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

• Published in: ESMO open Vol. 9; no. 5; p. 103007
• Main Authors: 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.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-05-2024; Elsevier
• 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; LPC/LAPC; LAPC; early-stage prostate cancer; localised prostate cancer; locally advanced prostate cancer; LPC
• ISSN: 2059-7029; 2059-7029
• DOI: 10.1016/j.esmoop.2024.103007

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.
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•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.