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      The Lancet Commission on diagnostics: transforming access to diagnostics

      , FRCPath a , , Prof, PhD b , * , , Prof, MD c , , Prof, FRCP d , , Prof, MD e , , MD f , , MMed g , , MD h , , BA i , , PhD j , , MD k , , MD l , , Prof, MD m , , MD n , , Prof, MD o , , Prof, PhD p , , Prof, MD q , , FRCR r , , ScD s , , PhD t , , Prof, PhD v , , Prof, FRCPath w , , Prof, MD x , , PhD y , , Prof, MD u , , MD z , , MD aa , , MD ab , , BSc b , , Prof, MD ac , , Prof, FRCR ad , , PhD ae
      Lancet (London, England)
      Elsevier Ltd.

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          The impact of the COVID-19 pandemic on cancer deaths due to delays in diagnosis in England, UK: a national, population-based, modelling study

          Summary Background Since a national lockdown was introduced across the UK in March, 2020, in response to the COVID-19 pandemic, cancer screening has been suspended, routine diagnostic work deferred, and only urgent symptomatic cases prioritised for diagnostic intervention. In this study, we estimated the impact of delays in diagnosis on cancer survival outcomes in four major tumour types. Methods In this national population-based modelling study, we used linked English National Health Service (NHS) cancer registration and hospital administrative datasets for patients aged 15–84 years, diagnosed with breast, colorectal, and oesophageal cancer between Jan 1, 2010, and Dec 31, 2010, with follow-up data until Dec 31, 2014, and diagnosed with lung cancer between Jan 1, 2012, and Dec 31, 2012, with follow-up data until Dec 31, 2015. We use a routes-to-diagnosis framework to estimate the impact of diagnostic delays over a 12-month period from the commencement of physical distancing measures, on March 16, 2020, up to 1, 3, and 5 years after diagnosis. To model the subsequent impact of diagnostic delays on survival, we reallocated patients who were on screening and routine referral pathways to urgent and emergency pathways that are associated with more advanced stage of disease at diagnosis. We considered three reallocation scenarios representing the best to worst case scenarios and reflect actual changes in the diagnostic pathway being seen in the NHS, as of March 16, 2020, and estimated the impact on net survival at 1, 3, and 5 years after diagnosis to calculate the additional deaths that can be attributed to cancer, and the total years of life lost (YLLs) compared with pre-pandemic data. Findings We collected data for 32 583 patients with breast cancer, 24 975 with colorectal cancer, 6744 with oesophageal cancer, and 29 305 with lung cancer. Across the three different scenarios, compared with pre-pandemic figures, we estimate a 7·9–9·6% increase in the number of deaths due to breast cancer up to year 5 after diagnosis, corresponding to between 281 (95% CI 266–295) and 344 (329–358) additional deaths. For colorectal cancer, we estimate 1445 (1392–1591) to 1563 (1534–1592) additional deaths, a 15·3–16·6% increase; for lung cancer, 1235 (1220–1254) to 1372 (1343–1401) additional deaths, a 4·8–5·3% increase; and for oesophageal cancer, 330 (324–335) to 342 (336–348) additional deaths, 5·8–6·0% increase up to 5 years after diagnosis. For these four tumour types, these data correspond with 3291–3621 additional deaths across the scenarios within 5 years. The total additional YLLs across these cancers is estimated to be 59 204–63 229 years. Interpretation Substantial increases in the number of avoidable cancer deaths in England are to be expected as a result of diagnostic delays due to the COVID-19 pandemic in the UK. Urgent policy interventions are necessary, particularly the need to manage the backlog within routine diagnostic services to mitigate the expected impact of the COVID-19 pandemic on patients with cancer. Funding UK Research and Innovation Economic and Social Research Council.
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            Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development.

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              Is Open Access

              A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis

              Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep learning algorithms versus health-care professionals in classifying diseases using medical imaging.

                Author and article information

                Lancet (London, England)
                Elsevier Ltd.
                6 October 2021
                6 October 2021
                [a ]Green Templeton College, University of Oxford, Oxford, UK
                [b ]School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada
                [c ]Denver Health and Hospital Authority, Denver, CO, USA
                [d ]Harvard T H Chan School of Public Health, Harvard University, Boston, MA, USA
                [e ]University of Vermont College of Medicine, Burlington, VT, USA
                [f ]National Cancer Institute, Bethesda, MD, USA
                [g ]Aga Khan University Hospital, Nairobi, Kenya
                [h ]Ministry of Health, Papeete, French Polynesia
                [i ]Médicos e Investigadores de la Lucha Contra el Cáncer de Mama, Mexico City, Mexico
                [j ]Perelman School of Medicine, University of Pennsylvania Philadelphia, Philadelphia, PA, USA
                [k ]FIND, Geneva, Switzerland
                [l ]Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
                [m ]The University of Hong Kong, Hong Kong Special Administrative Region, China
                [n ]University of Liberia, Monrovia, Liberia
                [o ]Department of Medical Imaging, Hospital Clínic of Barcelona, University of Barcelona, Barcelona, Spain
                [p ]University of Cape Town, Cape Town, South Africa
                [q ]Universidad Cayetano Heredia, Lima, Peru
                [r ]University of Massachusetts Medical School, Worcester, MA, USA
                [s ]Dana Farber Cancer Institute, Boston, MA, USA
                [t ]Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada
                [u ]School of Population and Global Health, McGill University, Montreal, QC, Canada
                [v ]Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
                [w ]University of Malaya, Kuala Lumpur, Malaysia
                [x ]Program in Global Surgery and Social Change, Harvard Medical School, Boston, MA, USA
                [y ]Africa Centres for Disease Control and Prevention, Addis Ababa, Ethiopia
                [z ]RAD-AID International, Santa Monica, CA, USA
                [aa ]Shri Hindu Mandal Hospital, Dar es Salaam, Tanzania
                [ab ]University of Michigan Medical School, Ann Arbor, MI, USA
                [ac ]King's College London, London, UK
                [ad ]Singapore General Hospital, Singapore
                [ae ]Indian Council of Medical Research, Delhi, India
                Author notes
                [* ]Correspondence to: Prof Susan Horton, School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada
                © 2021 Elsevier Ltd. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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