Objectives: To analyse which mammographic and tumour characteristics led to concordant versus discordant recalls at blinded double reading to further optimise our breast cancer screening programme.
Methods: We included a consecutive series of 99,013 screening mammograms obtained between July 2013 and January 2015. All mammograms were double read in a blinded fashion. Discordant readings were routinely recalled without consensus or arbitration. During the 2-year follow-up, relevant data of the recalled women were collected. We compared mammographic characteristics, screening outcome and tumour characteristics between concordant and discordant recalls.
Results: There were 2,543 concordant recalls (71.4%) and 997 discordant recalls (28.0%). The positive predictive value of a concordant recall was significantly higher (23.5% vs. 10.0%, p < 0.001). The proportion of BI-RADS 0 was significantly higher in the discordant recall group (75.7% vs. 56.3%, p < 0.001). Discordant recalls were more often an asymmetry or architectural distortion (21.8% vs. 13.2% and 9.3% vs. 6.5%, respectively, p < 0.001). There were no differences in the distribution of DCIS and invasive cancers and tumour characteristics were comparable for the two groups, except for a more favourable tumour grade in the discordant recall group (54.7% vs. 39.9% grade I tumours, p = 0.022).
Conclusions: Screen-detected cancers detected by a discordant reading show a more favourable tumour grade than cancers diagnosed after a concordant recall. The higher proportion of asymmetries and architectural distortions in this group provide a possible target for improving screening programmes by additional training of screening radiologists and the implementation of digital breast tomosynthesis.
Key Points: • With blinded double reading of screening mammograms, screen-detected cancers detected by a discordant reading show a more favourable tumour grade than cancers diagnosed after a concordant recall. • The proportions of asymmetries and architectural distortions are higher in case of a discordant reading. • Possible improvement strategies could target additional training of screening radiologists and the implementation of digital breast tomosynthesis in breast cancer screening programmes.
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http://dx.doi.org/10.1007/s00330-018-5586-9 | DOI Listing |
Neurology
December 2024
From the Department of Neurology (A.L.C.S.); Department of Biostatistics, Epidemiology, and Informatics, (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiation Medicine and Applied Sciences (A.R.), University of California, San Diego; Memory Impairment and Neurodegenerative Dementia (MIND) Center (J.A.H., T.H.M., M.G.), University of Mississippi Medical Center, Jackson; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Biostatistics and Bioinformatics (L.W.), Duke University, Durham, NC; Department of Population Health (J.R.P., J.C.), New York University Grossman School of Medicine, New York; Department of Epidemiology (A.G.), Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD; National Institute on Aging Intramural Program (K.W.), Baltimore, MD; Department of Epidemiology (A.K.-N.), University of North Carolina at Chapel Hill; and National Institute of Neurologic Disorders and Stroke Intramural Research Program (R.F.G.), Bethesda, MD.
Background And Objectives: Race and ethnicity are proxy measures of sociocultural factors that influence cognitive test performance. Our objective was to compare different regression-based cognitive normative models adjusting for demographics and different combinations of easily accessible/commonly used social determinants of health (SDoH) factors, which may help describe cognitive performance variability historically captured by ethnoracial differences.
Methods: We performed cross-sectional analyses on data from Black and White participants without mild cognitive impairment/dementia in the Atherosclerosis Risk in Communities Study who attended visit 5 in 2011-2013.
Cortex
September 2024
King's College London, Institute of Psychiatry, Psychology, and Neuroscience, London, UK. Electronic address:
J Med Screen
August 2024
The Daffodil Centre, The University of Sydney, A Joint Venture With Cancer Council NSW, Sydney, NSW, Australia.
Artificial intelligence (AI) algorithms have been retrospectively evaluated as replacement for one radiologist in screening mammography double-reading; however, methods for resolving discordance between radiologists and AI in the absence of 'real-world' arbitration may underestimate cancer detection rate (CDR) and recall. In 108,970 consecutive screens from a population screening program (BreastScreen WA, Western Australia), 20,120 were radiologist/AI discordant without real-world arbitration. Recall probabilities were randomly assigned for these screens in 1000 simulations.
View Article and Find Full Text PDFJ Clin Pathol
July 2024
Department of Pathology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
Objective: The use of artificial intelligence has potential in assisting many aspects of imaging interpretation. We undertook a prospective service evaluation from March to October 2022 of Mammography Intelligent Assessment (MIA) operating "silently" within our Breast Screening Service, with a view to establishing its performance in the local population and setting. This evaluation addressed the performance of standalone MIA vs conventional double human reading of mammograms.
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