk, 0.47 [95% CI, 0.29-0.77]) and additional false-positive recalls and biopsy results with the intervention; no longer-term advanced breast cancer incidence or morbidity and mortality outcomes were available. One RCT and 1 nonrandomized study of supplemental ultrasound screening reported additional false-positives and no differences in interval cancers. Conclusions and Relevance Evidence comparing the effectiveness of different breast cancer screening strategies is inconclusive because key studies have not yet been completed and few studies have reported the stage shift or mortality outcomes necessary to assess relative benefits.
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Affiliation(s)
- Jillian T Henderson
- Kaiser Permanente Evidence-based Practice Center, Center for Health Research, Portland, Oregon
| | - Elizabeth M Webber
- Kaiser Permanente Evidence-based Practice Center, Center for Health Research, Portland, Oregon
| | - Meghan S Weyrich
- University of California Davis Center for Healthcare Policy and Research, Sacramento
| | - Marykate Miller
- University of California Davis Center for Healthcare Policy and Research, Sacramento
| | - Joy Melnikow
- University of California Davis Center for Healthcare Policy and Research, Sacramento
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Franklin J, Hayes J, Knippa E, Dogan B. False negative breast cancers on imaging and associated risk factors: a single institution six-year analysis. Breast Cancer Res Treat 2024; 205:507-520. [PMID: 38483757 DOI: 10.1007/s10549-024-07259-0] [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: 05/09/2023] [Accepted: 01/18/2024] [Indexed: 05/19/2024]
Abstract
PURPOSE Mitigating false negative imaging studies remains an important issue given its association with worse morbidity and mortality in patients with breast cancer. We aimed to identify risk factors that predispose to false negative breast imaging exams. METHODS In an IRB-approved, HIPAA compliant retrospective study, we identified all patients who were diagnosed with breast cancer within 365 days of a negative imaging study assessed as BI-RADS 1-3 between January 1, 2014 and January 31, 2020. A matched cohort based on mammographic breast density was created from randomly selected studies with BI-RADS 4-5 designation that yielded breast cancer at pathology within the same time frame. Patient and cancer characteristics, prior personal history of breast cancer and gene mutation status were collected from patient charts. Pearson chi-squared and Student's t-test on two independent groups with significance at < 0.05 was used for statistical analysis. RESULTS We identified 155 false negative studies of 129 missed cancers and 128 breast density matched true positive cancers. False negative studies were screening mammograms in 57.42% (89/155), diagnostic mammograms in 29.68% (46/155), ultrasounds in 6.45% (10/155) and MRIs in 6.45% (10/155). Rates of personal (41.09% vs. 18.75%, p < 0.001) and family history of breast cancer (68.22% vs. 49.21%, p = 0.002) were higher in the false negative cohort and remained significant when asymptomatic MRI-detected cancers were removed. CONCLUSION Our findings suggest that supplemental screening may be useful in breast cancer survivors.
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Affiliation(s)
- Jordan Franklin
- The University of Texas Southwestern Medical Center Medical School, Dallas, TX, USA.
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA.
| | - Jody Hayes
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Emily Knippa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Başak Dogan
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Couto HL, Gargano LP, de Oliveira VM, Coelho BA, Pessoa EC, Hassan AT, Silva AL, Urban LABD, Fernandes LC, Sharma N, Mann R, McIntosh SA, Zanghelini F. Cost-Effectiveness Analysis of Digital Breast Tomosynthesis Added to Synthetic Mammography in Breast Cancer Screening in Brazil. PHARMACOECONOMICS - OPEN 2024; 8:403-416. [PMID: 38233699 PMCID: PMC11058155 DOI: 10.1007/s41669-023-00470-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/18/2023] [Indexed: 01/19/2024]
Abstract
BACKGROUND Literature meta-analysis results show that digital breast tomosynthesis (DBT) combined with synthesized two-dimensional (s2D) mammograms can reduce recalls and improve breast cancer detection. Uncertainty regarding the screening of patients with breast cancer presents a health economic challenge, both in terms of healthcare resource use and quality of life impact on patients. OBJECTIVE This study aims to estimate the cost effectiveness of DBT + s2D versus digital mammography (DM) used in a biennial breast cancer screening setting of women aged 40-69 years with scattered areas of fibroglandular breast density and heterogeneous dense breasts in the Brazilian supplementary health system. METHODS A cost-effectiveness analysis was performed on the basis of clinical data obtained from a systematic review with meta-analysis performed to evaluate the analytical validity and clinical utility of DBT + s2D compared with DM. The search was conducted in the PubMed, Cochrane Library and Embase databases, with the main descriptors of the technology, a comparator, and the clinical condition in question, on 9 June 2022. The hybrid economic model (decision tree plus Markov model) simulated costs and outcomes over a lifetime for women aged 40-69 years with scattered areas of fibroglandular breast density and heterogeneous dense breasts. We analyzed incremental cost-effectiveness ratio (ICER) to measure the incremental cost difference per quality-adjusted life year (QALY) of adding DBT + s2D to breast cancer screening. RESULTS DBT + s2D incurred a cost saving of € 954.02 per patient, in the time horizon of 30 years, compared with DM, and gained 5.1989 QALYs, which would be considered a dominant intervention. These results were confirmed in sensitivity analyses. CONCLUSION Switching from DM to biennial DBT + s2D was cost effective. Furthermore, reductions in false-positive recall rates should also be considered in decision making.
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Affiliation(s)
- Henrique Lima Couto
- Brazilian Society of Mastology, Rio de Janeiro, Rio de Janeiro, Brazil.
- Brazilian Federation of Associations of Gynecologists and Obstetricians, Rio de Janeiro, Rio de Janeiro, Brazil.
- Redimama-Redimasto, Belo Horizonte, Minas Gerais, Brazil.
- Brazilian Society of Mastology, Av. João Pinheiro, 161-Centro, Belo Horizonte, MG, 30130-180, Brazil.
| | - Ludmila Peres Gargano
- Department of Social Pharmacy, Faculty of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, Brazil
- MAPESolutions, São Paulo, São Paulo, Brazil
| | - Vilmar Marques de Oliveira
- Brazilian Society of Mastology, Santa Casa de São Paulo School of Medical Sciences, São Paulo, São Paulo, Brazil
| | - Bertha Andrade Coelho
- Brazilian Society of Mastology, Rio de Janeiro, Rio de Janeiro, Brazil
- UNIFIMOC University Center, Montes Claros, Minas Gerais, Brazil
| | - Eduardo Carvalho Pessoa
- Brazilian Society of Mastology, Rio de Janeiro, Rio de Janeiro, Brazil
- Brazilian Federation of Associations of Gynecologists and Obstetricians, Rio de Janeiro, Rio de Janeiro, Brazil
- Department of Obstetrics and Gynecology, Botucatu Medical School, Sao Paulo State University-UNESP, Botucatu, Sao Paulo, Brazil
| | - Augusto Tufi Hassan
- Brazilian Society of Mastology, Rio de Janeiro, Rio de Janeiro, Brazil
- Oncoclinicas-CAM, Salvador, BA, Brazil
| | - Agnaldo Lopes Silva
- Brazilian Federation of Associations of Gynecologists and Obstetricians, Rio de Janeiro, Rio de Janeiro, Brazil
- Department of Obstetrics and Gynecology, Botucatu Medical School, Sao Paulo State University-UNESP, Botucatu, Sao Paulo, Brazil
- Department of Obstetrics and Gynecology, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | | | | | - Nisha Sharma
- Breast Screening Unit, Seacroft Hospital, Leeds Teaching Hospital NHS Trust, York Road, Leeds, West Yorkshire, LS146UH, UK
| | - Ritse Mann
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Stuart A McIntosh
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Fernando Zanghelini
- MAPESolutions, São Paulo, São Paulo, Brazil
- Health Economics Consultant, Norwich Medical School, University of East Anglia, Norwich, UK
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Taylor-Phillips S, Jenkinson D, Stinton C, Kunar MA, Watson DG, Freeman K, Mansbridge A, Wallis MG, Kearins O, Hudson S, Clarke A. Fatigue and vigilance in medical experts detecting breast cancer. Proc Natl Acad Sci U S A 2024; 121:e2309576121. [PMID: 38437559 PMCID: PMC10945845 DOI: 10.1073/pnas.2309576121] [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: 06/08/2023] [Accepted: 12/19/2023] [Indexed: 03/06/2024] Open
Abstract
An abundance of laboratory-based experiments has described a vigilance decrement of reducing accuracy to detect targets with time on task, but there are few real-world studies, none of which have previously controlled the environment to control for bias. We describe accuracy in clinical practice for 360 experts who examined >1 million women's mammograms for signs of cancer, whilst controlling for potential biases. The vigilance decrement pattern was not observed. Instead, test accuracy improved over time, through a reduction in false alarms and an increase in speed, with no significant change in sensitivity. The multiple-decision model explains why experts miss targets in low prevalence settings through a change in decision threshold and search quit threshold and propose it should be adapted to explain these observed patterns of accuracy with time on task. What is typically thought of as standard and robust research findings in controlled laboratory settings may not directly apply to real-world environments and instead large, controlled studies in relevant environments are needed.
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Affiliation(s)
- Sian Taylor-Phillips
- Division of Health Sciences, Warwick Medical School, University of Warwick, CoventryCV4 7AL, United Kingdom
| | - David Jenkinson
- Division of Health Sciences, Warwick Medical School, University of Warwick, CoventryCV4 7AL, United Kingdom
| | - Chris Stinton
- Division of Health Sciences, Warwick Medical School, University of Warwick, CoventryCV4 7AL, United Kingdom
| | - Melina A. Kunar
- Department of Psychology, University of Warwick, CoventryCV4 7AL, United Kingdom
| | - Derrick G. Watson
- Department of Psychology, University of Warwick, CoventryCV4 7AL, United Kingdom
| | - Karoline Freeman
- Division of Health Sciences, Warwick Medical School, University of Warwick, CoventryCV4 7AL, United Kingdom
| | - Alice Mansbridge
- Division of Health Sciences, Warwick Medical School, University of Warwick, CoventryCV4 7AL, United Kingdom
| | - Matthew G. Wallis
- Cambridge Breast Unit and National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre, Cambridge University Hospitals NHS Trust, CambridgeCB2 0QQ, United Kingdom
| | - Olive Kearins
- Screening Quality Assurance Service, National Health Service (NHS) England, BirminghamB2 4HQ, United Kingdom
| | - Sue Hudson
- Peel and Schriek Consulting Limited, London NW3 4QG, United Kingdom
| | - Aileen Clarke
- Division of Health Sciences, Warwick Medical School, University of Warwick, CoventryCV4 7AL, United Kingdom
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Wolfson EA, Schonberg MA, Eliassen AH, Bertrand KA, Shvetsov YB, Rosner BA, Palmer JR, LaCroix AZ, Chlebowski RT, Nelson RA, Ngo LH. Validating a model for predicting breast cancer and nonbreast cancer death in women aged 55 years and older. J Natl Cancer Inst 2024; 116:81-96. [PMID: 37676833 PMCID: PMC10777669 DOI: 10.1093/jnci/djad188] [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: 05/15/2023] [Revised: 07/24/2023] [Accepted: 08/29/2023] [Indexed: 09/09/2023] Open
Abstract
BACKGROUND To support mammography screening decision making, we developed a competing-risk model to estimate 5-year breast cancer risk and 10-year nonbreast cancer death for women aged 55 years and older using Nurses' Health Study data and examined model performance in the Black Women's Health Study (BWHS). Here, we examine model performance in predicting 10-year outcomes in the BWHS, Women's Health Initiative-Extension Study (WHI-ES), and Multiethnic Cohort (MEC) and compare model performance to existing breast cancer prediction models. METHODS We used competing-risk regression and Royston and Altman methods for validating survival models to calculate our model's calibration and discrimination (C index) in BWHS (n = 17 380), WHI-ES (n = 106 894), and MEC (n = 49 668). The Nurses' Health Study development cohort (n = 48 102) regression coefficients were applied to the validation cohorts. We compared our model's performance with breast cancer risk assessment tool (Gail) and International Breast Cancer Intervention Study (IBIS) models by computing breast cancer risk estimates and C statistics. RESULTS When predicting 10-year breast cancer risk, our model's C index was 0.569 in BWHS, 0.572 in WHI-ES, and 0.576 in MEC. The Gail model's C statistic was 0.554 in BWHS, 0.564 in WHI-ES, and 0.551 in MEC; IBIS's C statistic was 0.547 in BWHS, 0.552 in WHI-ES, and 0.562 in MEC. The Gail model underpredicted breast cancer risk in WHI-ES; IBIS underpredicted breast cancer risk in WHI-ES and in MEC but overpredicted breast cancer risk in BWHS. Our model calibrated well. Our model's C index for predicting 10-year nonbreast cancer death was 0.760 in WHI-ES and 0.763 in MEC. CONCLUSIONS Our competing-risk model performs as well as existing breast cancer prediction models in diverse cohorts and predicts nonbreast cancer death. We are developing a website to disseminate our model.
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Affiliation(s)
- Emily A Wolfson
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mara A Schonberg
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Harvard School of Public Health, Boston, MA, USA
| | - Kimberly A Bertrand
- Slone Epidemiology Center at Boston University and Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Yurii B Shvetsov
- University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Bernard A Rosner
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Harvard School of Public Health, Boston, MA, USA
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University and Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Andrea Z LaCroix
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | | | - Rebecca A Nelson
- Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, USA
| | - Long H Ngo
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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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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Mao X, He W, Humphreys K, Eriksson M, Holowko N, Yang H, Tapia J, Hall P, Czene K. Breast Cancer Incidence After a False-Positive Mammography Result. JAMA Oncol 2024; 10:63-70. [PMID: 37917078 PMCID: PMC10623302 DOI: 10.1001/jamaoncol.2023.4519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 07/26/2023] [Indexed: 11/03/2023]
Abstract
Importance False-positive mammography results are common. However, long-term outcomes after a false-positive result remain unclear. Objectives To examine long-term outcomes after a false-positive mammography result and to investigate whether the association of a false-positive mammography result with cancer differs by baseline characteristics, tumor characteristics, and time since the false-positive result. Design, Setting, and Participants This population-based, matched cohort study was conducted in Sweden from January 1, 1991, to March 31, 2020. It included 45 213 women who received a first false-positive mammography result between 1991 and 2017 and 452 130 controls matched on age, calendar year of mammography, and screening history (no previous false-positive result). The study also included 1113 women with a false-positive result and 11 130 matched controls with information on mammographic breast density from the Karolinska Mammography Project for Risk Prediction of Breast Cancer study. Statistical analysis was performed from April 2022 to February 2023. Exposure A false-positive mammography result. Main Outcomes and Measures Breast cancer incidence and mortality. Results The study cohort included 497 343 women (median age, 52 years [IQR, 42-59 years]). The 20-year cumulative incidence of breast cancer was 11.3% (95% CI, 10.7%-11.9%) among women with a false-positive result vs 7.3% (95% CI, 7.2%-7.5%) among those without, with an adjusted hazard ratio (HR) of 1.61 (95% CI, 1.54-1.68). The corresponding HRs were higher among women aged 60 to 75 years at the examination (HR, 2.02; 95% CI, 1.80-2.26) and those with lower mammographic breast density (HR, 4.65; 95% CI, 2.61-8.29). In addition, breast cancer risk was higher for women who underwent a biopsy at the recall (HR, 1.77; 95% CI, 1.63-1.92) than for those without a biopsy (HR, 1.51; 95% CI, 1.43-1.60). Cancers after a false-positive result were more likely to be detected on the ipsilateral side of the false-positive result (HR, 1.92; 95% CI, 1.81-2.04) and were more common during the first 4 years of follow-up (HR, 2.57; 95% CI, 2.33-2.85 during the first 2 years; HR, 1.93; 95% CI, 1.76-2.12 at >2 to 4 years). No statistical difference was found for different tumor characteristics (except for larger tumor size). Furthermore, associated with the increased risk of breast cancer, women with a false-positive result had an 84% higher rate of breast cancer death than those without (HR, 1.84; 95% CI, 1.57-2.15). Conclusions and Relevance This study suggests that the risk of developing breast cancer after a false-positive mammography result differs by individual characteristics and follow-up. These findings can be used to develop individualized risk-based breast cancer screening after a false-positive result.
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Affiliation(s)
- Xinhe Mao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Wei He
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Chronic Disease Research Institute, the Children’s Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Natalie Holowko
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
| | - Haomin Yang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - José Tapia
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Salim M, Dembrower K, Eklund M, Smith K, Strand F. Differences and similarities in false interpretations by AI CAD and radiologists in screening mammography. Br J Radiol 2023; 96:20230210. [PMID: 37660400 PMCID: PMC10607417 DOI: 10.1259/bjr.20230210] [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: 02/28/2023] [Revised: 07/10/2023] [Accepted: 07/20/2023] [Indexed: 09/05/2023] Open
Abstract
OBJECTIVE We aimed to evaluate the false interpretations between artificial intelligence (AI) and radiologists in screening mammography to get a better understanding of how the distribution of diagnostic mistakes might change when moving from entirely radiologist-driven to AI-integrated breast cancer screening. METHODS AND MATERIALS This retrospective case-control study was based on a mammography screening cohort from 2008 to 2015. The final study population included screening examinations for 714 women diagnosed with breast cancer and 8029 randomly selected healthy controls. Oversampling of controls was applied to attain a similar cancer proportion as in the source screening cohort. We examined how false-positive (FP) and false-negative (FN) assessments by AI, the first reader (RAD 1) and the second reader (RAD 2), were associated with age, density, tumor histology and cancer invasiveness in a single- and double-reader setting. RESULTS For each reader, the FN assessments were distributed between low- and high-density females with 53 (42%) and 72 (58%) for AI; 59 (36%) and 104 (64%) for RAD 1 and 47 (36%) and 84 (64%) for RAD 2. The corresponding numbers for FP assessments were 1820 (47%) and 2016 (53%) for AI; 1568 (46%) and 1834 (54%) for RAD 1 and 1190 (43%) and 1610 (58%) for RAD 2. For ductal cancer, the FN assessments were 79 (77%) for AI CAD; with 120 (83%) for RAD 1 and with 96 (16%) for RAD 2. For the double-reading simulation, the FP assessments were distributed between younger and older females with 2828 (2.5%) and 1554 (1.4%) for RAD 1 + RAD 2; 3850 (3.4%) and 2940 (2.6%) for AI+RAD 1 and 3430 (3%) and 2772 (2.5%) for AI+RAD 2. CONCLUSION The most pronounced decrease in FN assessments was noted for females over the age of 55 and for high density-women. In conclusion, AI could have an important complementary role when combined with radiologists to increase sensitivity for high-density and older females. ADVANCES IN KNOWLEDGE Our results highlight the potential impact of integrating AI in breast cancer screening, particularly to improve interpretation accuracy. The use of AI could enhance screening outcomes for high-density and older females.
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Affiliation(s)
| | | | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Kevin Smith
- Science for Life Laboratory, KTH Royal Insitute of Technology, Stockholm, Sweden
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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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10
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Zhang J, Mazurowski MA, Grimm LJ. Feasibility of predicting a screening digital breast tomosynthesis recall using features extracted from the electronic medical record. Eur J Radiol 2023; 166:110979. [PMID: 37473618 DOI: 10.1016/j.ejrad.2023.110979] [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: 02/12/2023] [Revised: 07/05/2023] [Accepted: 07/12/2023] [Indexed: 07/22/2023]
Abstract
PURPOSE Tools to predict a screening mammogram recall at the time of scheduling could improve patient care. We extracted patient demographic and breast care history information within the electronic medical record (EMR) for women undergoing digital breast tomosynthesis (DBT) to identify which factors were associated with a screening recall recommendation. METHOD In 2018, 21,543 women aged 40 years or greater who underwent screening DBT at our institution were identified. Demographic information and breast care factors were extracted automatically from the EMR. The primary outcome was a screening recall recommendation of BI-RADS 0. A multivariable logistic regression model was built and included age, race, ethnicity groups, family breast cancer history, personal breast cancer history, surgical breast cancer history, recall history, and days since last available screening mammogram. RESULTS Multiple factors were associated with a recall on the multivariable model: history of breast cancer surgery (OR: 2.298, 95% CI: 1.854, 2.836); prior recall within the last five years (vs no prior, OR: 0.768, 95% CI: 0.687, 0.858); prior screening mammogram within 0-18 months (vs no prior, OR: 0.601, 95% CI: 0.520, 0.691), prior screening mammogram within 18-30 months (vs no prior, OR: 0.676, 95% CI: 0.520, 0.691); and age (normalized OR: 0.723, 95% CI: 0.690, 0.758). CONCLUSIONS It is feasible to predict a DBT screening recall recommendation using patient demographics and breast care factors that can be extracted automatically from the EMR.
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Affiliation(s)
- Jikai Zhang
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States Room 10070, 2424 Erwin Road, Durham, NC 27705, United States.
| | - Maciej A Mazurowski
- Department of Radiology, Duke University Medical Center, Durham, NC, United States; Department of Electrical and Computer Engineering, Department of Biostatistics and Bioinformatics, Department of Computer Science, Duke University, Durham, NC, United States
| | - Lars J Grimm
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States Room 10070, 2424 Erwin Road, Durham, NC 27705, United States
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11
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Grewal D, Bhanu KU, Sahni H, Maheshwari S, Kakria N, Mishra P, Anand V. Role of qualitative contrast-enhanced ultrasound in the diagnosis of malignant breast lesions. Med J Armed Forces India 2023; 79:414-420. [PMID: 37441290 PMCID: PMC10334224 DOI: 10.1016/j.mjafi.2022.01.015] [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: 06/26/2021] [Accepted: 01/27/2022] [Indexed: 12/24/2022] Open
Abstract
Background Carcinoma breast is the commonest cancer among women. Various authors have studied breast cancer with Contrast-Enhanced Ultrasound (CEUS) with promising results. Despite promising results, the additional cost of post-processing software limits its availability. In this study, we evaluated the utility of CEUS in differentiating malignant from benign breast lesions on regular ultrasound equipment without the use of dedicated software. Methods We performed CEUS in 121 women with 121 breast lesions. CEUS was done by creating a custom preset on existing ultrasound equipment with the help of an application specialist authorized by the vendor. Lesions were evaluated qualitatively without the use of any commercial software. The pattern of enhancement i.e. homogenous, heterogeneous, peripheral, or no enhancement, and the number of penetrating vessels i.e., few or multiple were recorded. Results were compared with histopathological diagnosis. Results There were a total of 121 breast lesions. The study showed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 86.67 %, 54.10 %, 65 %, and 80.49% respectively for differentiating benign vs malignant lesions on the basis of the pattern of contrast enhancement. Using penetrating vessels for differentiating malignant lesions from benign lesions, the sensitivity, specificity, PPV, and NPV were found to be 64%, 67.86%, 78.05%, and 51.35% respectively. Conclusion CEUS is useful in differentiating malignant from benign breast lesions. It can be easily performed by creating a custom preset on standard ultrasound equipment without the use of expensive software.
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Affiliation(s)
- D.S. Grewal
- Associate Professor, Department of Radiodiagnosis & Imaging, Armed Forces Medical College, Pune, India
| | - K. Uday Bhanu
- Professor, Department of Radiodiagnosis & Imaging, Armed Forces Medical College, Pune, India
| | - Hirdesh Sahni
- Professor & Head, Department of Radiodiagnosis & Imaging, Armed Forces Medical College, Pune, India
| | - Saurabh Maheshwari
- Assistant Professor, Department of Radiodiagnosis & Imaging, Armed Forces Medical College, Pune, India
| | - Neha Kakria
- Classified Specialist (Radiology), Command Hospital (Northern Command), Udhampur, India
| | - P.S. Mishra
- Classified Specialist, Department of Pathology, Army Hospital (R & R), New Delhi, India
| | - Varun Anand
- Clinical Tutor, Department of Radiodiagnosis & Imaging, Armed Forces Medical College, Pune, India
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12
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Chen QQ, Lin ST, Ye JY, Tong YF, Lin S, Cai SQ. Diagnostic value of mammography density of breast masses by using deep learning. Front Oncol 2023; 13:1110657. [PMID: 37333830 PMCID: PMC10275606 DOI: 10.3389/fonc.2023.1110657] [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: 11/29/2022] [Accepted: 05/23/2023] [Indexed: 06/20/2023] Open
Abstract
Objective In order to explore the relationship between mammographic density of breast mass and its surrounding area and benign or malignant breast, this paper proposes a deep learning model based on C2FTrans to diagnose the breast mass using mammographic density. Methods This retrospective study included patients who underwent mammographic and pathological examination. Two physicians manually depicted the lesion edges and used a computer to automatically extend and segment the peripheral areas of the lesion (0, 1, 3, and 5 mm, including the lesion). We then obtained the mammary glands' density and the different regions of interest (ROI). A diagnostic model for breast mass lesions based on C2FTrans was constructed based on a 7: 3 ratio between the training and testing sets. Finally, receiver operating characteristic (ROC) curves were plotted. Model performance was assessed using the area under the ROC curve (AUC) with 95% confidence intervals (CI), sensitivity, and specificity. Results In total, 401 lesions (158 benign and 243 malignant) were included in this study. The probability of breast cancer in women was positively correlated with age and mass density and negatively correlated with breast gland classification. The largest correlation was observed for age (r = 0.47). Among all models, the single mass ROI model had the highest specificity (91.8%) with an AUC = 0.823 and the perifocal 5mm ROI model had the highest sensitivity (86.9%) with an AUC = 0.855. In addition, by combining the cephalocaudal and mediolateral oblique views of the perifocal 5 mm ROI model, we obtained the highest AUC (AUC = 0.877 P < 0.001). Conclusions Deep learning model of mammographic density can better distinguish benign and malignant mass-type lesions in digital mammography images and may become an auxiliary diagnostic tool for radiologists in the future.
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Affiliation(s)
- Qian-qian Chen
- Department of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Shu-ting Lin
- Department of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Jia-yi Ye
- Department of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Yun-fei Tong
- Shanghai Yanghe Huajian Artificial Intelligence Technology Co. Ltd., Shanghai, China
| | - Shu Lin
- Centre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
- Department of Neuroendocrinology, Group of Neuroendocrinology, Garvan Institute of Medical Research, Sydney, Australia
| | - Si-qing Cai
- Department of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
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13
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Sprague BL, Coley RY, Lowry KP, Kerlikowske K, Henderson LM, Su YR, Lee CI, Onega T, Bowles EJA, Herschorn SD, diFlorio-Alexander RM, Miglioretti DL. Digital Breast Tomosynthesis versus Digital Mammography Screening Performance on Successive Screening Rounds from the Breast Cancer Surveillance Consortium. Radiology 2023; 307:e223142. [PMID: 37249433 PMCID: PMC10315524 DOI: 10.1148/radiol.223142] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/23/2023] [Accepted: 03/29/2023] [Indexed: 05/31/2023]
Abstract
Background Prior cross-sectional studies have observed that breast cancer screening with digital breast tomosynthesis (DBT) has a lower recall rate and higher cancer detection rate compared with digital mammography (DM). Purpose To evaluate breast cancer screening outcomes with DBT versus DM on successive screening rounds. Materials and Methods In this retrospective cohort study, data from 58 breast imaging facilities in the Breast Cancer Surveillance Consortium were collected. Analysis included women aged 40-79 years undergoing DBT or DM screening from 2011 to 2020. Absolute differences in screening outcomes by modality and screening round were estimated during the study period by using generalized estimating equations with marginal standardization to adjust for differences in women's risk characteristics across modality and round. Results A total of 523 485 DBT examinations (mean age of women, 58.7 years ± 9.7 [SD]) and 1 008 123 DM examinations (mean age, 58.4 years ± 9.8) among 504 863 women were evaluated. DBT and DM recall rates decreased with successive screening round, but absolute recall rates in each round were significantly lower with DBT versus DM (round 1 difference, -3.3% [95% CI: -4.6, -2.1] [P < .001]; round 2 difference, -1.8% [95% CI: -2.9, -0.7] [P = .003]; round 3 or above difference, -1.2% [95% CI: -2.4, -0.1] [P = .03]). DBT had significantly higher cancer detection (difference, 0.6 per 1000 examinations [95% CI: 0.2, 1.1]; P = .009) compared with DM only for round 3 and above. There were no significant differences in interval cancer rate (round 1 difference, 0.00 per 1000 examinations [95% CI: -0.24, 0.30] [P = .96]; round 2 or above difference, 0.04 [95% CI: -0.19, 0.31] [P = .76]) or total advanced cancer rate (round 1 difference, 0.00 per 1000 examinations [95% CI: -0.15, 0.19] [P = .94]; round 2 or above difference, -0.06 [95% CI: -0.18, 0.11] [P = .43]). Conclusion DBT had lower recall rates and could help detect more cancers than DM across three screening rounds, with no difference in interval or advanced cancer rates. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Skaane in this issue.
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Affiliation(s)
- Brian L. Sprague
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Rebecca Yates Coley
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Kathryn P. Lowry
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Karla Kerlikowske
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Louise M. Henderson
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Yu-Ru Su
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Christoph I. Lee
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Tracy Onega
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Erin J. A. Bowles
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Sally D. Herschorn
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Roberta M. diFlorio-Alexander
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Diana L. Miglioretti
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
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14
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Geertse TD, van der Waal D, Vreuls W, Tetteroo E, Duijm LEM, Pijnappel RM, Broeders MJM. The dilemma of recalling well-circumscribed masses in a screening population: A narrative literature review and exploration of Dutch screening practice. Breast 2023:S0960-9776(23)00451-4. [PMID: 37169601 DOI: 10.1016/j.breast.2023.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 05/13/2023] Open
Abstract
BACKGROUND In Dutch breast cancer screening, solitary, new or growing well-circumscribed masses should be recalled for further assessment. This results in cancers detected but also in false positive recalls, especially at initial screening. The aim of this study was to determine characteristics of well-circumscribed masses at mammography and identify potential methods to improve the recall strategy. METHODS A systematic literature search was performed using PubMed. In addition, follow-up data were retrieved on all 8860 recalled women in a Dutch screening region from 2014 to 2019. RESULTS Based on 15 articles identified in the literature search, we found that probably benign well-circumscribed masses that were kept under surveillance had a positive predictive value (PPV) of 0-2%. New or enlarging solitary well-circumscribed masses had a PPV of 10-12%. In general the detected carcinomas had a favorable prognosis. In our exploration of screening practice, 25% of recalls (2133/8860) were triggered by a well-circumscribed mass. Those recalls had a PPV of 2.0% for initial and 10.6% for subsequent screening. Most detected carcinomas had a favorable prognosis as well. CONCLUSION To recognize malignancies presenting as well-circumscribed masses, identifying solitary, new or growing lesions is key. This information is missing at initial screening since prior examinations are not available, leading to a low PPV. Access to prior clinical examinations may therefore improve this PPV. In addition, given the generally favorable prognosis of screen-detected malignant well-circumscribed masses, one may opt to recall these lesions at subsequent screening, if grown, rather than at initial screening.
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Affiliation(s)
- Tanya D Geertse
- Dutch Expert Centre for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, the Netherlands.
| | - Daniëlle van der Waal
- Dutch Expert Centre for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, the Netherlands
| | - Willem Vreuls
- Canisius Wilhelmina Hospital, Department of Radiology Weg Door, Jonkerbos 100, 6532 SZ, Nijmegen, the Netherlands
| | - Eric Tetteroo
- Amphia Hospital, Department of Radiology Molengracht 21, 4818 CK, Breda, the Netherlands
| | - Lucien E M Duijm
- Canisius Wilhelmina Hospital, Department of Radiology Weg Door, Jonkerbos 100, 6532 SZ, Nijmegen, the Netherlands
| | - Ruud M Pijnappel
- Dutch Expert Centre for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, the Netherlands; University Medical Centre Utrecht, Utrecht UniversityDepartment of Radiology, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands
| | - Mireille J M Broeders
- Dutch Expert Centre for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, the Netherlands; Radboud University Medical CenterDepartment for Health Evidence Geert Grooteplein 21, 6525 EZ, Nijmegen, the Netherlands
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15
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Seiler SJ, Neuschler EI, Butler RS, Lavin PT, Dogan BE. Optoacoustic Imaging With Decision Support for Differentiation of Benign and Malignant Breast Masses: A 15-Reader Retrospective Study. AJR Am J Roentgenol 2023; 220:646-658. [PMID: 36475811 DOI: 10.2214/ajr.22.28470] [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: 12/12/2022]
Abstract
BACKGROUND. Overlap in ultrasound features of benign and malignant breast masses yields high rates of false-positive interpretations and benign biopsy results. Optoacoustic imaging is an ultrasound-based functional imaging technique that can increase specificity. OBJECTIVE. The purpose of this study was to compare specificity at fixed sensitivity of ultrasound images alone and of fused ultrasound and optoacoustic images evaluated with machine learning-based decision support tool (DST) assistance. METHODS. This retrospective Reader-02 study included 480 patients (mean age, 49.9 years) with 480 breast masses (180 malignant, 300 benign) that had been classified as BI-RADS category 3-5 on the basis of conventional gray-scale ultrasound findings. The patients were selected by stratified random sampling from the earlier prospective 16-site Pioneer-01 study. For that study, masses were further evaluated by ultrasound alone followed by fused ultrasound and optoacoustic imaging between December 2012 and September 2015. For the current study, 15 readers independently reviewed the previously acquired images after training in optoacoustic imaging interpretation. Readers first assigned probability of malignancy (POM) on the basis of clinical history, mammographic findings, and conventional ultrasound findings. Readers then evaluated fused ultrasound and optoacoustic images, assigned scores for ultrasound and optoacoustic imaging features, and viewed a POM prediction score derived by a machine learning-based DST before issuing final POM. Individual and mean specificities at fixed sensitivity of 98% and partial AUC (pAUC) (95-100% sensitivity) were calculated. RESULTS. Averaged across all readers, specificity at fixed sensitivity of 98% was significantly higher for fused ultrasound and optoacoustic imaging with DST assistance than for ultrasound alone (47.2% vs 38.2%; p = .03). Across all readers, pAUC was higher (p < .001) for fused ultrasound and optoacoustic imaging with DST assistance (0.024 [95% CI, 0.023-0.026]) than for ultrasound alone (0.021 [95% CI, 0.019-0.022]). Better performance using fused ultrasound and optoacoustic imaging with DST assistance than using ultrasound alone was observed for 14 of 15 readers for specificity at fixed sensitivity and for 15 of 15 readers for pAUC. CONCLUSION. Fused ultrasound and optoacoustic imaging with DST assistance had significantly higher specificity at fixed sensitivity than did conventional ultrasound alone. CLINICAL IMPACT. Optoacoustic imaging, integrated with reader training and DST assistance, may help reduce the frequency of biopsy of benign breast masses.
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Affiliation(s)
- Stephen J Seiler
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-8585
| | - Erin I Neuschler
- Department of Radiology, University of Illinois College of Medicine, Chicago, IL
| | - Reni S Butler
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Philip T Lavin
- Boston Biostatistics Research Foundation, Framingham, MA
| | - Basak E Dogan
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-8585
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16
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Sherman ME, Vierkant RA, Masters M, Radisky DC, Winham SJ, Degnim AC, Vachon CM, Patel AV, Teras LR. Benign Breast Disease, NSAIDs, and Postmenopausal Breast Cancer Risk in the CPS-II Cohort. Cancer Prev Res (Phila) 2023; 16:175-184. [PMID: 36596665 PMCID: PMC10043807 DOI: 10.1158/1940-6207.capr-22-0403] [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: 09/06/2022] [Revised: 11/22/2022] [Accepted: 12/28/2022] [Indexed: 01/05/2023]
Abstract
ABSTRACT Nonsteroidal anti-inflammatory agents (NSAID) are associated with modest inconsistent reductions in breast cancer risk in population-based cohorts, whereas two focused studies of patients with benign breast disease (BBD) have found lower risk with NSAID use. Given that BBD includes fibroinflammatory lesions linked to elevated breast cancer risk, we assessed whether NSAID use was associated with lower breast cancer risk among patients with BBD.Participants were postmenopausal women in the Cancer Prevention Study-II (CPS-II), a prospective study of cancer incidence and mortality, who completed follow-up surveys in 1997 with follow-up through June 30, 2015. History of BBD, NSAID use, and covariate data were updated biennially. This analysis included 23,615 patients with BBD and 36,751 patients with non-BBD, including 3,896 incident breast cancers over an average of 12.72 years of follow-up among participants. NSAID use, overall and by formulation, recency, duration, and pills per month was analyzed versus breast cancer risk overall and by BBD status using multivariable-adjusted Cox models; BBD status and NSAID use were modeled as time-dependent exposures.Patients with BBD who reported using NSAIDs experienced lower breast cancer risk (HR, 0.87; 95% CI, 0.78-0.97), with similar effects for estrogen receptor (ER)-positive breast cancers [HR, 0.85; 95% confidence interval (CI), 0.74-0.97] and ER-negative breast cancers (HR, 0.87; 95% CI, 0.59-1.29); among women without BBD, NSAID use was unrelated to risk (HR, 1.02; 95% CI, 0.92-1.13; Pinteraction = 0.04). Associations stratified by age, obesity, menopausal hormone use, and cardiovascular disease were similar.Among patients with BBD, NSAID use appears linked to lower breast cancer risk. Further studies to assess the value of NSAID use among patients with BBD are warranted. PREVENTION RELEVANCE We examined whether NSAID use, a modifiable exposure, is associated with breast cancer risk in postmenopausal women from the Cancer Prevention Study-II with self-reported benign breast disease, an often inflammatory condition associated with higher rates of breast cancer.
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Affiliation(s)
- Mark E Sherman
- Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | | | - Matthew Masters
- Behavioral and Epidemiology Research Program, American Cancer Society, Atlanta, Georgia
| | - Derek C Radisky
- Department of Cancer Biology, Mayo Clinic, Jacksonville, Florida
| | - Stacey J Winham
- Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Amy C Degnim
- Department of Surgery, Mayo Clinic, Rochester, Minnesota
| | | | - Alpa V Patel
- Behavioral and Epidemiology Research Program, American Cancer Society, Atlanta, Georgia
| | - Lauren R Teras
- Behavioral and Epidemiology Research Program, American Cancer Society, Atlanta, Georgia
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17
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Ho TQH, Bissell MCS, Lee CI, Lee JM, Sprague BL, Tosteson ANA, Wernli KJ, Henderson LM, Kerlikowske K, Miglioretti DL. Prioritizing Screening Mammograms for Immediate Interpretation and Diagnostic Evaluation on the Basis of Risk for Recall. J Am Coll Radiol 2023; 20:299-310. [PMID: 36273501 PMCID: PMC10044471 DOI: 10.1016/j.jacr.2022.09.030] [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: 06/18/2022] [Revised: 09/08/2022] [Accepted: 09/19/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE The aim of this study was to develop a prioritization strategy for scheduling immediate screening mammographic interpretation and possible diagnostic evaluation. METHODS A population-based cohort with screening mammograms performed from 2012 to 2020 at 126 radiology facilities from 7 Breast Cancer Surveillance Consortium registries was identified. Classification trees identified combinations of clinical history (age, BI-RADS® density, time since prior mammogram, history of false-positive recall or biopsy result), screening modality (digital mammography, digital breast tomosynthesis), and facility characteristics (profit status, location, screening volume, practice type, academic affiliation) that grouped screening mammograms by recall rate, with ≥12/100 considered high and ≥16/100 very high. An efficiency ratio was estimated as the percentage of recalls divided by the percentage of mammograms. RESULTS The study cohort included 2,674,051 screening mammograms in 925,777 women, with 235,569 recalls. The most important predictor of recall was time since prior mammogram, followed by age, history of false-positive recall, breast density, history of benign biopsy, and screening modality. Recall rates were very high for baseline mammograms (21.3/100; 95% confidence interval, 19.7-23.0) and high for women with ≥5 years since prior mammogram (15.1/100; 95% confidence interval, 14.3-16.1). The 9.2% of mammograms in subgroups with very high and high recall rates accounted for 19.2% of recalls, an efficiency ratio of 2.1 compared with a random approach. Adding women <50 years of age with dense breasts accounted for 20.3% of mammograms and 33.9% of recalls (efficiency ratio = 1.7). Results including facility-level characteristics were similar. CONCLUSIONS Prioritizing women with baseline mammograms or ≥5 years since prior mammogram for immediate interpretation and possible diagnostic evaluation could considerably reduce the number of women needing to return for diagnostic imaging at another visit.
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Affiliation(s)
- Thao-Quyen H Ho
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, School of Medicine, Davis, California; Breast Imaging Unit, Diagnostic Imaging Center, Tam Anh General Hospital, Ho Chi Minh City, Vietnam; Department of Training and Scientific Research, University Medical Center, Ho Chi Minh City, Vietnam
| | - Michael C S Bissell
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, School of Medicine, Davis, California
| | - Christoph I Lee
- Breast Imaging, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Washington; Hutchinson Institute for Cancer Outcomes Research, Seattle, Washington; Northwest Screening and Cancer Outcomes Research Enterprise, University of Washington, Seattle, Washington; Deputy Editor, JACR
| | - Janie M Lee
- Breast Imaging, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Hutchinson Institute for Cancer Outcomes Research, Seattle, Washington; Breast Imaging, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Brian L Sprague
- Department of Surgery, Office of Health Promotion Research, Larner College of Medicine at the University of Vermont and Co-Leader, Cancer Control and Population Health Sciences Program, University of Vermont Cancer Center, Burlington, Vermont
| | - Anna N A Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth and Associate Director for Population Sciences, Dartmouth Cancer Center, Lebanon, New Hampshire
| | - Karen J Wernli
- Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington
| | - Louise M Henderson
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina; Cancer Epidemiology Program, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California; General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, San Francisco, California; Women's Health Comprehensive Clinic, and Director, Advanced Postdoctoral Fellowship in Women's Health, San Francisco Veterans Affairs Health Care System, San Francisco, California
| | - Diana L Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, School of Medicine, Davis, California; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington; Biostatistics and Population Sciences and Health Disparities Program, University of California, Davis, Comprehensive Cancer Center, Davis, California.
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18
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Hanafy MM, Ahmed AAH, Ali EA. Mammographically detected asymmetries in the era of artificial intelligence. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2023. [DOI: 10.1186/s43055-023-00979-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023] Open
Abstract
Abstract
Background
Proper assessment of mammographically detected asymmetries is essential to avoid unnecessary biopsies and missed cancers as they may be of a benign or malignant cause. According to ACR BIRADS atlas 2013, mammographically detected asymmetries are classified into asymmetry, focal asymmetry, global asymmetry, and developing asymmetry. We aimed to assess the diagnostic performance of artificial intelligence in mammographically detected asymmetries compared to breast ultrasound as well as combined mammography and ultrasound.
Results
This study was a prospective study that comprised 51 women with breast asymmetry found on screening as well as diagnostic mammography. All participants conducted full-field digital mammography and ultrasound. Then the obtained mammographic images were processed by the artificial intelligence software system. Mammography had a sensitivity of 100%, specificity of 73%, a positive predictive value of 56.52%, a negative predictive value of 100%, and diagnostic accuracy of 80%. The results of Ultrasound revealed a sensitivity of 100.00%, a specificity of 89.47%, a positive predictive value of 76.47%, a negative predictive value of 100.00%, and an accuracy of 92.16%. Combined mammography and breast ultrasound showed a sensitivity of 100.00%, a specificity of 86.84%, a positive predictive value of 72.22%, a negative predictive value of 100.00%, and an accuracy of 90.20%. Artificial intelligence results demonstrated a sensitivity of 84.62%, a specificity of 94.74%, a positive predictive value of 48.26%, a negative predictive value of 94.47%, and an accuracy of 92.16%.
Conclusions
Adding breast ultrasound in the assessment of mammographically detected asymmetries led to better characterization, so it reduced the false-positive results and improved the specificity. Also, Artificial intelligence showed better specificity compared to mammography, breast ultrasound, and combined Mammography and ultrasound, so AI can be used to decrease unnecessary biopsies as it increases confidence in diagnosis, especially in cases with no definite ultrasound suspicious abnormality.
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19
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Seki A, Tsunoda H, Takei J, Suzuki M, Kanomata N, Yamauchi H. Clinicopathological and imaging features of ductal carcinoma in situ in BRCA1/2 mutation carriers. Breast Dis 2023; 42:5-15. [PMID: 36806499 DOI: 10.3233/bd-220006] [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: 02/19/2023]
Abstract
BACKGROUND BRCA1/2-associated invasive breast cancer has been extensively studied. However, there are few reports of ductal carcinoma in situ (DCIS). OBJECTIVE This study aimed to investigate the clinicopathological and imaging findings of DCIS in patients with BRCA1/2 mutations. METHODS This was a single-institution, retrospective study. We identified patients diagnosed with DCIS with BRCA mutations between September 2003 and December 2020. Clinicopathological data and mammography (MG), magnetic resonance imaging (MRI), and ultrasound (US) findings were reviewed. RESULTS We identified 30 cancers in 28 patients; 7 (25.0%) patients had BRCA1 mutations, and 21 (75.0%) had BRCA2 mutations. The median patient age was 42 years. Screening was the most common reason for the detection of DCIS (50.0%), followed by occult cancer diagnosed by pathological examination after risk-reducing mastectomy (26.7%). The nuclear grade was most often 1 (46.7%), and 93.3% were estrogen and/or progesterone receptor positive. The detection rates of MG, MRI, and US were 64.3%, 72.0%, and 64.0%, respectively. The most common imaging findings were calcification (100%) on MG, non-mass enhancement (88.9%) on MRI, and hypoechoic area (75.0%) on US. CONCLUSION BRCA-associated DCIS was more strongly associated with BRCA2, and imaging features were similar to those of sporadic DCIS. Our results are helpful in informing surveillance strategies based on genotypes in women with BRCA mutations.
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Affiliation(s)
- Akina Seki
- Department of Breast Surgical Oncology, St. Luke's International Hospital, Tokyo, Japan
| | - Hiroko Tsunoda
- Department of Radiology, St. Luke's International Hospital, Tokyo, Japan
| | - Junko Takei
- Department of Breast Surgical Oncology, St. Luke's International Hospital, Tokyo, Japan
| | - Misato Suzuki
- Department of Clinical Genetics, St. Luke's International Hospital, Tokyo, Japan
| | - Naoki Kanomata
- Department of Pathology, St. Luke's International Hospital, Tokyo, Japan
| | - Hideko Yamauchi
- Department of Breast Surgical Oncology, St. Luke's International Hospital, Tokyo, Japan
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20
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Giess CS, Licaros AL, Kwait DC, Yeh ED, Lacson R, Khorasani R, Chikarmane SA. Live Mammographic Screening Interpretation Versus Offline Same-Day Screening Interpretation at a Tertiary Cancer Center. J Am Coll Radiol 2023; 20:207-214. [PMID: 36496088 DOI: 10.1016/j.jacr.2022.10.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/17/2022] [Accepted: 10/19/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVES The aim of this study was to compare screening mammography performance metrics for immediate (live) interpretation versus offline interpretation at a cancer center. METHODS An institutional review board-approved, retrospective comparison of screening mammography metrics at a cancer center for January 1, 2018, to December 31, 2019 (live period), and September 1, 2020, to March 31, 2022 (offline period), was performed. Before July 2020, screening examinations were interpreted while patients waited (live period), and diagnostic workup was performed concurrently. After the coronavirus disease 2019 shutdown from March to mid-June 2020, offline same-day interpretation was instituted. Patients with abnormal screening results returned for separate diagnostic evaluation. Screening metrics of positive predictive value 1 (PPV1), cancer detection rate (CDR), and abnormal interpretation rate (AIR) were compared for 17 radiologists who interpreted during both periods. Statistical significance was assessed using χ2 analysis. RESULTS In the live period, there were 7,105 screenings, 635 recalls, and 51 screen-detected cancers. In the offline period, there were 7,512 screenings, 586 recalls, and 47 screen-detected cancers. Comparison of live screening metrics versus offline metrics produced the following results: AIR, 8.9% (635 of 7,105) versus 7.8% (586 of 7,512) (P = .01); PPV1, 8.0% (51 of 635) versus 8.0% (47 of 586); and CDR, 7.2/1,000 versus 6.3/1,000 (P = .50). When grouped by >10% AIR or <10% AIR for the live period, the >10% AIR group showed a significant decrease in AIR for offline interpretation (from 12.7% to 9.7%, P < .001), whereas the <10% AIR group showed no significant change (from 7.4% to 6.7%, P = .17). CONCLUSIONS Conversion to offline screening interpretation from immediate interpretation at a cancer center was associated with lower AIR and similar CDR and PPV1. This effect was seen largely in radiologists with AIR > 10% in the live setting.
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Affiliation(s)
- Catherine S Giess
- Center for Evidence-Based Imaging, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts; Deputy Chair, Department of Radiology, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts.
| | - Andro L Licaros
- Center for Evidence-Based Imaging, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts; Department of Radiology, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts
| | - Dylan C Kwait
- Department of Radiology, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts; Interim Division Chief of Breast Imaging, Brigham and Women's Hospital, Boston, Massachusetts; Chief of Radiology, Brigham and Women's Faulkner Hospital, Boston, Massachusetts
| | - Eren D Yeh
- Department of Radiology, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts
| | - Ronilda Lacson
- Center for Evidence-Based Imaging, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts; Department of Radiology, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts
| | - Ramin Khorasani
- Center for Evidence-Based Imaging, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts; Department of Radiology, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts; Vice Chair, Quality/Safety and Patient Experience, Brigham and Women's Hospital, Mass General Brigham Health Care, Boston, Massachusetts
| | - Sona A Chikarmane
- Department of Radiology, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts
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21
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Mao X, He W, Humphreys K, Eriksson M, Holowko N, Strand F, Hall P, Czene K. Factors Associated With False-Positive Recalls in Mammography Screening. J Natl Compr Canc Netw 2023; 21:143-152.e4. [PMID: 36791753 DOI: 10.6004/jnccn.2022.7081] [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: 05/28/2022] [Accepted: 09/27/2022] [Indexed: 02/17/2023]
Abstract
BACKGROUND We aimed to identify factors associated with false-positive recalls in mammography screening compared with women who were not recalled and those who received true-positive recalls. METHODS We included 29,129 women, aged 40 to 74 years, who participated in the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) between 2011 and 2013 with follow-up until the end of 2017. Nonmammographic factors were collected from questionnaires, mammographic factors were generated from mammograms, and genotypes were determined using the OncoArray or an Illumina custom array. By the use of conditional and regular logistic regression models, we investigated the association between breast cancer risk factors and risk models and false-positive recalls. RESULTS Women with a history of benign breast disease, high breast density, masses, microcalcifications, high Tyrer-Cuzick 10-year risk scores, KARMA 2-year risk scores, and polygenic risk scores were more likely to have mammography recalls, including both false-positive and true-positive recalls. Further analyses restricted to women who were recalled found that women with a history of benign breast disease and dense breasts had a similar risk of having false-positive and true-positive recalls, whereas women with masses, microcalcifications, high Tyrer-Cuzick 10-year risk scores, KARMA 2-year risk scores, and polygenic risk scores were more likely to have true-positive recalls than false-positive recalls. CONCLUSIONS We found that risk factors associated with false-positive recalls were also likely, or even more likely, to be associated with true-positive recalls in mammography screening.
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Affiliation(s)
- Xinhe Mao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Wei He
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Chronic Disease Research Institute, the Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Natalie Holowko
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
| | - Fredrik Strand
- Department of Radiology, Karolinska University Hospital, Stockholm, Sweden.,Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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22
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Alsharif WM. The utilization of artificial intelligence applications to improve breast cancer detection and prognosis. Saudi Med J 2023; 44:119-127. [PMID: 36773967 PMCID: PMC9987701 DOI: 10.15537/smj.2023.44.2.20220611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2023] Open
Abstract
Breast imaging faces challenges with the current increase in medical imaging requests and lesions that breast screening programs can miss. Solutions to improve these challenges are being sought with the recent advancement and adoption of artificial intelligent (AI)-based applications to enhance workflow efficiency as well as patient-healthcare outcomes. rtificial intelligent tools have been proposed and used to analyze different modes of breast imaging, in most of the published studies, mainly for the detection and classification of breast lesions, breast lesion segmentation, breast density evaluation, and breast cancer risk assessment. This article reviews the background of the Conventional Computer-aided Detection system and AI, AI-based applications in breast medical imaging for the identification, segmentation, and categorization of lesions, breast density and cancer risk evaluation. In addition, the challenges, and limitations of AI-based applications in breast imaging are also discussed.
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Affiliation(s)
- Walaa M. Alsharif
- From the Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Al Madinah Al Munawwarah; and from the Society of Artificial Intelligence in Healthcare, Riyadh, Kingdom of Saudi Arabia.
- Address correspondence and reprint request to: Dr. Walaa M. Alsharif, Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Al Madinah Al Munawwarah, Kingdom of Saudi Arabia. E-mail: ORCID ID: https//:orcid.org/0000-0001-7607-3255
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23
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Zhang J, McGuinness JE, He X, Jones T, Silverman T, Guzman A, May BL, Kukafka R, Crew KD. Breast Cancer Risk and Screening Mammography Frequency Among Multiethnic Women. Am J Prev Med 2023; 64:51-60. [PMID: 36137818 DOI: 10.1016/j.amepre.2022.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 07/19/2022] [Accepted: 08/02/2022] [Indexed: 02/05/2023]
Abstract
INTRODUCTION In 2009, the U.S. Preventive Services Task Force updated recommended mammography screening frequency from annual to biennial for average-risk women aged 50-74 years. The association between estimated breast cancer risk and mammography screening frequency was evaluated. METHODS A single-center retrospective cohort study was conducted among racially/ethnically diverse women, aged 50-74 years, who underwent screening mammography from 2014 to 2018. Data on age, race/ethnicity, first-degree family history of breast cancer, previous benign breast biopsies, and mammographic density were extracted from the electronic health record to calculate Breast Cancer Surveillance Consortium 5-year risk of invasive breast cancer, with a 5-year risk ≥1.67% defined as high risk. Multivariable analyses were conducted to determine the association between breast cancer risk factors and mammography screening frequency (annual versus biennial). Data were analyzed from 2020 to 2022. RESULTS Among 12,929 women with a mean age of 61±6.9 years, 82.7% underwent annual screening mammography, and 30.7% met high-risk criteria for breast cancer. Hispanic women were more likely to screen annually than non-Hispanic Whites (85.0% vs 79.8%, respectively), despite fewer meeting high-risk criteria. In multivariable analyses adjusting for breast cancer risk factors, high- versus low/average-risk women (OR=1.17; 95% CI=1.04, 1.32) and Hispanic versus non-Hispanic White women (OR=1.46; 95% CI=1.29, 1.65) were more likely to undergo annual mammography. CONCLUSIONS A majority of women continue to undergo annual screening mammography despite only a minority meeting high-risk criteria, and Hispanic women were more likely to screen annually despite lower overall breast cancer risk. Future studies should focus on the implementation of risk-stratified breast cancer screening strategies.
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Affiliation(s)
- Jingwen Zhang
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York
| | - Julia E McGuinness
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York; Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York.
| | - Xin He
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
| | - Tarsha Jones
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, Florida
| | - Thomas Silverman
- Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York
| | - Ashlee Guzman
- Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York
| | - Benjamin L May
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York
| | - Rita Kukafka
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York; Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York; Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, New York, New York
| | - Katherine D Crew
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York; Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York; Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
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Muacevic A, Adler JR, Choudhari SG. Thermography as a Breast Cancer Screening Technique: A Review Article. Cureus 2022; 14:e31251. [PMID: 36505165 PMCID: PMC9731505 DOI: 10.7759/cureus.31251] [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: 08/30/2022] [Accepted: 11/08/2022] [Indexed: 11/10/2022] Open
Abstract
Globally, breast cancer is the most frequently occurring cancer in women and is the reason for more disability-adjusted life years lost than any other type of cancer. Hence, early screening plays a vital role in reducing breast cancer mortality. Although mammography is the standard procedure used for screening and diagnosis of breast cancer, it still has some limitations. Other methods used for screening include ultrasound and clinical breast examination. Despite its limitations, mammography is the gold standard for screening breast malignancy. Another emerging method for screening is thermography. With recent technological advances, breast cancer screening through thermography has demonstrated several advantages over existing modalities. For this review, a literature search was performed using databases such as PubMed, Google Scholar, and ScienceDirect. The keywords searched included breast cancer, early detection, breast cancer screening, mammography, and thermography. This review discusses the benefits of thermography showing that it can be a significant modality for breast cancer screening. The recent developments in thermal sensors, imaging protocols, and computer-aided software diagnostics hold great promise for making this technique a mainstream screening method for cancer. Moreover, the use of artificial intelligence and thermal imaging to detect early-stage breast cancer can provide impressive results. Therefore, thermography will be a promising technology for the early detection of breast cancer.
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25
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Homayoun H, Yee Chan W, Mohammadi A, Yusuf Kuzan T, Mirza-Aghazadeh-Attari M, Wai Ling L, Murzoglu Altintoprak K, Vijayananthan A, Rahmat K, Ab Mumin MRad N, Sam Leong S, Ejtehadifar S, Faeghi F, Abolghasemi J, Ciaccio EJ, Rajendra Acharya U, Abbasian Ardakani A. Artificial Intelligence, BI-RADS Evaluation and Morphometry: A Novel Combination to Diagnose Breast Cancer Using Ultrasonography, Results from Multi-Center Cohorts. Eur J Radiol 2022; 157:110591. [DOI: 10.1016/j.ejrad.2022.110591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 10/07/2022] [Accepted: 11/01/2022] [Indexed: 11/07/2022]
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26
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Shao SH, Allen B, Clement J, Chung G, Gao J, Hubbell E, Liu MC, Swanton C, Tang WHW, Yimer H, Tummala M. Multi-cancer early detection test sensitivity for cancers with and without current population-level screening options. TUMORI JOURNAL 2022:3008916221133136. [PMID: 36316952 DOI: 10.1177/03008916221133136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
There are four solid tumors with common screening options in the average-risk population aged 21 to 75 years (breast, cervical, colorectal, and, based on personalized risk assessment, prostate), but many cancers lack recommended population screening and are often detected at advanced stages when mortality is high. Blood-based multi-cancer early detection tests have the potential to improve cancer mortality through additional population screening. Reported here is a post-hoc analysis from the third Circulating Cell-free Genome Atlas substudy to examine multi-cancer early detection test performance in solid tumors with and without population screening recommendations and in hematologic malignancies. Participants with cancer in the third Circulating Cell-free Genome Atlas substudy analysis were split into three subgroups: solid screened tumors (breast, cervical, colorectal, prostate), solid unscreened tumors, and hematologic malignancies. In this post hoc analysis, sensitivity is reported for each subgroup across all ages and those aged ⩾50 years overall, by cancer, and by clinical cancer stage. Aggregate sensitivity in the solid screened, solid unscreened, and hematologic malignancy subgroups was 34%, 66%, and 55% across all cancer stages, respectively; restricting to participants aged ⩾50 years showed similar aggregate sensitivity. Aggregate sensitivity was 27%, 53%, and 60% across stages I to III, respectively. Within the solid unscreened subgroup, aggregate sensitivity was >75% in 8/18 cancers (44%) and >50% in 13/18 (72%). This multi-cancer early detection test detected cancer signals at high (>75%) sensitivity for multiple cancers without existing population screening recommendations, suggesting its potential to complement recommended screening programs. Clinical trial identifier: NCT02889978.
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Affiliation(s)
| | - Brian Allen
- GRAIL, LLC, a subsidiary of Illumina Inc., Menlo Park, CA, USA
| | | | - Gina Chung
- The Christ Hospital Health Network, Cincinnati, OH, USA
| | - Jingjing Gao
- GRAIL, LLC, a subsidiary of Illumina Inc., Menlo Park, CA, USA
| | - Earl Hubbell
- GRAIL, LLC, a subsidiary of Illumina Inc., Menlo Park, CA, USA
| | | | - Charles Swanton
- The Francis Crick Institute, London, UK
- University College London Cancer Institute, London, UK
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Breast Cancer Screening Modalities, Recommendations, and Novel Imaging Techniques. Surg Clin North Am 2022; 103:63-82. [DOI: 10.1016/j.suc.2022.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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28
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Patterns of Screening Recall Behavior Among Subspecialty Breast Radiologists. Acad Radiol 2022; 30:798-806. [PMID: 35803888 DOI: 10.1016/j.acra.2022.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/22/2022] [Accepted: 06/08/2022] [Indexed: 11/22/2022]
Abstract
RATIONALE AND OBJECTIVES Determine whether there are patterns of lesion recall among breast imaging subspecialists interpreting screening mammography, and if so, whether recall patterns correlate to morphologies of screen-detected cancers. MATERIALS AND METHODS This Institutional Review Board-approved, retrospective review included all screening examinations January 3, 2012-October 1, 2018 interpreted by fifteen breast imaging subspecialists at a large academic medical center and two outpatient imaging centers. Natural language processing identified radiologist recalls by lesion type (mass, calcifications, asymmetry, architectural distortion); proportions of callbacks by lesion types were calculated per radiologist. Hierarchical cluster analysis grouped radiologists based on recall patterns. Groups were compared to overall practice and each other by proportions of lesion types recalled, and overall and lesion-specific positive predictive value-1 (PPV1). RESULTS Among 161,859 screening mammograms with 13,086 (8.1%) recalls, Hierarchical cluster analysis grouped 15 radiologists into five groups. There was substantial variation in proportions of lesions recalled: calcifications 13%-18% (Chi-square 45.69, p < 0.00001); mass 16%-44% (Chi-square 498.42, p < 0.00001); asymmetry 13%-47% (Chi-square 660.93, p < 0.00001) architectural distortion 6%-20% (Chi-square 283.81, p < 0.00001). Radiologist groups differed significantly in overall PPV1 (range 5.6%-8.8%; Chi-square 17.065, p = 0.0019). PPV1 by lesion type varied among groups: calcifications 9.2%-15.4% (Chi-square 2.56, p = 0.6339); mass 5.6%-8.5% (Chi-square 1.31, p = 0.8597); asymmetry 3.4%-5.9% (Chi-square 2.225, p = 0.6945); architectural distortion 5.6%-10.8% (Chi-square 5.810, p = 0.2138). Proportions of recalled lesions did not consistently correlate to proportions of screen-detected cancer. CONCLUSION Breast imaging subspecialists have patterns for screening mammography recalls, suggesting differential weighting of imaging findings for perceived malignant potential. Radiologist recall patterns are not always predictive of screen-detected cancers nor lesion-specific PPV1s.
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Body Mass Index Is Inversely Associated with Risk of Postmenopausal Interval Breast Cancer: Results from the Women’s Health Initiative. Cancers (Basel) 2022; 14:cancers14133228. [PMID: 35804998 PMCID: PMC9264843 DOI: 10.3390/cancers14133228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 06/28/2022] [Accepted: 06/29/2022] [Indexed: 02/04/2023] Open
Abstract
Interval breast cancer refers to cancer diagnosed after a negative screening mammogram and before the next scheduled screening mammogram. Interval breast cancer has worse prognosis than screening-detected cancer. Body mass index (BMI) influences the accuracy of mammography and overall postmenopausal breast cancer risk, yet how is obesity associated with postmenopausal interval breast cancer incidence is unclear. The current study included cancer-free postmenopausal women aged 50–79 years at enrollment in the Women’s Health Initiative who were diagnosed with breast cancer during follow-up. Analyses include 324 interval breast cancer cases diagnosed within one year after the participant’s last negative screening mammogram and 1969 screening-detected breast cancer patients. Obesity (BMI ≥ 30 kg/m2) was measured at baseline. Associations between obesity and incidence of interval cancer were determined by sequential logistic regression analyses. In multivariable-adjusted models, obesity was inversely associated with interval breast cancer risk [OR (95% CI) = 0.65 (0.46, 0.92)]. The inverse association persisted after excluding women diagnosed within 2 years [OR (95% CI) = 0.60 (0.42, 0.87)] or 4 years [OR (95% CI) = 0.56 (0.37, 0.86)] of enrollment, suggesting consistency of the association regardless of screening practices prior to trial entry. These findings warrant confirmation in studies with body composition measures.
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Xie J, Zhang B, Ma J, Zeng D, Lo-Ciganic J. Readmission Prediction for Patients with Heterogeneous Medical History: A Trajectory-Based Deep Learning Approach. ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2022. [DOI: 10.1145/3468780] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Hospital readmission refers to the situation where a patient is re-hospitalized with the same primary diagnosis within a specific time interval after discharge. Hospital readmission causes $26 billion preventable expenses to the U.S. health systems annually and often indicates suboptimal patient care. To alleviate those severe financial and health consequences, it is crucial to proactively predict patients’ readmission risk. Such prediction is challenging because the evolution of patients’ medical history is dynamic and complex. The state-of-the-art studies apply statistical models which use static predictors in a period, failing to consider patients’ heterogeneous medical history. Our approach –
Trajectory-BAsed DEep Learning (TADEL)
– is motivated to tackle the deficiencies of the existing approaches by capturing dynamic medical history. We evaluate TADEL on a five-year national Medicare claims dataset including 3.6 million patients per year over all hospitals in the United States, reaching an F1 score of 87.3% and an AUC of 88.4%. Our approach significantly outperforms all the state-of-the-art methods. Our findings suggest that health status factors and insurance coverage are important predictors for readmission. This study contributes to IS literature and analytical methodology by formulating the trajectory-based readmission prediction problem and developing a novel deep-learning-based readmission risk prediction framework. From a health IT perspective, this research delivers implementable methods to assess patients’ readmission risk and take early interventions to avoid potential negative consequences.
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Affiliation(s)
- Jiaheng Xie
- Lerner College of Business & Economics, University of Delaware, Newark, DE, USA
| | - Bin Zhang
- Eller College of Management, University of Arizona, Tucson, AZ, USA
| | - Jian Ma
- University of Colorado, Colorado Springs, Colorado Springs CO, USA
| | - Daniel Zeng
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jenny Lo-Ciganic
- Department of Pharmaceutical Outcomes & Policy, University of Florida, FL
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Chen X, Zhang K, Abdoli N, Gilley PW, Wang X, Liu H, Zheng B, Qiu Y. Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms. Diagnostics (Basel) 2022; 12:diagnostics12071549. [PMID: 35885455 PMCID: PMC9320758 DOI: 10.3390/diagnostics12071549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/21/2022] [Accepted: 06/24/2022] [Indexed: 11/16/2022] Open
Abstract
Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operations, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying or mapping corresponding breast lesion features computed from unregistered multiple mammograms. This motivated us to leverage the architecture of Multi-view Vision Transformers to capture long-range relationships of multiple mammograms from the same patient in one examination. For this purpose, we employed local transformer blocks to separately learn patch relationships within four mammograms acquired from two-view (CC/MLO) of two-side (right/left) breasts. The outputs from different views and sides were concatenated and fed into global transformer blocks, to jointly learn patch relationships between four images representing two different views of the left and right breasts. To evaluate the proposed model, we retrospectively assembled a dataset involving 949 sets of mammograms, which included 470 malignant cases and 479 normal or benign cases. We trained and evaluated the model using a five-fold cross-validation method. Without any arduous preprocessing steps (e.g., optimal window cropping, chest wall or pectoral muscle removal, two-view image registration, etc.), our four-image (two-view-two-side) transformer-based model achieves case classification performance with an area under ROC curve (AUC = 0.818 ± 0.039), which significantly outperforms AUC = 0.784 ± 0.016 achieved by the state-of-the-art multi-view CNNs (p = 0.009). It also outperforms two one-view-two-side models that achieve AUC of 0.724 ± 0.013 (CC view) and 0.769 ± 0.036 (MLO view), respectively. The study demonstrates the potential of using transformers to develop high-performing computer-aided diagnosis schemes that combine four mammograms.
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Affiliation(s)
- Xuxin Chen
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (K.Z.); (N.A.); (P.W.G.); (H.L.); (B.Z.)
- Correspondence: (X.C.); (Y.Q.)
| | - Ke Zhang
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (K.Z.); (N.A.); (P.W.G.); (H.L.); (B.Z.)
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Neman Abdoli
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (K.Z.); (N.A.); (P.W.G.); (H.L.); (B.Z.)
| | - Patrik W. Gilley
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (K.Z.); (N.A.); (P.W.G.); (H.L.); (B.Z.)
| | | | - Hong Liu
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (K.Z.); (N.A.); (P.W.G.); (H.L.); (B.Z.)
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (K.Z.); (N.A.); (P.W.G.); (H.L.); (B.Z.)
| | - Yuchen Qiu
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (K.Z.); (N.A.); (P.W.G.); (H.L.); (B.Z.)
- Correspondence: (X.C.); (Y.Q.)
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32
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Leong YS, Hasikin K, Lai KW, Mohd Zain N, Azizan MM. Microcalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosis. Front Public Health 2022; 10:875305. [PMID: 35570962 PMCID: PMC9096221 DOI: 10.3389/fpubh.2022.875305] [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: 02/14/2022] [Accepted: 04/04/2022] [Indexed: 11/30/2022] Open
Abstract
Breast cancer is among the most common types of cancer in women and under the cases of misdiagnosed, or delayed in treatment, the mortality risk is high. The existence of breast microcalcifications is common in breast cancer patients and they are an effective indicator for early sign of breast cancer. However, microcalcifications are often missed and wrongly classified during screening due to their small sizes and indirect scattering in mammogram images. Motivated by this issue, this project proposes an adaptive transfer learning deep convolutional neural network in segmenting breast mammogram images with calcifications cases for early breast cancer diagnosis and intervention. Mammogram images of breast microcalcifications are utilized to train several deep neural network models and their performance is compared. Image filtering of the region of interest images was conducted to remove possible artifacts and noises to enhance the quality of the images before the training. Different hyperparameters such as epoch, batch size, etc were tuned to obtain the best possible result. In addition, the performance of the proposed fine-tuned hyperparameter of ResNet50 is compared with another state-of-the-art machine learning network such as ResNet34, VGG16, and AlexNet. Confusion matrices were utilized for comparison. The result from this study shows that the proposed ResNet50 achieves the highest accuracy with a value of 97.58%, followed by ResNet34 of 97.35%, VGG16 96.97%, and finally AlexNet of 83.06%.
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Affiliation(s)
- Yew Sum Leong
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.,Department of Biomedical Engineering, Center for Image and Signal Processing (CISIP), Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Norita Mohd Zain
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Muhammad Mokhzaini Azizan
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai, Malaysia
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Sivasubramanian M, Lo LW. Assessment of Nanoparticle-Mediated Tumor Oxygen Modulation by Photoacoustic Imaging. BIOSENSORS 2022; 12:bios12050336. [PMID: 35624636 PMCID: PMC9138624 DOI: 10.3390/bios12050336] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/09/2022] [Accepted: 05/11/2022] [Indexed: 06/01/2023]
Abstract
Photoacoustic imaging (PAI) is an invaluable tool in biomedical imaging, as it provides anatomical and functional information in real time. Its ability to image at clinically relevant depths with high spatial resolution using endogenous tissues as contrast agents constitutes its major advantage. One of the most important applications of PAI is to quantify tissue oxygen saturation by measuring the differential absorption characteristics of oxy and deoxy Hb. Consequently, PAI can be utilized to monitor tumor-related hypoxia, which is a crucial factor in tumor microenvironments that has a strong influence on tumor invasiveness. Reactive oxygen species (ROS)-based therapies, such as photodynamic therapy, radiotherapy, and sonodynamic therapy, are oxygen-consuming, and tumor hypoxia is detrimental to their efficacy. Therefore, a persistent demand exists for agents that can supply oxygen to tumors for better ROS-based therapeutic outcomes. Among the various strategies, NP-mediated supplemental tumor oxygenation is especially encouraging due to its physio-chemical, tumor targeting, and theranostic properties. Here, we focus on NP-based tumor oxygenation, which includes NP as oxygen carriers and oxygen-generating strategies to alleviate hypoxia monitored by PAI. The information obtained from quantitative tumor oxygenation by PAI not only supports optimal therapeutic design but also serves as a highly effective tool to predict therapeutic outcomes.
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34
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Hogan D, Yao HHI, Kanagarajah A, Ogluszko C, Tran PVP, Dundee P, O’Connell HE. Can multi-parametric magnetic resonance imaging and prostate-specific antigen density accurately stratify patients prior to prostate biopsy? JOURNAL OF CLINICAL UROLOGY 2022. [DOI: 10.1177/20514158221084820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Objective: This study examines the diagnostic accuracy of multi-parametric magnetic resonance imaging (mpMRI) in a high-volume centre to potentially stratify patients prior to prostate biopsy. Methods: All biopsy naïve patients who had mpMRI prostate and transperineal biopsy of prostate (TPBx) in 2017 and 2018 were included. There were no exclusion criteria. All patients, regardless of the mpMRI result, underwent systematic template biopsy under general anaesthesia with cognitive target biopsy if indicated. Clinicopathological data were extracted from medical records. The primary outcome was the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of mpMRI prostate in the detection of prostate cancer (PCa) compared with template TPBx. Results: In total, 140 patients were included. Overall, 57.1% had a positive biopsy. A higher Prostate Imaging-Reporting and Data Systems (PI-RADS) score was associated with a higher risk of diagnosing clinically significant PCa (International Society of Urological Pathology (ISUP) ⩾ 2) ( p < 0.001). The sensitivity, specificity, NPV, and PPV of mpMRI in detecting clinically significant PCa with a PI-RADS ⩾ 3 lesion, was 95% (95% confidence interval (CI) 83.0–99.3%), 41% (95% CI 31.3–51.3%), 95.3% (95% CI 84.2–99.4%) and 39.2% (95% CI 29.4–49.6%), respectively. Combining this with prostate-specific antigen density (PSAD) of <0.15 further improved the NPV to 100% (86.3–100). Binomial logistic regression to understand the effects of PSA, DRE and PI-RADS score on predicting clinically significant PCa (ISUP ⩾ 2) found increasing PSA (odds ratio (OR) 1.06, (95% CI 1.00–1.11, p = 0.022)) and PI-RADS (OR 3.17, (95% CI 1.94–5.18, p < 0.001)) to be significant predictors. Malignant DRE was not a significant predictor ( p = 0.087). Conclusion: This study demonstrates that the high sensitivity and NPV of mpMRI combined with PSAD may play a pivotal role in stratifying men for prostate biopsy and help avoid biopsy and its associated morbidity in select patients. Level of Evidence: 2b (Oxford Centre for Evidence-Based Medicine: Levels of Evidence)
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Affiliation(s)
- Donnacha Hogan
- Department of Urology, Western Health, Australia
- University College Cork, Ireland
| | | | | | | | | | - Phil Dundee
- Department of Urology, Western Health, Australia
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35
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Prevalence and correlates of false-positive results after 3-D screening mammography among uninsured women in a community outreach program. Prev Med Rep 2022; 27:101790. [PMID: 35656225 PMCID: PMC9152806 DOI: 10.1016/j.pmedr.2022.101790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 03/29/2022] [Accepted: 04/02/2022] [Indexed: 11/23/2022] Open
Abstract
False-positive results have been rarely investigated among uninsured minority women who undergo 3-D screening mammography. Here, we analyzed data from 21,022 women participating in the Breast Screening and Patient Navigation (BSPAN) program of North Texas with an aim to report prevalence and correlates of false-positive results after 3-D screening mammography, stratified by age. False-positives were defined as a negative diagnostic mammogram or a negative biopsy within 1 year of a positive screen. We used multivariable logistic regression to assess associations of demographic and clinical covariates and false positive results for age groups 40–49 and 50–64 years. Prevalence of false-positive results was 11.8% and 9.6% in the 40–49 and 50–64 age groups, respectively. Multivariable logistic regression demonstrated that, in the 40–49 age group, women who were non-menopausal, did not use hormone replacement therapy (HRT), and had self-reported prior mammograms had higher odds of false-positive results than those who were menopausal, used HRT and had no self-reported prior mammograms, respectively. In the 50–64 age group, women with a prior self-reported diagnostic mammogram had higher odds of false-positive results than those without a prior self-reported diagnostic mammogram. This study establishes contemporary evidence regarding prevalence and correlates of false-positive results after 3-D mammography in the unique BSPAN population, and demonstrate that use of 3-D mammography is not enough to reduce false-positive rates among uninsured women served through community outreach programs. Further research is needed to explore improved techniques to reduce false-positive rates, and ensure optimal use of scarce resources in outreach programs.
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36
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Marcondes FO, Armstrong K. Reducing the Burden of Overdiagnosis in Breast Cancer Screening and Beyond. Ann Intern Med 2022; 175:598-599. [PMID: 35226534 DOI: 10.7326/m22-0483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Affiliation(s)
- Felippe O Marcondes
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Katrina Armstrong
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
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Ho TQH, Bissell MCS, Kerlikowske K, Hubbard RA, Sprague BL, Lee CI, Tice JA, Tosteson ANA, Miglioretti DL. Cumulative Probability of False-Positive Results After 10 Years of Screening With Digital Breast Tomosynthesis vs Digital Mammography. JAMA Netw Open 2022; 5:e222440. [PMID: 35333365 PMCID: PMC8956976 DOI: 10.1001/jamanetworkopen.2022.2440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 12/23/2021] [Indexed: 11/25/2022] Open
Abstract
Importance Breast cancer screening with digital breast tomosynthesis may decrease false-positive results compared with digital mammography. Objective To estimate the probability of receiving at least 1 false-positive result after 10 years of screening with digital breast tomosynthesis vs digital mammography in the US. Design, Setting, and Participants An observational comparative effectiveness study with data collected prospectively for screening examinations was performed between January 1, 2005, and December 31, 2018, at 126 radiology facilities in the Breast Cancer Surveillance Consortium. Analysis included 903 495 individuals aged 40 to 79 years. Data analysis was conducted from February 9 to September 7, 2021. Exposures Screening modality, screening interval, age, and Breast Imaging Reporting and Data System breast density. Main Outcomes and Measures Cumulative risk of at least 1 false-positive recall for further imaging, short-interval follow-up recommendation, and biopsy recommendation after 10 years of annual or biennial screening with digital breast tomosynthesis vs digital mammography, accounting for competing risks of breast cancer diagnosis and death. Results In this study of 903 495 women, 2 969 055 nonbaseline screening examinations were performed with interpretation by 699 radiologists. Mean (SD) age of the women at the time of the screening examinations was 57.6 (9.9) years, and 58% of the examinations were in individuals younger than 60 years and 46% were performed in women with dense breasts. A total of 15% of examinations used tomosynthesis. For annual screening, the 10-year cumulative probability of at least 1 false-positive result was significantly lower with tomosynthesis vs digital mammography for all outcomes: 49.6% vs 56.3% (difference, -6.7; 95% CI, -7.4 to -6.1) for recall, 16.6% vs 17.8% (difference, -1.1; 95% CI, -1.7 to -0.6) for short-interval follow-up recommendation, and 11.2% vs 11.7% (difference, -0.5; 95% CI, -1.0 to -0.1) for biopsy recommendation. For biennial screening, the cumulative probability of a false-positive recall was significantly lower for tomosynthesis vs digital mammography (35.7% vs 38.1%; difference, -2.4; 95% CI, -3.4 to -1.5), but cumulative probabilities did not differ significantly by modality for short-interval follow-up recommendation (10.3% vs 10.5%; difference, -0.1; 95% CI, -0.7 to 0.5) or biopsy recommendation (6.6% vs 6.7%; difference, -0.1; 95% CI, -0.5 to 0.4). Decreases in cumulative probabilities of false-positive results with tomosynthesis vs digital mammography were largest for annual screening in women with nondense breasts (differences for recall, -6.5 to -12.8; short-interval follow-up, 0.1 to -5.2; and biopsy recommendation, -0.5 to -3.1). Regardless of modality, cumulative probabilities of false-positive results were substantially lower for biennial vs annual screening (overall recall, 35.7 to 38.1 vs 49.6 to 56.3; short-interval follow-up, 10.3 to 10.5 vs 16.6 to 17.8; and biopsy recommendation, 6.6 to 6.7 vs 11.2 to 11.7); older vs younger age groups (eg, among annual screening in women ages 70-79 vs 40-49, recall, 39.8 to 47.0 vs 60.8 to 68.0; short-interval follow-up, 13.3 to 14.2 vs 20.7 to 20.9; and biopsy recommendation, 9.1 to 9.3 vs 13.2 to 13.4); and women with entirely fatty vs extremely dense breasts (eg, among annual screening in women aged 50-59 years, recall, 29.1 to 36.3 vs 58.8 to 60.4; short-interval follow-up, 8.9 to 11.6 vs 19.5 to 19.8; and biopsy recommendation, 4.9 to 8.0 vs 15.1 to 15.3). Conclusions and Relevance In this comparative effectiveness study, 10-year cumulative probabilities of false-positive results were lower on digital breast tomosynthesis vs digital mammography. Biennial screening interval, older age, and nondense breasts were associated with larger reductions in false-positive probabilities than screening modality.
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Affiliation(s)
- Thao-Quyen H. Ho
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis
- Department of Training and Scientific Research, University Medical Center, Ho Chi Minh City, Vietnam
| | - Michael C. S. Bissell
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis
| | - Karla Kerlikowske
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco
- Department of Medicine, University of California, San Francisco
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Rebecca A. Hubbard
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Brian L. Sprague
- Department of Surgery, Office of Health Promotion Research, Larner College of Medicine at the University of Vermont and University of Vermont Cancer Center, Burlington, Vermont
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine, Seattle
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle
- Hutchinson Institute for Cancer Outcomes Research, Seattle, Washington
| | - Jeffrey A. Tice
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco
| | - Anna N. A. Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth College, Lebanon, New Hampshire
- Department of Medicine, Geisel School of Medicine at Dartmouth, Hanover, Lebanon, New Hampshire
- Department of Oncology, Norris Cotton Cancer Center, Lebanon, New Hampshire
| | - Diana L. Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
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Advani S, Abraham L, Buist DS, Kerlikowske K, Miglioretti DL, Sprague BL, Henderson LM, Onega T, Schousboe JT, Demb J, Zhang D, Walter LC, Lee CI, Braithwaite D, O’Meara ES. Breast biopsy patterns and findings among older women undergoing screening mammography: The role of age and comorbidity. J Geriatr Oncol 2022; 13:161-169. [PMID: 34896059 PMCID: PMC9450010 DOI: 10.1016/j.jgo.2021.11.013] [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: 06/25/2021] [Revised: 10/06/2021] [Accepted: 11/29/2021] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Limited evidence exists on the impact of age and comorbidity on biopsy rates and findings among older women. MATERIALS AND METHODS We used data from 170,657 women ages 66-94 enrolled in the United States Breast Cancer Surveillance Consortium (BCSC). We estimated one-year rates of biopsy by type (any, fine-needle aspiration (FNA), core or surgical) and yield of the most invasive biopsy finding (benign, ductal carcinoma in situ (DCIS) and invasive breast cancer) by age and comorbidity. Statistical significance was assessed using Wald statistics comparing coefficients estimated from logistic regression models adjusted for age, comorbidity, BCSC registry, and interaction between age and comorbidity. RESULTS Of 524,860 screening mammograms, 9830 biopsies were performed following 7930 exams (1.5%) within one year, specifically 5589 core biopsies (1.1%), 3422 (0.7%) surgical biopsies and 819 FNAs (0.2%). Biopsy rates per 1000 screens decreased with age (66-74:15.7, 95%CI:14.8-16.8), 75-84:14.5(13.5-15.6), 85-94:13.2(11.3,15.4), ptrend < 0.001) and increased with Charlson Comorbidity Score (CCS = 0:14.4 (13.5-15.3), CCS = 1:16.6 (15.2-18.1), CCS ≥2:19.0 (16.9-21.5), ptrend < 0.001).Biopsy rates increased with CCS at ages 66-74 and 75-84 but not 85-94. Core and surgical biopsy rates increased with CCS at ages 66-74 only. For each biopsy type, the yield of invasive breast cancer increased with age irrespective of comorbidity. DISCUSSION Women aged 66-84 with significant comorbidity in a breast cancer screening population had higher breast biopsy rates and similar rates of invasive breast cancer diagnosis than their counterparts with lower comorbidity. A considerable proportion of these diagnoses may represent overdiagnoses, given the high competing risk of death from non-breast-cancer causes among older women.
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Affiliation(s)
- Shailesh Advani
- Department of Oncology, Georgetown University, Washington, DC
| | - Linn Abraham
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Diana S.M. Buist
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Karla Kerlikowske
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA,Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Diana L. Miglioretti
- Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, CA
| | - Brian L. Sprague
- Department of Surgery, University of Vermont Larner College of Medicine, Burlington, VT
| | | | - Tracy Onega
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH
| | | | - Joshua Demb
- Division of Gastroenterology, Department of Internal Medicine, School of Medicine, University of California, San Diego, La Jolla, CA
| | - Dongyu Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL
| | - Louise C. Walter
- Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine; Department of Health Services, University of Washington School of Public Health, Seattle, WA
| | - Dejana Braithwaite
- Department of Epidemiology, University of Florida, Gainesville, FL, United States of America; University of Florida Health Cancer Center, Gainesville, FL, United States of America; Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, United States of America.
| | - Ellen S. O’Meara
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
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Development and validation of a circulating microRNA panel for the early detection of breast cancer. Br J Cancer 2022; 126:472-481. [PMID: 35013577 PMCID: PMC8810862 DOI: 10.1038/s41416-021-01593-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 08/05/2021] [Accepted: 10/06/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Mammography is widely used for breast cancer screening but suffers from a high false-positive rate. Here, we perform the largest comprehensive, multi-center study to date involving diverse ethnic groups, for the identification of circulating miRNAs for breast cancer screening. METHODS This study had a discovery phase (n = 289) and two validation phases (n = 374 and n = 379). Quantitative PCR profiling of 324 miRNAs was performed on serum samples from breast cancer (all stages) and healthy subjects to identify miRNA biomarkers. Two-fold cross-validation was used for building and optimising breast cancer-associated miRNA panels. An optimal panel was validated in cohorts with Caucasian and Asian samples. Diagnostic ability was evaluated using area under the curve (AUC) analysis. RESULTS The study identified and validated 30 miRNAs dysregulated in breast cancer. An optimised eight-miRNA panel showed consistent performance in all cohorts and was successfully validated with AUC, accuracy, sensitivity, and specificity of 0.915, 82.3%, 72.2% and 91.5%, respectively. The prediction model detected breast cancer in both Caucasian and Asian populations with AUCs ranging from 0.880 to 0.973, including pre-malignant lesions (stage 0; AUC of 0.831) and early-stage (stages I-II) cancers (AUC of 0.916). CONCLUSIONS Our panel can potentially be used for breast cancer screening, in conjunction with mammography.
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Zhu J, Ma S, Chen R, Liu Z, Liu Z, Wei W. The psychological impact of esophageal cancer screening on anxiety and depression in China. Front Psychiatry 2022; 13:933678. [PMID: 36339848 PMCID: PMC9630588 DOI: 10.3389/fpsyt.2022.933678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 10/06/2022] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE The psychological impact of screening is unclear and has been ignored. This study aimed to evaluate the psychological impact of esophageal cancer (EC) screening on anxiety and depression in China. MATERIALS AND METHODS A multicenter, population-based study in five high-risk regions of EC was conducted from 2019 to 2020. Residents were recruited and underwent endoscopic screening and then were diagnosed with normal, esophagitis, low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN) and EC. Subjects who did not participate in the screening were referred to as the control group. We surveyed their anxiety and depression levels at baseline and after endoscopy and informed them of different pathological results to evaluate the psychological impact of the screening process. RESULTS A total of 2,337 subjects completed all surveys in the screening process (normal: 355, esophagitis: 1,713, LGIN: 213, HGIN: 43 and EC: 13), with 63 controls. The levels of anxiety and depression of screeners were significantly higher than those of controls (P < 0.001). The fluctuation of anxiety and depression showed a "V" pattern in the screening process. The prevalence of anxiety symptoms at baseline, after endoscopy and after knowing the pathological results was 5.6, 0.3, and 3.2%, respectively (P < 0.001), and the corresponding prevalence of depression was 3.6, 0.2, and 2.1%, respectively (P < 0.001). With the aggravation of pathological results, the levels of anxiety and depression increased significantly (P < 0.001), especially in patients informed of HGIN (16.3 and 9.3%) and EC (23.1 and 30.8%). CONCLUSION Participation in endoscopic screening may bring short-term adverse psychological effects, especially at baseline and knowing the pathological results. More attention should be given to participants waiting for endoscopic screening. The method of informing the screening results of HGIN and EC should be improved. Further precise screening is needed to concentrate on high-risk groups to reduce the psychological impact of screening.
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Affiliation(s)
- Juan Zhu
- National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, Hangzhou, China
| | - Shanrui Ma
- National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ru Chen
- National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhaorui Liu
- Institute of Mental Health, Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
| | - Zhengkui Liu
- Chinese Academy of Sciences, Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
| | - Wenqiang Wei
- National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Wang J, Greuter MJW, Vermeulen KM, Brokken FB, Dorrius MD, Lu W, de Bock GH. Cost-effectiveness of abbreviated-protocol MRI screening for women with mammographically dense breasts in a national breast cancer screening program. Breast 2021; 61:58-65. [PMID: 34915447 PMCID: PMC8683595 DOI: 10.1016/j.breast.2021.12.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/07/2021] [Accepted: 12/09/2021] [Indexed: 12/25/2022] Open
Abstract
Introduction Magnetic resonance imaging (MRI) has shown the potential to improve the screening effectiveness among women with dense breasts. The introduction of fast abbreviated protocols (AP) makes MRI more feasible to be used in a general population. We aimed to investigate the cost-effectiveness of AP-MRI in women with dense breasts (heterogeneously/extremely dense) in a population-based screening program. Methods A previously validated model (SiMRiSc) was applied, with parameters updated for women with dense breasts. Breast density was assumed to decrease with increased age. The base scenarios included six biennial AP-MRI strategies, with biennial mammography from age 50–74 as reference. Fourteen alternative scenarios were performed by varying screening interval (triennial and quadrennial) and by applying a combined strategy of mammography and AP-MRI. A 3% discount rate for both costs and life years gained (LYG) was applied. Model robustness was evaluated using univariate and probabilistic sensitivity analyses. Results The six biennial AP-MRI strategies ranged from 132 to 562 LYG per 10,000 women, where more frequent application of AP-MRI was related to higher LYG. The optimal strategy was biennial AP-MRI screening from age 50–65 for only women with extremely dense breasts, producing an incremental cost-effectiveness ratio of € 18,201/LYG. At a threshold of € 20,000/LYG, the probability that the optimal strategy was cost-effective was 79%. Conclusion Population-based biennial breast cancer screening with AP-MRI from age 50–65 for women with extremely dense breasts might be a cost-effective alternative to mammography, but is not an option for women with heterogeneously dense breasts. AP-MRI can be cost-effective for screening women with extremely dense breast. The more frequent the use of AP-MRI, the more life years will be gained. Biennial AP-MRI for women with extremely dense breast up to age 65 is optimal.
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Affiliation(s)
- Jing Wang
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands.
| | - Marcel J W Greuter
- University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, the Netherlands.
| | - Karin M Vermeulen
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands.
| | - Frank B Brokken
- University of Groningen, Department of Computing Science, Groningen, the Netherlands.
| | - Monique D Dorrius
- University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, the Netherlands.
| | - Wenli Lu
- Department of Epidemiology and Health Statistics, Tianjin Medical University, Tianjin, China.
| | - Geertruida H de Bock
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands.
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Abstract
This article gives a brief overview of the development of artificial intelligence in clinical breast imaging. For multiple decades, artificial intelligence (AI) methods have been developed and translated for breast imaging tasks such as detection, diagnosis, and assessing response to therapy. As imaging modalities arise to support breast cancer screening programs and diagnostic examinations, including full-field digital mammography, breast tomosynthesis, ultrasound, and MRI, AI techniques parallel the efforts with more complex algorithms, faster computers, and larger data sets. AI methods include human-engineered radiomics algorithms and deep learning methods. Examples of these AI-supported clinical tasks are given along with commentary on the future.
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Affiliation(s)
- Qiyuan Hu
- Committee on Medical Physics, Department of Radiology, The University of Chicago, 5841 S Maryland Avenue, MC2026, Chicago, IL 60637, USA
| | - Maryellen L Giger
- Committee on Medical Physics, Department of Radiology, The University of Chicago, 5841 S Maryland Avenue, MC2026, Chicago, IL 60637, USA.
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Deep Vision for Breast Cancer Classification and Segmentation. Cancers (Basel) 2021; 13:cancers13215384. [PMID: 34771547 PMCID: PMC8582536 DOI: 10.3390/cancers13215384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 10/18/2021] [Accepted: 10/24/2021] [Indexed: 11/30/2022] Open
Abstract
Simple Summary Breast cancer misdiagnoses increase individual and system stressors as well as costs and result in increased morbidity and mortality. Digital mammography studies are typically about 80% sensitive and 90% specific. Improvement in classification of breast cancer imagery is possible using deep vision methods, and these methods may be further used to identify autonomously regions of interest most closely associated with anomalies to support clinician analysis. This research explores deep vision techniques for improving mammography classification and for identifying associated regions of interest. The findings from this research contribute to the future of automated assistive diagnoses of breast cancer and the isolation of regions of interest. Abstract (1) Background: Female breast cancer diagnoses odds have increased from 11:1 in 1975 to 8:1 today. Mammography false positive rates (FPR) are associated with overdiagnoses and overtreatment, while false negative rates (FNR) increase morbidity and mortality. (2) Methods: Deep vision supervised learning classifies 299 × 299 pixel de-noised mammography images as negative or non-negative using models built on 55,890 pre-processed training images and applied to 15,364 unseen test images. A small image representation from the fitted training model is returned to evaluate the portion of the loss function gradient with respect to the image that maximizes the classification probability. This gradient is then re-mapped back to the original images, highlighting the areas of the original image that are most influential for classification (perhaps masses or boundary areas). (3) Results: initial classification results were 97% accurate, 99% specific, and 83% sensitive. Gradient techniques for unsupervised region of interest mapping identified areas most associated with the classification results clearly on positive mammograms and might be used to support clinician analysis. (4) Conclusions: deep vision techniques hold promise for addressing the overdiagnoses and treatment, underdiagnoses, and automated region of interest identification on mammography.
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Manassi M, Ghirardo C, Canas-Bajo T, Ren Z, Prinzmetal W, Whitney D. Serial dependence in the perceptual judgments of radiologists. COGNITIVE RESEARCH-PRINCIPLES AND IMPLICATIONS 2021; 6:65. [PMID: 34648124 PMCID: PMC8517058 DOI: 10.1186/s41235-021-00331-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 08/21/2021] [Indexed: 11/10/2022]
Abstract
In radiological screening, clinicians scan myriads of radiographs with the intent of recognizing and differentiating lesions. Even though they are trained experts, radiologists’ human search engines are not perfect: average daily error rates are estimated around 3–5%. A main underlying assumption in radiological screening is that visual search on a current radiograph occurs independently of previously seen radiographs. However, recent studies have shown that human perception is biased by previously seen stimuli; the bias in our visual system to misperceive current stimuli towards previous stimuli is called serial dependence. Here, we tested whether serial dependence impacts radiologists’ recognition of simulated lesions embedded in actual radiographs. We found that serial dependence affected radiologists’ recognition of simulated lesions; perception on an average trial was pulled 13% toward the 1-back stimulus. Simulated lesions were perceived as biased towards the those seen in the previous 1 or 2 radiographs. Similar results were found when testing lesion recognition in a group of untrained observers. Taken together, these results suggest that perceptual judgements of radiologists are affected by previous visual experience, and thus some of the diagnostic errors exhibited by radiologists may be caused by serial dependence from previously seen radiographs.
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Affiliation(s)
- Mauro Manassi
- School of Psychology, King's College, University of Aberdeen, Aberdeen, UK.
| | - Cristina Ghirardo
- Department of Psychology, University of California, Berkeley, CA, USA
| | - Teresa Canas-Bajo
- Department of Psychology, University of California, Berkeley, CA, USA.,Vision Science Group, University of California, Berkeley, CA, USA
| | - Zhihang Ren
- Department of Psychology, University of California, Berkeley, CA, USA.,Vision Science Group, University of California, Berkeley, CA, USA
| | | | - David Whitney
- Department of Psychology, University of California, Berkeley, CA, USA.,Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA.,Vision Science Group, University of California, Berkeley, CA, USA
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Akpan E, Kitundu J, Ekpo E. Public Health Radiography: A Scoping Review of Benefits, and Growth Opportunities for Radiographers. J Med Imaging Radiat Sci 2021; 52:615-625. [PMID: 34531164 DOI: 10.1016/j.jmir.2021.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 06/17/2021] [Accepted: 08/06/2021] [Indexed: 10/20/2022]
Abstract
INTRODUCTION There is growing adoption of radiographic techniques in public health to improve outcomes of chronic and communicable diseases. This review examines the applications, benefits, and implications of radiography in public health. It also examines the challenges and potential advanced practice roles for radiographers in public health radiography (PHR). METHODOLOGY Preferred Reporting Items for Systematic Reviews and Meta-Analyses - Scoping review extension (PRISMA- ScR) checklist was employed, and the search was conducted using PubMed, Medline, Web of Science, ScienceDirect, and Google Scholar to identify relevant articles that explored the concept of radiography in public health. Evidence was analysed using an inductive iterative approach. RESULTS Radiographic imaging modalities such as ultrasound, computed tomography, and plain X-ray had wide applicability in public health fields of preventive cardiology, preventive oncology, maternal health, infectious disease epidemiology, and radiographic informatics. PHR effectively reduced mortality, improved outcomes, informed lifestyle changes to mitigate the risk of impending disease. PHR also helped in monitoring disease progression and predicting treatment outcomes. However, evidence establishing a competency framework that supports PHR is scarce. CONCLUSION Radiography makes a significant contribution to public health in reducing mortality and morbidity. Therefore, developing a PHR competency framework can accentuate the contribution Radiographers make to solving public health issues.
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Affiliation(s)
- Eyo Akpan
- Grayscale International Ltd, Lagos, Nigeria.
| | - Jane Kitundu
- Vijibweni District Hospital, Kigamboni Municipal, Dar es Salaam, Tanzania
| | - Ernest Ekpo
- Image Optimisation and Perception Group, Discipline of Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Cumberland Campus C42
- 75 East Street, Lidcombe, NS, W
- 2141
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Raos D, Ulamec M, Katusic Bojanac A, Bulic-Jakus F, Jezek D, Sincic N. Epigenetically inactivated RASSF1A as a tumor biomarker. Bosn J Basic Med Sci 2021; 21:386-397. [PMID: 33175673 PMCID: PMC8292865 DOI: 10.17305/bjbms.2020.5219] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 11/11/2020] [Indexed: 12/18/2022] Open
Abstract
RASSF1A, one of the eight isoforms of the RASSF1 gene, is a tumor suppressor gene that influences tumor initiation and development. In cancer, RASSF1A is frequently inactivated by mutations, loss of heterozygosity, and, most commonly, by promoter hypermethylation. Epigenetic inactivation of RASSF1A was detected in various cancer types and led to significant interest; current research on RASSF1A promoter methylation focuses on its roles as an epigenetic tumor biomarker. Typically, researchers analyzed genomic DNA (gDNA) to measure the amount of RASSF1A promoter methylation. Cell-free DNA (cfDNA) from liquid biopsies is a recent development showing promise as an early cancer diagnostic tool using biomarkers, such as RASSF1A. This review discusses the evidence on aberrantly methylated RASSF1A in gDNA and cfDNA from different cancer types and its utility for early cancer diagnosis, prognosis, and surveillance. We compared methylation frequencies of RASSF1A in gDNA and cfDNA in various cancer types. The weaknesses and strengths of these analyses are discussed. In conclusion, although the importance of RASSSF1A methylation to cancer has been established and is included in several diagnostic panels, its diagnostic utility is still experimental.
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Affiliation(s)
- Dora Raos
- Department of Medical Biology, University of Zagreb School of Medicine, Zagreb, Croatia; Scientific Group for Research on Epigenetic Biomarkers, University of Zagreb School of Medicine, Zagreb, Croatia; Scientific Centre of Excellence for Reproductive and Regenerative Medicine, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Monika Ulamec
- Scientific Group for Research on Epigenetic Biomarkers, University of Zagreb School of Medicine, Zagreb, Croatia; Scientific Centre of Excellence for Reproductive and Regenerative Medicine, University of Zagreb School of Medicine, Zagreb, Croatia; Ljudevit Jurak Clinical Department of Pathology and Cytology, Sestre Milosrdnice University Hospital Center, Zagreb, Croatia; Department of Pathology, University of Zagreb School of Dental Medicine and School of Medicine, Zagreb, Croatia
| | - Ana Katusic Bojanac
- Department of Medical Biology, University of Zagreb School of Medicine, Zagreb, Croatia; Scientific Centre of Excellence for Reproductive and Regenerative Medicine, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Floriana Bulic-Jakus
- University of Zagreb School of Medicine, Department of Medical Biology, Zagreb, Croatia
| | - Davor Jezek
- Scientific Centre of Excellence for Reproductive and Regenerative Medicine, University of Zagreb School of Medicine, Zagreb, Croatia; Department of Histology and Embryology, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Nino Sincic
- Department of Medical Biology, University of Zagreb School of Medicine, Zagreb, Croatia; Scientific Group for Research on Epigenetic Biomarkers, University of Zagreb School of Medicine, Zagreb, Croatia; Scientific Centre of Excellence for Reproductive and Regenerative Medicine, University of Zagreb School of Medicine, Zagreb, Croatia
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Lin L, Koh WL, Huang Q, Lee JK. Breast Cancer Information Behaviours and Needs among Singapore Women: A Qualitative Study. Asian Pac J Cancer Prev 2021; 22:1767-1774. [PMID: 34181332 PMCID: PMC8418835 DOI: 10.31557/apjcp.2021.22.6.1767] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Indexed: 11/28/2022] Open
Abstract
Background: There is growing evidence on cancer communication and its impact on cancer-related health outcomes; however, little is known about how women gain access to and use breast cancer information in the multi-ethnic Asian context. This paper aimed to explore the breast cancer information acquisition behaviours and needs among Singapore women who attended a community-based health organisation for mammography screening. Methods, design and setting: Qualitative data were collected through semi-structured interviews with 37 racially diverse, aged 50 and above women, who have received mammography screening within the past two years. The interviews were conducted at either the Singapore Cancer Society Clinic or participant’s home. Results: Although cancer information scanning was more prevalent than information seeking (91.9% vs. 62.2%), those who purposively seek information exhibited a higher knowledge level of breast cancer. The most commonly cited sources for information scanning were friends, television and family, and for information seeking were the Internet, pamphlets from a healthcare organisation/ public authority, and healthcare providers. Singapore women were well-informed about the benefits of mammogram; however, specific knowledge, such as modifiable risk factors, reasons for different screening options and the trade-off between harm and benefit, was still lacking which led to confusion about screening. Conclusion: Breast cancer health educational materials should provide clear and balanced information to give women a more accurate or realistic expectation about mammography screening. Study findings provide important implications for breast cancer education and programs to move beyond simply raising awareness and craft specific informative messages addressing the needs of the target group.
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Affiliation(s)
- Lavinia Lin
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | | | | | - Jeong Kyu Lee
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
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Martin-Noguerol T, Luna A. External validation of AI algorithms in breast radiology: the last healthcare security checkpoint? Quant Imaging Med Surg 2021; 11:2888-2892. [PMID: 34079749 DOI: 10.21037/qims-20-1409] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
| | - Antonio Luna
- Radiology Department, HTmédica, Clinica Las Nieves, Jaén, Spain
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Batchu S, Liu F, Amireh A, Waller J, Umair M. A Review of Applications of Machine Learning in Mammography and Future Challenges. Oncology 2021; 99:483-490. [PMID: 34023831 DOI: 10.1159/000515698] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 03/05/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND The aim of this study is to systematically review the literature to summarize the evidence surrounding the clinical utility of artificial intelligence (AI) in the field of mammography. Databases from PubMed, IEEE Xplore, and Scopus were searched for relevant literature. Studies evaluating AI models in the context of prediction and diagnosis of breast malignancies that also reported conventional performance metrics were deemed suitable for inclusion. From 90 unique citations, 21 studies were considered suitable for our examination. Data was not pooled due to heterogeneity in study evaluation methods. SUMMARY Three studies showed the applicability of AI in reducing workload. Six studies demonstrated that AI can aid in diagnosis, with up to 69% reduction in false positives and an increase in sensitivity ranging from 84 to 91%. Five studies show how AI models can independently mark and classify suspicious findings on conventional scans, with abilities comparable with radiologists. Seven studies examined AI predictive potential for breast cancer and risk score calculation. Key Messages: Despite limitations in the current evidence base and technical obstacles, this review suggests AI has marked potential for extensive use in mammography. Additional works, including large-scale prospective studies, are warranted to elucidate the clinical utility of AI.
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Affiliation(s)
- Sai Batchu
- Cooper Medical School of Rowan University, Camden, New Jersey, USA
| | - Fan Liu
- Stanford University School of Medicine, Stanford, California, USA
| | - Ahmad Amireh
- Duke University Medical Center, Durham, North Carolina, USA
| | - Joseph Waller
- Drexel University College of Medicine, Philadelphia, Pennsylvania, USA
| | - Muhammad Umair
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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Can supplementary contrast-enhanced MRI of the breast avoid needle biopsies in suspicious microcalcifications seen on mammography? A systematic review and meta-analysis. Breast 2021; 56:53-60. [PMID: 33618160 PMCID: PMC7907894 DOI: 10.1016/j.breast.2021.02.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/05/2021] [Accepted: 02/06/2021] [Indexed: 11/23/2022] Open
Abstract
Purpose To analyze the rate of potentially avoidable needle biopsies in mammographically suspicious calcifications if supplementary Contrast-Enhanced MRI (CE-MRI) is negative. Methods Using predefined criteria, a systematic review was performed. Studies investigating the use of supplemental CE-MRI in the setting of mammographically suspicious calcifications undergoing stereotactic biopsy and published between 2000 and 2020 were eligible. Two reviewers extracted study characteristics and true positives (TP), false positives, true negatives and false negatives (FN). Specificity, in this setting equaling the number of avoidable biopsies and FN rates were calculated. The maximum pre-test probability at which post-test probabilities of a negative CE-MRI met with BI-RADS benchmarks was determined by a Fagan nomogram. Random-effects models, I2-statistics, Deek’s funnel plot testing and meta-regression were employed. P-values <0.05 were considered significant. Results Thirteen studies investigating 1414 lesions with a cancer prevalence of 43.6% (range: 22.7–66.9%) were included. No publication bias was found (P = 0.91). CE-MRI performed better in pure microcalcification studies compared to those also including associate findings (P < 0.001). In the first group, the pooled rate of avoidable biopsies was 80.6% (95%-CI: 64.6–90.5%) while the overall and invasive cancer FN rates were 3.7% (95%-CI: 1.2–6.2%) and 1.6% (95%-CI 0–3.6%), respectively. Up to a pre-test probability of 22%, the post-test probability did not exceed 2%. Conclusion A negative supplementary CE-MRI could potentially avoid 80.6% of unnecessary stereotactic biopsies in BI-RADS 4 microcalcifications at a cost of 3.7% missed breast cancers, 1.6% invasive. BI-RADS benchmarks for downgrading mammographic calcifications would be met up to a pretest probability of 22%. A negative breast MRI can downgrade up to 80.6% of suspicious microcalcifications, potentially avoiding vacuum-assisted breast biopsies. Up to a pretest probability of 22% , a negative breast MRI result would not exceed the 2% cancer rate required for a BI-RADS 3 category assignment.
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