Effectiveness and Cost-effectiveness of Artificial Intelligence-assisted Pathology for Prostate Cancer Diagnosis in Sweden: A Microsimulation Study.

Artificial intelligence Cost effectiveness Microsimulation Pathology Prostate cancer Sweden

Journal

European urology oncology
ISSN: 2588-9311
Titre abrégé: Eur Urol Oncol
Pays: Netherlands
ID NLM: 101724904

Informations de publication

Date de publication:
23 May 2024
Historique:
received: 05 03 2024
revised: 23 04 2024
accepted: 13 05 2024
medline: 25 5 2024
pubmed: 25 5 2024
entrez: 24 5 2024
Statut: aheadofprint

Résumé

Image-based artificial intelligence (AI) methods have shown high accuracy in prostate cancer (PCa) detection. Their impact on patient outcomes and cost effectiveness in comparison to human pathologists remains unknown. Our aim was to evaluate the effectiveness and cost-effectiveness of AI-assisted pathology for PCa diagnosis in Sweden. We modeled quadrennial prostate-specific antigen (PSA) screening for men between the ages of 50 and 74 yr over a lifetime horizon using a health care perspective. Men with PSA ≥3 ng/ml were referred for standard biopsy (SBx), for which cores were either examined via AI followed by a pathologist for AI-labeled positive cores, or a pathologist alone. The AI performance characteristics were estimated using an internal STHLM3 validation data set. Outcome measures included the number of tests, PCa incidence and mortality, overdiagnosis, quality-adjusted life years (QALYs), and the potential reduction in pathologist-evaluated biopsy cores if AI were used. Cost-effectiveness was assessed using the incremental cost-effectiveness ratio. In comparison to a pathologist alone, the AI-assisted workflow increased the number of PSA tests, SBx procedures, and PCa deaths by ≤0.03%, and slightly reduced PCa incidence and overdiagnosis. AI would reduce the proportion of biopsy cores evaluated by a pathologist by 80%. At a cost of €10 per case, the AI-assisted workflow would cost less and result in <0.001% lower QALYs in comparison to a pathologist alone. The results were sensitive to the AI cost. According to our model, AI-assisted pathology would significantly decrease the workload of pathologists, would not affect patient quality of life, and would yield cost savings in Sweden when compared to a human pathologist alone. We compared outcomes for prostate cancer patients and relevant costs for two methods of assessing prostate biopsies in Sweden: (1) artificial intelligence (AI) technology and review of positive biopsies by a human pathologist; and (2) a human pathologist alone for all biopsies. We found that addition of AI would reduce the pathology workload and save money, and would not affect patient outcomes when compared to a human pathologist alone. The results suggest that adding AI to prostate pathology in Sweden would save costs.

Sections du résumé

BACKGROUND AND OBJECTIVE OBJECTIVE
Image-based artificial intelligence (AI) methods have shown high accuracy in prostate cancer (PCa) detection. Their impact on patient outcomes and cost effectiveness in comparison to human pathologists remains unknown. Our aim was to evaluate the effectiveness and cost-effectiveness of AI-assisted pathology for PCa diagnosis in Sweden.
METHODS METHODS
We modeled quadrennial prostate-specific antigen (PSA) screening for men between the ages of 50 and 74 yr over a lifetime horizon using a health care perspective. Men with PSA ≥3 ng/ml were referred for standard biopsy (SBx), for which cores were either examined via AI followed by a pathologist for AI-labeled positive cores, or a pathologist alone. The AI performance characteristics were estimated using an internal STHLM3 validation data set. Outcome measures included the number of tests, PCa incidence and mortality, overdiagnosis, quality-adjusted life years (QALYs), and the potential reduction in pathologist-evaluated biopsy cores if AI were used. Cost-effectiveness was assessed using the incremental cost-effectiveness ratio.
KEY FINDINGS AND LIMITATIONS UNASSIGNED
In comparison to a pathologist alone, the AI-assisted workflow increased the number of PSA tests, SBx procedures, and PCa deaths by ≤0.03%, and slightly reduced PCa incidence and overdiagnosis. AI would reduce the proportion of biopsy cores evaluated by a pathologist by 80%. At a cost of €10 per case, the AI-assisted workflow would cost less and result in <0.001% lower QALYs in comparison to a pathologist alone. The results were sensitive to the AI cost.
CONCLUSIONS AND CLINICAL IMPLICATIONS CONCLUSIONS
According to our model, AI-assisted pathology would significantly decrease the workload of pathologists, would not affect patient quality of life, and would yield cost savings in Sweden when compared to a human pathologist alone.
PATIENT SUMMARY RESULTS
We compared outcomes for prostate cancer patients and relevant costs for two methods of assessing prostate biopsies in Sweden: (1) artificial intelligence (AI) technology and review of positive biopsies by a human pathologist; and (2) a human pathologist alone for all biopsies. We found that addition of AI would reduce the pathology workload and save money, and would not affect patient outcomes when compared to a human pathologist alone. The results suggest that adding AI to prostate pathology in Sweden would save costs.

Identifiants

pubmed: 38789385
pii: S2588-9311(24)00133-0
doi: 10.1016/j.euo.2024.05.004
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024. Published by Elsevier B.V.

Auteurs

Xiaoyang Du (X)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Electronic address: xiaoyang.du@ki.se.

Shuang Hao (S)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Henrik Olsson (H)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Kimmo Kartasalo (K)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Nita Mulliqi (N)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Balram Rai (B)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Dominik Menges (D)

Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.

Emelie Heintz (E)

Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden; Centre for Health Economics, Informatics and Health Services Research, Stockholm Health Care Services, Stockholm, Sweden.

Lars Egevad (L)

Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden.

Martin Eklund (M)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Mark Clements (M)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Classifications MeSH