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Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings

Stephen Ali, Thomas Dobbs, Adib Tarafdar, Huw Strafford, Beata Fonferko-Shadrach, Arron S. Lacey Orcid Logo, Owen Pickrell Orcid Logo, Hayley Hutchings Orcid Logo, Iain Whitaker

British Journal of Surgery, Volume: 111, Issue: 1

Swansea University Authors: Stephen Ali, Thomas Dobbs, Adib Tarafdar, Huw Strafford, Beata Fonferko-Shadrach, Arron S. Lacey Orcid Logo, Owen Pickrell Orcid Logo, Hayley Hutchings Orcid Logo, Iain Whitaker

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DOI (Published version): 10.1093/bjs/znad347

Abstract

BackgroundCancer multidisciplinary team (MDT) meetings are under intense pressure to reform given the rapidly rising incidence of cancer and national mandates for protocolized streaming of cases. The aim of this study was to validate a natural language processing (NLP)-based web platform to automate...

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Published in: British Journal of Surgery
ISSN: 0007-1323 1365-2168
Published: Oxford University Press (OUP) 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa65543
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Lacey</name><active>true</active><ethesisStudent>true</ethesisStudent></author><author><sid>1c3044b5ff7a6552ff5e8c9e3901c807</sid><ORCID>0000-0003-4396-5657</ORCID><firstname>Owen</firstname><surname>Pickrell</surname><name>Owen Pickrell</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>bdf5d5f154d339dd92bb25884b7c3652</sid><ORCID>0000-0003-4155-1741</ORCID><firstname>Hayley</firstname><surname>Hutchings</surname><name>Hayley Hutchings</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>830074c59291938a55b480dcbee4697e</sid><ORCID/><firstname>Iain</firstname><surname>Whitaker</surname><name>Iain Whitaker</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-01-30</date><deptcode>PMSC</deptcode><abstract>BackgroundCancer multidisciplinary team (MDT) meetings are under intense pressure to reform given the rapidly rising incidence of cancer and national mandates for protocolized streaming of cases. The aim of this study was to validate a natural language processing (NLP)-based web platform to automate evidence-based MDT decisions for skin cancer with basal cell carcinoma as a use case.MethodsA novel and validated NLP information extraction model was used to extract perioperative tumour and surgical factors from histopathology reports. A web application with a bespoke application programming interface used data from this model to provide an automated clinical decision support system, mapped to national guidelines and generating a patient letter to communicate ongoing management. Performance was assessed against retrospectively derived recommendations by two independent and blinded expert clinicians.ResultsThere were 893 patients (1045 lesions) used to internally validate the model. High accuracy was observed when compared against human predictions, with an overall value of 0.92. Across all classifiers the virtual skin MDT was highly specific (0.96), while sensitivity was lower (0.72).ConclusionThis study demonstrates the feasibility of a fully automated, virtual, web-based service model to host the skin MDT with good system performance. This platform could be used to support clinical decision-making during MDTs as ‘human in the loop’ approach to aid protocolized streaming. 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I.S.W. is the surgical Specialty Lead for Health and Care Research Wales, and reports active grants from the American Association of Plastic Surgeons and the European Association of Plastic Surgeons; is an associate editor for the Annals of Plastic Surgery, editorial board of BMC Medicine and numerous other editorial board roles. S.R.A. received a grant from the British Association of Plastic, Reconstructive and Aesthetic Surgeons specifically for this work. The Reconstructive Surgery &amp; Regenerative Medicine Research Centre is funded by The Scar Free Foundation and Health and Care Research Wales. 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spelling v2 65543 2024-01-30 Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings 8c210736c07c6aa2514e0f6b3cfd9764 Stephen Ali Stephen Ali true false d18101ae0b4e72051f735ef68f45e1a8 Thomas Dobbs Thomas Dobbs true false 442ced26864ef6ca9f5a9421502a4318 Adib Tarafdar Adib Tarafdar true false a6389fc6d4d18e7b67033ee04b381e43 Huw Strafford Huw Strafford true false 7d3f1e80939f2b8fab6a16b5ec6ac845 Beata Fonferko-Shadrach Beata Fonferko-Shadrach true false 7af5c8bdd1197f85720e4f3d65e803eb 0000-0001-7983-8073 Arron S. Lacey Arron S. Lacey true true 1c3044b5ff7a6552ff5e8c9e3901c807 0000-0003-4396-5657 Owen Pickrell Owen Pickrell true false bdf5d5f154d339dd92bb25884b7c3652 0000-0003-4155-1741 Hayley Hutchings Hayley Hutchings true false 830074c59291938a55b480dcbee4697e Iain Whitaker Iain Whitaker true false 2024-01-30 PMSC BackgroundCancer multidisciplinary team (MDT) meetings are under intense pressure to reform given the rapidly rising incidence of cancer and national mandates for protocolized streaming of cases. The aim of this study was to validate a natural language processing (NLP)-based web platform to automate evidence-based MDT decisions for skin cancer with basal cell carcinoma as a use case.MethodsA novel and validated NLP information extraction model was used to extract perioperative tumour and surgical factors from histopathology reports. A web application with a bespoke application programming interface used data from this model to provide an automated clinical decision support system, mapped to national guidelines and generating a patient letter to communicate ongoing management. Performance was assessed against retrospectively derived recommendations by two independent and blinded expert clinicians.ResultsThere were 893 patients (1045 lesions) used to internally validate the model. High accuracy was observed when compared against human predictions, with an overall value of 0.92. Across all classifiers the virtual skin MDT was highly specific (0.96), while sensitivity was lower (0.72).ConclusionThis study demonstrates the feasibility of a fully automated, virtual, web-based service model to host the skin MDT with good system performance. This platform could be used to support clinical decision-making during MDTs as ‘human in the loop’ approach to aid protocolized streaming. Future prospective studies are needed to validate the model in tumour types where guidelines are more complex. Journal Article British Journal of Surgery 111 1 Oxford University Press (OUP) 0007-1323 1365-2168 10 1 2024 2024-01-10 10.1093/bjs/znad347 COLLEGE NANME Medicine COLLEGE CODE PMSC Swansea University External research funder(s) paid the OA fee (includes OA grants disbursed by the Library) S.R.A. and T.D.D. are funded by the Welsh Clinical Academic Training Fellowship. I.S.W. is the surgical Specialty Lead for Health and Care Research Wales, and reports active grants from the American Association of Plastic Surgeons and the European Association of Plastic Surgeons; is an associate editor for the Annals of Plastic Surgery, editorial board of BMC Medicine and numerous other editorial board roles. S.R.A. received a grant from the British Association of Plastic, Reconstructive and Aesthetic Surgeons specifically for this work. The Reconstructive Surgery & Regenerative Medicine Research Centre is funded by The Scar Free Foundation and Health and Care Research Wales. The Scar Free Foundation is the only medical research charity focused on scarring with the mission to achieve scar free healing within a generation. 2024-03-14T15:22:17.7960557 2024-01-30T16:00:05.0942467 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Biomedical Science Stephen Ali 1 Thomas Dobbs 2 Adib Tarafdar 3 Huw Strafford 4 Beata Fonferko-Shadrach 5 Arron S. Lacey 0000-0001-7983-8073 6 Owen Pickrell 0000-0003-4396-5657 7 Hayley Hutchings 0000-0003-4155-1741 8 Iain Whitaker 9 65543__29498__b2e9a14cc98c4bfda37029a0074e4a71.pdf znad347.pdf 2024-01-30T16:02:30.8702216 Output 538683 application/pdf Version of Record true This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) true eng https://creativecommons.org/licenses/by/4.0/
title Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings
spellingShingle Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings
Stephen Ali
Thomas Dobbs
Adib Tarafdar
Huw Strafford
Beata Fonferko-Shadrach
Arron S. Lacey
Owen Pickrell
Hayley Hutchings
Iain Whitaker
title_short Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings
title_full Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings
title_fullStr Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings
title_full_unstemmed Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings
title_sort Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings
author_id_str_mv 8c210736c07c6aa2514e0f6b3cfd9764
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a6389fc6d4d18e7b67033ee04b381e43
7d3f1e80939f2b8fab6a16b5ec6ac845
7af5c8bdd1197f85720e4f3d65e803eb
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author_id_fullname_str_mv 8c210736c07c6aa2514e0f6b3cfd9764_***_Stephen Ali
d18101ae0b4e72051f735ef68f45e1a8_***_Thomas Dobbs
442ced26864ef6ca9f5a9421502a4318_***_Adib Tarafdar
a6389fc6d4d18e7b67033ee04b381e43_***_Huw Strafford
7d3f1e80939f2b8fab6a16b5ec6ac845_***_Beata Fonferko-Shadrach
7af5c8bdd1197f85720e4f3d65e803eb_***_Arron S. Lacey
1c3044b5ff7a6552ff5e8c9e3901c807_***_Owen Pickrell
bdf5d5f154d339dd92bb25884b7c3652_***_Hayley Hutchings
830074c59291938a55b480dcbee4697e_***_Iain Whitaker
author Stephen Ali
Thomas Dobbs
Adib Tarafdar
Huw Strafford
Beata Fonferko-Shadrach
Arron S. Lacey
Owen Pickrell
Hayley Hutchings
Iain Whitaker
author2 Stephen Ali
Thomas Dobbs
Adib Tarafdar
Huw Strafford
Beata Fonferko-Shadrach
Arron S. Lacey
Owen Pickrell
Hayley Hutchings
Iain Whitaker
format Journal article
container_title British Journal of Surgery
container_volume 111
container_issue 1
publishDate 2024
institution Swansea University
issn 0007-1323
1365-2168
doi_str_mv 10.1093/bjs/znad347
publisher Oxford University Press (OUP)
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Biomedical Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Biomedical Science
document_store_str 1
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description BackgroundCancer multidisciplinary team (MDT) meetings are under intense pressure to reform given the rapidly rising incidence of cancer and national mandates for protocolized streaming of cases. The aim of this study was to validate a natural language processing (NLP)-based web platform to automate evidence-based MDT decisions for skin cancer with basal cell carcinoma as a use case.MethodsA novel and validated NLP information extraction model was used to extract perioperative tumour and surgical factors from histopathology reports. A web application with a bespoke application programming interface used data from this model to provide an automated clinical decision support system, mapped to national guidelines and generating a patient letter to communicate ongoing management. Performance was assessed against retrospectively derived recommendations by two independent and blinded expert clinicians.ResultsThere were 893 patients (1045 lesions) used to internally validate the model. High accuracy was observed when compared against human predictions, with an overall value of 0.92. Across all classifiers the virtual skin MDT was highly specific (0.96), while sensitivity was lower (0.72).ConclusionThis study demonstrates the feasibility of a fully automated, virtual, web-based service model to host the skin MDT with good system performance. This platform could be used to support clinical decision-making during MDTs as ‘human in the loop’ approach to aid protocolized streaming. Future prospective studies are needed to validate the model in tumour types where guidelines are more complex.
published_date 2024-01-10T15:22:14Z
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