Can we screen for pancreatic cancer? Identifying a sub-population of patients at high risk of subsequent diagnosis using machine learning techniques applied to primary care data
Pancreatic cancer (PC) represents a substantial public health burden. Pancreatic cancer patients have very low survival due to the difficulty of identifying cancers early when the tumour is localised to the site of origin and treatable. Recent progress has been made in identifying biomarkers for PC...
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| Vydáno v: | PloS one Ročník 16; číslo 6; s. e0251876 |
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United States
Public Library of Science
02.06.2021
Public Library of Science (PLoS) |
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | Pancreatic cancer (PC) represents a substantial public health burden. Pancreatic cancer patients have very low survival due to the difficulty of identifying cancers early when the tumour is localised to the site of origin and treatable. Recent progress has been made in identifying biomarkers for PC in the blood and urine, but these cannot be used for population-based screening as this would be prohibitively expensive and potentially harmful.
We conducted a case-control study using prospectively-collected electronic health records from primary care individually-linked to cancer registrations. Our cases were comprised of 1,139 patients, aged 15-99 years, diagnosed with pancreatic cancer between January 1, 2005 and June 30, 2009. Each case was age-, sex- and diagnosis time-matched to four non-pancreatic (cancer patient) controls. Disease and prescription codes for the 24 months prior to diagnosis were used to identify 57 individual symptoms. Using a machine learning approach, we trained a logistic regression model on 75% of the data to predict patients who later developed PC and tested the model's performance on the remaining 25%.
We were able to identify 41.3% of patients < = 60 years at 'high risk' of developing pancreatic cancer up to 20 months prior to diagnosis with 72.5% sensitivity, 59% specificity and, 66% AUC. 43.2% of patients >60 years were similarly identified at 17 months, with 65% sensitivity, 57% specificity and, 61% AUC. We estimate that combining our algorithm with currently available biomarker tests could result in 30 older and 400 younger patients per cancer being identified as 'potential patients', and the earlier diagnosis of around 60% of tumours.
After further work this approach could be applied in the primary care setting and has the potential to be used alongside a non-invasive biomarker test to increase earlier diagnosis. This would result in a greater number of patients surviving this devastating disease. |
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| AbstractList | Pancreatic cancer (PC) represents a substantial public health burden. Pancreatic cancer patients have very low survival due to the difficulty of identifying cancers early when the tumour is localised to the site of origin and treatable. Recent progress has been made in identifying biomarkers for PC in the blood and urine, but these cannot be used for population-based screening as this would be prohibitively expensive and potentially harmful.
We conducted a case-control study using prospectively-collected electronic health records from primary care individually-linked to cancer registrations. Our cases were comprised of 1,139 patients, aged 15-99 years, diagnosed with pancreatic cancer between January 1, 2005 and June 30, 2009. Each case was age-, sex- and diagnosis time-matched to four non-pancreatic (cancer patient) controls. Disease and prescription codes for the 24 months prior to diagnosis were used to identify 57 individual symptoms. Using a machine learning approach, we trained a logistic regression model on 75% of the data to predict patients who later developed PC and tested the model's performance on the remaining 25%.
We were able to identify 41.3% of patients < = 60 years at 'high risk' of developing pancreatic cancer up to 20 months prior to diagnosis with 72.5% sensitivity, 59% specificity and, 66% AUC. 43.2% of patients >60 years were similarly identified at 17 months, with 65% sensitivity, 57% specificity and, 61% AUC. We estimate that combining our algorithm with currently available biomarker tests could result in 30 older and 400 younger patients per cancer being identified as 'potential patients', and the earlier diagnosis of around 60% of tumours.
After further work this approach could be applied in the primary care setting and has the potential to be used alongside a non-invasive biomarker test to increase earlier diagnosis. This would result in a greater number of patients surviving this devastating disease. Background Pancreatic cancer (PC) represents a substantial public health burden. Pancreatic cancer patients have very low survival due to the difficulty of identifying cancers early when the tumour is localised to the site of origin and treatable. Recent progress has been made in identifying biomarkers for PC in the blood and urine, but these cannot be used for population-based screening as this would be prohibitively expensive and potentially harmful. Methods We conducted a case-control study using prospectively-collected electronic health records from primary care individually-linked to cancer registrations. Our cases were comprised of 1,139 patients, aged 15-99 years, diagnosed with pancreatic cancer between January 1, 2005 and June 30, 2009. Each case was age-, sex- and diagnosis time-matched to four non-pancreatic (cancer patient) controls. Disease and prescription codes for the 24 months prior to diagnosis were used to identify 57 individual symptoms. Using a machine learning approach, we trained a logistic regression model on 75% of the data to predict patients who later developed PC and tested the model's performance on the remaining 25%. Results We were able to identify 41.3% of patients 60 years were similarly identified at 17 months, with 65% sensitivity, 57% specificity and, 61% AUC. We estimate that combining our algorithm with currently available biomarker tests could result in 30 older and 400 younger patients per cancer being identified as 'potential patients', and the earlier diagnosis of around 60% of tumours. Conclusion After further work this approach could be applied in the primary care setting and has the potential to be used alongside a non-invasive biomarker test to increase earlier diagnosis. This would result in a greater number of patients surviving this devastating disease. Pancreatic cancer (PC) represents a substantial public health burden. Pancreatic cancer patients have very low survival due to the difficulty of identifying cancers early when the tumour is localised to the site of origin and treatable. Recent progress has been made in identifying biomarkers for PC in the blood and urine, but these cannot be used for population-based screening as this would be prohibitively expensive and potentially harmful. We conducted a case-control study using prospectively-collected electronic health records from primary care individually-linked to cancer registrations. Our cases were comprised of 1,139 patients, aged 15-99 years, diagnosed with pancreatic cancer between January 1, 2005 and June 30, 2009. Each case was age-, sex- and diagnosis time-matched to four non-pancreatic (cancer patient) controls. Disease and prescription codes for the 24 months prior to diagnosis were used to identify 57 individual symptoms. Using a machine learning approach, we trained a logistic regression model on 75% of the data to predict patients who later developed PC and tested the model's performance on the remaining 25%. We were able to identify 41.3% of patients 60 years were similarly identified at 17 months, with 65% sensitivity, 57% specificity and, 61% AUC. We estimate that combining our algorithm with currently available biomarker tests could result in 30 older and 400 younger patients per cancer being identified as 'potential patients', and the earlier diagnosis of around 60% of tumours. After further work this approach could be applied in the primary care setting and has the potential to be used alongside a non-invasive biomarker test to increase earlier diagnosis. This would result in a greater number of patients surviving this devastating disease. BackgroundPancreatic cancer (PC) represents a substantial public health burden. Pancreatic cancer patients have very low survival due to the difficulty of identifying cancers early when the tumour is localised to the site of origin and treatable. Recent progress has been made in identifying biomarkers for PC in the blood and urine, but these cannot be used for population-based screening as this would be prohibitively expensive and potentially harmful.MethodsWe conducted a case-control study using prospectively-collected electronic health records from primary care individually-linked to cancer registrations. Our cases were comprised of 1,139 patients, aged 15-99 years, diagnosed with pancreatic cancer between January 1, 2005 and June 30, 2009. Each case was age-, sex- and diagnosis time-matched to four non-pancreatic (cancer patient) controls. Disease and prescription codes for the 24 months prior to diagnosis were used to identify 57 individual symptoms. Using a machine learning approach, we trained a logistic regression model on 75% of the data to predict patients who later developed PC and tested the model's performance on the remaining 25%.ResultsWe were able to identify 41.3% of patients < = 60 years at 'high risk' of developing pancreatic cancer up to 20 months prior to diagnosis with 72.5% sensitivity, 59% specificity and, 66% AUC. 43.2% of patients >60 years were similarly identified at 17 months, with 65% sensitivity, 57% specificity and, 61% AUC. We estimate that combining our algorithm with currently available biomarker tests could result in 30 older and 400 younger patients per cancer being identified as 'potential patients', and the earlier diagnosis of around 60% of tumours.ConclusionAfter further work this approach could be applied in the primary care setting and has the potential to be used alongside a non-invasive biomarker test to increase earlier diagnosis. This would result in a greater number of patients surviving this devastating disease. Background Pancreatic cancer (PC) represents a substantial public health burden. Pancreatic cancer patients have very low survival due to the difficulty of identifying cancers early when the tumour is localised to the site of origin and treatable. Recent progress has been made in identifying biomarkers for PC in the blood and urine, but these cannot be used for population-based screening as this would be prohibitively expensive and potentially harmful. Methods We conducted a case-control study using prospectively-collected electronic health records from primary care individually-linked to cancer registrations. Our cases were comprised of 1,139 patients, aged 15–99 years, diagnosed with pancreatic cancer between January 1, 2005 and June 30, 2009. Each case was age-, sex- and diagnosis time-matched to four non-pancreatic (cancer patient) controls. Disease and prescription codes for the 24 months prior to diagnosis were used to identify 57 individual symptoms. Using a machine learning approach, we trained a logistic regression model on 75% of the data to predict patients who later developed PC and tested the model’s performance on the remaining 25%. Results We were able to identify 41.3% of patients < = 60 years at ‘high risk’ of developing pancreatic cancer up to 20 months prior to diagnosis with 72.5% sensitivity, 59% specificity and, 66% AUC. 43.2% of patients >60 years were similarly identified at 17 months, with 65% sensitivity, 57% specificity and, 61% AUC. We estimate that combining our algorithm with currently available biomarker tests could result in 30 older and 400 younger patients per cancer being identified as ‘potential patients’, and the earlier diagnosis of around 60% of tumours. Conclusion After further work this approach could be applied in the primary care setting and has the potential to be used alongside a non-invasive biomarker test to increase earlier diagnosis. This would result in a greater number of patients surviving this devastating disease. Pancreatic cancer (PC) represents a substantial public health burden. Pancreatic cancer patients have very low survival due to the difficulty of identifying cancers early when the tumour is localised to the site of origin and treatable. Recent progress has been made in identifying biomarkers for PC in the blood and urine, but these cannot be used for population-based screening as this would be prohibitively expensive and potentially harmful.BACKGROUNDPancreatic cancer (PC) represents a substantial public health burden. Pancreatic cancer patients have very low survival due to the difficulty of identifying cancers early when the tumour is localised to the site of origin and treatable. Recent progress has been made in identifying biomarkers for PC in the blood and urine, but these cannot be used for population-based screening as this would be prohibitively expensive and potentially harmful.We conducted a case-control study using prospectively-collected electronic health records from primary care individually-linked to cancer registrations. Our cases were comprised of 1,139 patients, aged 15-99 years, diagnosed with pancreatic cancer between January 1, 2005 and June 30, 2009. Each case was age-, sex- and diagnosis time-matched to four non-pancreatic (cancer patient) controls. Disease and prescription codes for the 24 months prior to diagnosis were used to identify 57 individual symptoms. Using a machine learning approach, we trained a logistic regression model on 75% of the data to predict patients who later developed PC and tested the model's performance on the remaining 25%.METHODSWe conducted a case-control study using prospectively-collected electronic health records from primary care individually-linked to cancer registrations. Our cases were comprised of 1,139 patients, aged 15-99 years, diagnosed with pancreatic cancer between January 1, 2005 and June 30, 2009. Each case was age-, sex- and diagnosis time-matched to four non-pancreatic (cancer patient) controls. Disease and prescription codes for the 24 months prior to diagnosis were used to identify 57 individual symptoms. Using a machine learning approach, we trained a logistic regression model on 75% of the data to predict patients who later developed PC and tested the model's performance on the remaining 25%.We were able to identify 41.3% of patients < = 60 years at 'high risk' of developing pancreatic cancer up to 20 months prior to diagnosis with 72.5% sensitivity, 59% specificity and, 66% AUC. 43.2% of patients >60 years were similarly identified at 17 months, with 65% sensitivity, 57% specificity and, 61% AUC. We estimate that combining our algorithm with currently available biomarker tests could result in 30 older and 400 younger patients per cancer being identified as 'potential patients', and the earlier diagnosis of around 60% of tumours.RESULTSWe were able to identify 41.3% of patients < = 60 years at 'high risk' of developing pancreatic cancer up to 20 months prior to diagnosis with 72.5% sensitivity, 59% specificity and, 66% AUC. 43.2% of patients >60 years were similarly identified at 17 months, with 65% sensitivity, 57% specificity and, 61% AUC. We estimate that combining our algorithm with currently available biomarker tests could result in 30 older and 400 younger patients per cancer being identified as 'potential patients', and the earlier diagnosis of around 60% of tumours.After further work this approach could be applied in the primary care setting and has the potential to be used alongside a non-invasive biomarker test to increase earlier diagnosis. This would result in a greater number of patients surviving this devastating disease.CONCLUSIONAfter further work this approach could be applied in the primary care setting and has the potential to be used alongside a non-invasive biomarker test to increase earlier diagnosis. This would result in a greater number of patients surviving this devastating disease. |
| Audience | Academic |
| Author | Malhotra, Ananya Bonaventure, Audrey Rachet, Bernard Pereira, Stephen P. Woods, Laura M. |
| AuthorAffiliation | 1 Faculty of Epidemiology and Population Health, Department of Non-Communicable Disease Epidemiology, Inequalities in Cancer Outcomes Network, London School of Hygiene & Tropical Medicine, London, United Kingdom 2 Epidemiology of Childhood and Adolescent Cancers Team, CRESS, Université de Paris-INSERM, Villejuif, France Centro Nacional de Investigaciones Oncologicas, SPAIN 3 UCL Institute for Liver and Digestive Health, University College London, London, United Kingdom |
| AuthorAffiliation_xml | – name: 1 Faculty of Epidemiology and Population Health, Department of Non-Communicable Disease Epidemiology, Inequalities in Cancer Outcomes Network, London School of Hygiene & Tropical Medicine, London, United Kingdom – name: Centro Nacional de Investigaciones Oncologicas, SPAIN – name: 3 UCL Institute for Liver and Digestive Health, University College London, London, United Kingdom – name: 2 Epidemiology of Childhood and Adolescent Cancers Team, CRESS, Université de Paris-INSERM, Villejuif, France |
| Author_xml | – sequence: 1 givenname: Ananya orcidid: 0000-0002-0691-0705 surname: Malhotra fullname: Malhotra, Ananya – sequence: 2 givenname: Bernard surname: Rachet fullname: Rachet, Bernard – sequence: 3 givenname: Audrey surname: Bonaventure fullname: Bonaventure, Audrey – sequence: 4 givenname: Stephen P. surname: Pereira fullname: Pereira, Stephen P. – sequence: 5 givenname: Laura M. surname: Woods fullname: Woods, Laura M. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34077433$$D View this record in MEDLINE/PubMed https://inserm.hal.science/inserm-04023259$$DView record in HAL |
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| Cites_doi | 10.1038/nature14581 10.1001/jama.2013.284664 10.1016/S1542-3565(04)00171-5 10.1038/nrclinonc.2009.236 10.1038/s41416-019-0694-0 10.1038/bjc.2012.190 10.1038/sj.bjc.6604576 10.1158/1078-0432.CCR-14-2467 10.1093/fampra/cmq046 10.1016/j.gastro.2005.05.007 10.1007/s12029-015-9724-1 10.1126/science.aar3247 10.1186/s12885-019-6284-y 10.1016/j.trecan.2017.04.005 10.1136/bmj.k764 10.1136/bmjopen-2019-031537 10.1158/0008-5472.CAN-14-0734 10.1038/sj.bjc.6605636 10.1016/S2468-1253(19)30416-9 10.1158/1078-0432.CCR-14-0365 10.1111/trf.14057 10.1136/bmjopen-2013-003389 10.1093/jnci/djw266 10.1053/j.gastro.2019.01.259 |
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| Copyright | COPYRIGHT 2021 Public Library of Science 2021 Malhotra et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Distributed under a Creative Commons Attribution 4.0 International License 2021 Malhotra et al 2021 Malhotra et al |
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| License | Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Creative Commons Attribution License |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 PMCID: PMC8171946 Competing Interests: The authors declare no competing interests. |
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| References | K Bhaskaran (pone.0251876.ref022) 2013; 3 A Stathis (pone.0251876.ref006) 2010; 7 AZ Daoud (pone.0251876.ref010) 2019; 19 EA Holly (pone.0251876.ref030) 2004; 2 Team R. (pone.0251876.ref024) 2020 SP Pereira (pone.0251876.ref001) 2020; 5 O Blyuss (pone.0251876.ref015) 2020; 122 A Carrato (pone.0251876.ref003) 2015; 46 A Lux (pone.0251876.ref011) 2019; 20 A Exarchakou (pone.0251876.ref005) 2018; 360 JD Cohen (pone.0251876.ref018) 2018; 359 M Capello (pone.0251876.ref016) 2017; 109 MC Bradley (pone.0251876.ref027) 2010; 102 K Mansfield (pone.0251876.ref028) 2019; 9 S. Aaron (pone.0251876.ref025) 2017; 57 LE Oldfield (pone.0251876.ref004) 2017; 3 ST Chari (pone.0251876.ref031) 2005; 129 pone.0251876.ref023 DP O’Brien (pone.0251876.ref013) 2015; 21 pone.0251876.ref020 pone.0251876.ref021 E Mitry (pone.0251876.ref002) 2008; 99 pone.0251876.ref008 AD Singhi (pone.0251876.ref009) 2019; 156 L Szatkowski (pone.0251876.ref029) 2010; 27 pone.0251876.ref007 AM Lennon (pone.0251876.ref019) 2014; 74 SA Melo (pone.0251876.ref012) 2015; 523 S Stapley (pone.0251876.ref026) 2012; 106 NA Schultz (pone.0251876.ref014) 2014; 311 TP Radon (pone.0251876.ref017) 2015; 21 |
| References_xml | – volume: 523 start-page: 177 issue: 7559 year: 2015 ident: pone.0251876.ref012 article-title: Glypican-1 identifies cancer exosomes and detects early pancreatic cancer publication-title: Nature doi: 10.1038/nature14581 – volume: 311 start-page: 392 issue: 4 year: 2014 ident: pone.0251876.ref014 article-title: MicroRNA Biomarkers in Whole Blood for Detection of Pancreatic Cancer publication-title: JAMA doi: 10.1001/jama.2013.284664 – volume: 2 start-page: 510 issue: 6 year: 2004 ident: pone.0251876.ref030 article-title: Signs and symptoms of pancreatic cancer: a population-based case-control study in the San Francisco Bay area publication-title: Clin Gastroenterol Hepatol doi: 10.1016/S1542-3565(04)00171-5 – volume: 7 start-page: 163 issue: 3 year: 2010 ident: pone.0251876.ref006 article-title: Advanced pancreatic carcinoma: current treatment and future challenges publication-title: Nat Rev Clin Oncol doi: 10.1038/nrclinonc.2009.236 – volume: 122 start-page: 692 issue: 5 year: 2020 ident: pone.0251876.ref015 article-title: Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients publication-title: Br J Cancer doi: 10.1038/s41416-019-0694-0 – volume: 106 start-page: 1940 issue: 12 year: 2012 ident: pone.0251876.ref026 article-title: The risk of pancreatic cancer in symptomatic patients in primary care: a large case-control study using electronic records publication-title: Br J Cancer doi: 10.1038/bjc.2012.190 – volume: 99 start-page: S21 year: 2008 ident: pone.0251876.ref002 article-title: Survival from cancer of the pancreas in England and Wales up to 2001 publication-title: Br J Cancer doi: 10.1038/sj.bjc.6604576 – ident: pone.0251876.ref007 – ident: pone.0251876.ref020 – volume: 21 start-page: 3512 issue: 15 year: 2015 ident: pone.0251876.ref017 article-title: Identification of a Three-Biomarker Panel in Urine for Early Detection of Pancreatic Adenocarcinoma publication-title: Clinical Cancer Research doi: 10.1158/1078-0432.CCR-14-2467 – volume: 27 start-page: 673 issue: 6 year: 2010 ident: pone.0251876.ref029 article-title: Is smoking status routinely recorded when patients register with a new GP? publication-title: Family Practice doi: 10.1093/fampra/cmq046 – volume: 129 start-page: 504 issue: 2 year: 2005 ident: pone.0251876.ref031 article-title: Probability of pancreatic cancer following diabetes: a population-based study publication-title: Gastroenterology doi: 10.1016/j.gastro.2005.05.007 – volume: 46 start-page: 201 issue: 3 year: 2015 ident: pone.0251876.ref003 article-title: A Systematic Review of the Burden of Pancreatic Cancer in Europe: Real-World Impact on Survival, Quality of Life and Costs publication-title: J Gastrointest Cancer. doi: 10.1007/s12029-015-9724-1 – volume: 359 start-page: 926 issue: 6378 year: 2018 ident: pone.0251876.ref018 article-title: Detection and localization of surgically resectable cancers with a multi-analyte blood test publication-title: Science doi: 10.1126/science.aar3247 – volume: 19 start-page: 1130 issue: 1 year: 2019 ident: pone.0251876.ref010 article-title: MicroRNAs in Pancreatic Cancer: biomarkers, prognostic, and therapeutic modulators publication-title: BMC Cancer doi: 10.1186/s12885-019-6284-y – volume: 3 start-page: 336 issue: 5 year: 2017 ident: pone.0251876.ref004 article-title: Molecular Events in the Natural History of Pancreatic Cancer publication-title: Trends Cancer doi: 10.1016/j.trecan.2017.04.005 – volume: 20 issue: 13 year: 2019 ident: pone.0251876.ref011 article-title: c-Met and PD-L1 on Circulating Exosomes as Diagnostic and Prognostic Markers for Pancreatic Cancer publication-title: Int J Mol Sci – volume: 360 start-page: k764 year: 2018 ident: pone.0251876.ref005 article-title: Impact of national cancer policies on cancer survival trends and socioeconomic inequalities in England, 1996–2013: population based study publication-title: BMJ doi: 10.1136/bmj.k764 – volume: 9 issue: 11 year: 2019 ident: pone.0251876.ref028 article-title: Completeness and validity of alcohol recording in general practice within the UK: a cross-sectional study publication-title: BMJ Open doi: 10.1136/bmjopen-2019-031537 – volume: 74 start-page: 3381 issue: 13 year: 2014 ident: pone.0251876.ref019 article-title: The Early Detection of Pancreatic Cancer: What Will It Take to Diagnose and Treat Curable Pancreatic Neoplasia? publication-title: Cancer Res doi: 10.1158/0008-5472.CAN-14-0734 – volume: 102 start-page: 1415 issue: 9 year: 2010 ident: pone.0251876.ref027 article-title: Non-steroidal anti-inflammatory drugs and pancreatic cancer risk: a nested case-control study publication-title: Br J Cancer doi: 10.1038/sj.bjc.6605636 – volume: 5 start-page: 698 issue: 7 year: 2020 ident: pone.0251876.ref001 article-title: Early detection of pancreatic cancer publication-title: Lancet Gastroenterol Hepatol. doi: 10.1016/S2468-1253(19)30416-9 – ident: pone.0251876.ref021 – volume: 21 start-page: 622 issue: 3 year: 2015 ident: pone.0251876.ref013 article-title: Serum CA19-9 is significantly upregulated up to 2 years before diagnosis with pancreatic cancer: implications for early disease detection publication-title: Clin Cancer Res doi: 10.1158/1078-0432.CCR-14-0365 – ident: pone.0251876.ref023 – volume: 57 start-page: 877 issue: 4 year: 2017 ident: pone.0251876.ref025 article-title: Understanding tests of the association of categorical variables: the Pearson chi‐square test and Fisher’s exact test publication-title: Transfusion doi: 10.1111/trf.14057 – volume: 3 start-page: e003389 issue: 9 year: 2013 ident: pone.0251876.ref022 article-title: Representativeness and optimal use of body mass index (BMI) in the UK Clinical Practice Research Datalink (CPRD) publication-title: BMJ Open doi: 10.1136/bmjopen-2013-003389 – ident: pone.0251876.ref008 – volume: 109 issue: 4 year: 2017 ident: pone.0251876.ref016 article-title: Sequential Validation of Blood-Based Protein Biomarker Candidates for Early-Stage Pancreatic Cancer publication-title: J Natl Cancer Inst doi: 10.1093/jnci/djw266 – volume: 156 start-page: 2024 issue: 7 year: 2019 ident: pone.0251876.ref009 article-title: Early Detection of Pancreatic Cancer: Opportunities and Challenges publication-title: Gastroenterology doi: 10.1053/j.gastro.2019.01.259 – volume-title: RStudio: Integrated Development for R. RStudio year: 2020 ident: pone.0251876.ref024 |
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| Title | Can we screen for pancreatic cancer? Identifying a sub-population of patients at high risk of subsequent diagnosis using machine learning techniques applied to primary care data |
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