Perception of cure in prostate cancer: human-led and artificial intelligence-assisted landscape review and linguistic analysis of literature, social media and policy documents

Understanding stakeholders’ perception of cure in prostate cancer (PC) is essential to preparing for effective communication about emerging treatments with curative intent. This study used artificial intelligence (AI) for landscape review and linguistic analysis of definition, context and value of c...

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Veröffentlicht in:ESMO open 2024-05, Vol.9 (5), p.103007-103007, Article 103007
Hauptverfasser: Efstathiou, E., Merseburger, A., Liew, A., Kurtyka, K., Panda, O., Dalechek, D., Heerdegen, A.C.S., Jain, R., De Solda, F., McCarthy, S.A., Brookman-May, S.D., Mundle, S.D., Yu Ko, W., Krabbe, L.-M.
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container_issue 5
container_start_page 103007
container_title ESMO open
container_volume 9
creator Efstathiou, E.
Merseburger, A.
Liew, A.
Kurtyka, K.
Panda, O.
Dalechek, D.
Heerdegen, A.C.S.
Jain, R.
De Solda, F.
McCarthy, S.A.
Brookman-May, S.D.
Mundle, S.D.
Yu Ko, W.
Krabbe, L.-M.
description Understanding stakeholders’ perception of cure in prostate cancer (PC) is essential to preparing for effective communication about emerging treatments with curative intent. This study used artificial intelligence (AI) for landscape review and linguistic analysis of definition, context and value of cure among stakeholders in PC. Subject-matter experts (SMEs) selected cure-related key words using Elicit, a semantic literature search engine, and extracted hits containing the key words from Medline, Sermo and Overton, representing academic researchers, health care providers (HCPs) and policymakers, respectively. NetBase Quid, a social media analytics and natural language processing tool, was used to carry out key word searches in social media (representing the general public). NetBase Quid analysed linguistics of key word-specific hit sets for key word count, geolocation and sentiments. SMEs qualitatively summarised key word-specific insights. Contextual terms frequently occurring with key words were identified and quantified. SMEs identified seven key words applicable to PC (number of acquired hits) across four platforms: Cure (12429), Survivor (6063), Remission (1904), Survivorship (1179), Curative intent (432), No evidence of disease (381) and Complete remission (83). Most commonly used key words were Cure by the general public and HCPs (11815 and 224 hits), Survivorship by academic researchers and Survivor by policymakers (378 hits each). All stakeholders discussed Cure and cure-related key words primarily in early-stage PC and associated them with positive sentiments. All stakeholders defined cure differently but communicated about it in relation to disease measurements (e.g. prostate-specific antigen) or surgery. Stakeholders preferred different terms when discussing cure in PC: Cure (academic researchers), Cure rates (HCPs), Potential cure and Survivor/Survivorship (policymakers) and Cure and Survivor (general public). This human-led, AI-assisted large-scale qualitative language-based research revealed that cure was commonly discussed by academic researchers, HCPs, policymakers and the general public, especially in early-stage PC. Stakeholders defined and contextualised cure in their communications differently and associated it with positive value. [Display omitted] •AI can be used successfully in qualitative research involving large language-based databases.•Academic researchers, clinicians, policymakers and the general public actively discuss cure in
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This study used artificial intelligence (AI) for landscape review and linguistic analysis of definition, context and value of cure among stakeholders in PC. Subject-matter experts (SMEs) selected cure-related key words using Elicit, a semantic literature search engine, and extracted hits containing the key words from Medline, Sermo and Overton, representing academic researchers, health care providers (HCPs) and policymakers, respectively. NetBase Quid, a social media analytics and natural language processing tool, was used to carry out key word searches in social media (representing the general public). NetBase Quid analysed linguistics of key word-specific hit sets for key word count, geolocation and sentiments. SMEs qualitatively summarised key word-specific insights. Contextual terms frequently occurring with key words were identified and quantified. SMEs identified seven key words applicable to PC (number of acquired hits) across four platforms: Cure (12429), Survivor (6063), Remission (1904), Survivorship (1179), Curative intent (432), No evidence of disease (381) and Complete remission (83). Most commonly used key words were Cure by the general public and HCPs (11815 and 224 hits), Survivorship by academic researchers and Survivor by policymakers (378 hits each). All stakeholders discussed Cure and cure-related key words primarily in early-stage PC and associated them with positive sentiments. All stakeholders defined cure differently but communicated about it in relation to disease measurements (e.g. prostate-specific antigen) or surgery. Stakeholders preferred different terms when discussing cure in PC: Cure (academic researchers), Cure rates (HCPs), Potential cure and Survivor/Survivorship (policymakers) and Cure and Survivor (general public). This human-led, AI-assisted large-scale qualitative language-based research revealed that cure was commonly discussed by academic researchers, HCPs, policymakers and the general public, especially in early-stage PC. Stakeholders defined and contextualised cure in their communications differently and associated it with positive value. [Display omitted] •AI can be used successfully in qualitative research involving large language-based databases.•Academic researchers, clinicians, policymakers and the general public actively discuss cure in early-stage PC.•Stakeholders use different definitions of and context for cure in their communications about cure.•Cure and cure-related key words are positively perceived by all stakeholders.</description><identifier>ISSN: 2059-7029</identifier><identifier>EISSN: 2059-7029</identifier><identifier>DOI: 10.1016/j.esmoop.2024.103007</identifier><identifier>PMID: 38744101</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Artificial Intelligence ; early-stage prostate cancer ; Health Policy ; Humans ; LAPC ; Linguistics - methods ; localised prostate cancer ; locally advanced prostate cancer ; LPC ; LPC/LAPC ; Male ; Natural Language Processing ; Original Research ; Perception ; Prostatic Neoplasms - therapy ; Social Media</subject><ispartof>ESMO open, 2024-05, Vol.9 (5), p.103007-103007, Article 103007</ispartof><rights>2024 The Authors</rights><rights>Copyright © 2024 The Authors. 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All rights reserved.</rights><rights>2024 The Authors 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c413t-de62ca46c417e2a3ece8af85dc2be7f20097b31760b0ae0a8e93f2a540e85efb3</cites><orcidid>0000-0001-6430-3324 ; 0000-0002-1217-8231 ; 0000-0001-5504-9707 ; 0000-0002-3751-3980 ; 0000-0002-5007-3529 ; 0000-0003-2967-4028</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11108859/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11108859/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,729,782,786,887,27931,27932,53798,53800</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38744101$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Efstathiou, E.</creatorcontrib><creatorcontrib>Merseburger, A.</creatorcontrib><creatorcontrib>Liew, A.</creatorcontrib><creatorcontrib>Kurtyka, K.</creatorcontrib><creatorcontrib>Panda, O.</creatorcontrib><creatorcontrib>Dalechek, D.</creatorcontrib><creatorcontrib>Heerdegen, A.C.S.</creatorcontrib><creatorcontrib>Jain, R.</creatorcontrib><creatorcontrib>De Solda, F.</creatorcontrib><creatorcontrib>McCarthy, S.A.</creatorcontrib><creatorcontrib>Brookman-May, S.D.</creatorcontrib><creatorcontrib>Mundle, S.D.</creatorcontrib><creatorcontrib>Yu Ko, W.</creatorcontrib><creatorcontrib>Krabbe, L.-M.</creatorcontrib><title>Perception of cure in prostate cancer: human-led and artificial intelligence-assisted landscape review and linguistic analysis of literature, social media and policy documents</title><title>ESMO open</title><addtitle>ESMO Open</addtitle><description>Understanding stakeholders’ perception of cure in prostate cancer (PC) is essential to preparing for effective communication about emerging treatments with curative intent. This study used artificial intelligence (AI) for landscape review and linguistic analysis of definition, context and value of cure among stakeholders in PC. Subject-matter experts (SMEs) selected cure-related key words using Elicit, a semantic literature search engine, and extracted hits containing the key words from Medline, Sermo and Overton, representing academic researchers, health care providers (HCPs) and policymakers, respectively. NetBase Quid, a social media analytics and natural language processing tool, was used to carry out key word searches in social media (representing the general public). NetBase Quid analysed linguistics of key word-specific hit sets for key word count, geolocation and sentiments. SMEs qualitatively summarised key word-specific insights. Contextual terms frequently occurring with key words were identified and quantified. SMEs identified seven key words applicable to PC (number of acquired hits) across four platforms: Cure (12429), Survivor (6063), Remission (1904), Survivorship (1179), Curative intent (432), No evidence of disease (381) and Complete remission (83). Most commonly used key words were Cure by the general public and HCPs (11815 and 224 hits), Survivorship by academic researchers and Survivor by policymakers (378 hits each). All stakeholders discussed Cure and cure-related key words primarily in early-stage PC and associated them with positive sentiments. All stakeholders defined cure differently but communicated about it in relation to disease measurements (e.g. prostate-specific antigen) or surgery. Stakeholders preferred different terms when discussing cure in PC: Cure (academic researchers), Cure rates (HCPs), Potential cure and Survivor/Survivorship (policymakers) and Cure and Survivor (general public). This human-led, AI-assisted large-scale qualitative language-based research revealed that cure was commonly discussed by academic researchers, HCPs, policymakers and the general public, especially in early-stage PC. Stakeholders defined and contextualised cure in their communications differently and associated it with positive value. [Display omitted] •AI can be used successfully in qualitative research involving large language-based databases.•Academic researchers, clinicians, policymakers and the general public actively discuss cure in early-stage PC.•Stakeholders use different definitions of and context for cure in their communications about cure.•Cure and cure-related key words are positively perceived by all stakeholders.</description><subject>Artificial Intelligence</subject><subject>early-stage prostate cancer</subject><subject>Health Policy</subject><subject>Humans</subject><subject>LAPC</subject><subject>Linguistics - methods</subject><subject>localised prostate cancer</subject><subject>locally advanced prostate cancer</subject><subject>LPC</subject><subject>LPC/LAPC</subject><subject>Male</subject><subject>Natural Language Processing</subject><subject>Original Research</subject><subject>Perception</subject><subject>Prostatic Neoplasms - therapy</subject><subject>Social Media</subject><issn>2059-7029</issn><issn>2059-7029</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kc1u1TAQhSMEolXpGyDkJQtysfMfFiBU8SdVahewtibO5HaunDjYTtF9Kl6RuTelajddWPbY35xjzUmS10pulFTV-90Gw-jcvMlkVvBVLmX9LDnNZNmmtcza5w_OJ8l5CDsppaoLvqxeJid5UxcFC50mf6_RG5wjuUm4QZjFo6BJzN6FCBGFgcmg_yBulhGm1GIvYOLlIw1kCCzDEa2lLTKXQggUIkOWqWBgRuHxlvDPscvStF34nQyXYPfMHjwtRfQQ2fmdCO4oOmJPcOyZnSWzF70zy4hTDK-SFwPYgOd3-1ny6-uXnxff08urbz8uPl-mplB5THusMgNFxVWNGeRosIGhKXuTdVgPmZRt3eWqrmQnASU02OZDBmUhsSlx6PKz5NOqOy8d_8awtwerZ08j-L12QPrxy0Q3eututVJKNk3ZssLbOwXvfi8Yoh4pGJ4VTOiWoHNZlkWpqrpktFhRw2MPHod7HyX1IW-902ve-pC3XvPmtjcP_3jf9D9dBj6uAPKkOAavg6FDUD15NFH3jp52-AdqRMQ7</recordid><startdate>20240501</startdate><enddate>20240501</enddate><creator>Efstathiou, E.</creator><creator>Merseburger, A.</creator><creator>Liew, A.</creator><creator>Kurtyka, K.</creator><creator>Panda, O.</creator><creator>Dalechek, D.</creator><creator>Heerdegen, A.C.S.</creator><creator>Jain, R.</creator><creator>De Solda, F.</creator><creator>McCarthy, S.A.</creator><creator>Brookman-May, S.D.</creator><creator>Mundle, S.D.</creator><creator>Yu Ko, W.</creator><creator>Krabbe, L.-M.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-6430-3324</orcidid><orcidid>https://orcid.org/0000-0002-1217-8231</orcidid><orcidid>https://orcid.org/0000-0001-5504-9707</orcidid><orcidid>https://orcid.org/0000-0002-3751-3980</orcidid><orcidid>https://orcid.org/0000-0002-5007-3529</orcidid><orcidid>https://orcid.org/0000-0003-2967-4028</orcidid></search><sort><creationdate>20240501</creationdate><title>Perception of cure in prostate cancer: human-led and artificial intelligence-assisted landscape review and linguistic analysis of literature, social media and policy documents</title><author>Efstathiou, E. ; 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This study used artificial intelligence (AI) for landscape review and linguistic analysis of definition, context and value of cure among stakeholders in PC. Subject-matter experts (SMEs) selected cure-related key words using Elicit, a semantic literature search engine, and extracted hits containing the key words from Medline, Sermo and Overton, representing academic researchers, health care providers (HCPs) and policymakers, respectively. NetBase Quid, a social media analytics and natural language processing tool, was used to carry out key word searches in social media (representing the general public). NetBase Quid analysed linguistics of key word-specific hit sets for key word count, geolocation and sentiments. SMEs qualitatively summarised key word-specific insights. Contextual terms frequently occurring with key words were identified and quantified. SMEs identified seven key words applicable to PC (number of acquired hits) across four platforms: Cure (12429), Survivor (6063), Remission (1904), Survivorship (1179), Curative intent (432), No evidence of disease (381) and Complete remission (83). Most commonly used key words were Cure by the general public and HCPs (11815 and 224 hits), Survivorship by academic researchers and Survivor by policymakers (378 hits each). All stakeholders discussed Cure and cure-related key words primarily in early-stage PC and associated them with positive sentiments. All stakeholders defined cure differently but communicated about it in relation to disease measurements (e.g. prostate-specific antigen) or surgery. Stakeholders preferred different terms when discussing cure in PC: Cure (academic researchers), Cure rates (HCPs), Potential cure and Survivor/Survivorship (policymakers) and Cure and Survivor (general public). This human-led, AI-assisted large-scale qualitative language-based research revealed that cure was commonly discussed by academic researchers, HCPs, policymakers and the general public, especially in early-stage PC. Stakeholders defined and contextualised cure in their communications differently and associated it with positive value. [Display omitted] •AI can be used successfully in qualitative research involving large language-based databases.•Academic researchers, clinicians, policymakers and the general public actively discuss cure in early-stage PC.•Stakeholders use different definitions of and context for cure in their communications about cure.•Cure and cure-related key words are positively perceived by all stakeholders.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>38744101</pmid><doi>10.1016/j.esmoop.2024.103007</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-6430-3324</orcidid><orcidid>https://orcid.org/0000-0002-1217-8231</orcidid><orcidid>https://orcid.org/0000-0001-5504-9707</orcidid><orcidid>https://orcid.org/0000-0002-3751-3980</orcidid><orcidid>https://orcid.org/0000-0002-5007-3529</orcidid><orcidid>https://orcid.org/0000-0003-2967-4028</orcidid><oa>free_for_read</oa></addata></record>
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subjects Artificial Intelligence
early-stage prostate cancer
Health Policy
Humans
LAPC
Linguistics - methods
localised prostate cancer
locally advanced prostate cancer
LPC
LPC/LAPC
Male
Natural Language Processing
Original Research
Perception
Prostatic Neoplasms - therapy
Social Media
title Perception of cure in prostate cancer: human-led and artificial intelligence-assisted landscape review and linguistic analysis of literature, social media and policy documents
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