Development and retrospective validation of an artificial intelligence system for diagnostic assessment of prostate biopsies: study protocol.
Authors
Affiliations (27)
Affiliations (27)
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
- Department of Pathology, Stavanger University Hospital, Stavanger, Norway.
- Faculty of Health Sciences, University of Stavanger, Stavanger, Norway.
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
- Institute of Biomedicine, University of Turku, Turku, Finland.
- The General Practice and Care Coordination Research Group, Stavanger University Hospital, Stavanger, Norway.
- Department of Global Public Health and Primary Care, Faculty of Medicine, University of Bergen, Bergen, Norway.
- Department of Pathology, SYNLAB, Madrid, Spain.
- Department of Pathology, SYNLAB, Brescia, Italy.
- Department of Pathology, Chair of Oncology, Medical University of Lodz, Lodz, Poland.
- 1st Department of Urology, Medical University of Lodz, Lodz, Poland.
- Department of Clinical Chemistry, University of Helsinki, Helsinki, Finland.
- Laboratory Services, Mehiläinen Oy, Helsinki, Finland.
- Mehiläinen Länsi-Pohja Hospital, Kemi, Finland.
- Department of Radiology, Aarhus University Hospital, Aarhus, Denmark.
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark.
- Department of Pathology, Aarhus University Hospital, Aarhus, Denmark.
- InFLAMES Research Flagship, University of Turku, Turku, Finland.
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- Malaghan Institute of Medical Research, Wellington, New Zealand.
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden.
- Aquesta Uropathology and University of Queensland, Brisbane, Queensland, Australia.
- Department of Surgical Pathology, School of Medicine, Aichi Medical University, Nagoya, Japan.
- Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, Stavanger, Norway.
- Institute for Biomedicine and Glycomics, Griffith University, Brisbane, Queensland, Australia.
- Department of Medical Epidemiology and Biostatistics, SciLifeLab, Karolinska Institutet, Stockholm, Sweden [email protected].
Abstract
Histopathological evaluation of prostate biopsies using the Gleason scoring system is critical for prostate cancer diagnosis and treatment selection. However, grading variability among pathologists can lead to inconsistent assessments, risking inappropriate treatment. Similar challenges complicate the assessment of other prognostic features like cribriform cancer morphology and perineural invasion. Many pathology departments are also facing an increasingly unsustainable workload due to rising prostate cancer incidence and a decreasing pathologist workforce coinciding with increasing requirements for more complex assessments and reporting. Digital pathology and artificial intelligence (AI) algorithms for analysing whole slide images show promise in improving the accuracy and efficiency of histopathological assessments. Studies have demonstrated AI's capability to diagnose and grade prostate cancer comparably to expert pathologists. However, external validations on diverse data sets have been limited and often show reduced performance. Historically, there have been no well-established guidelines for AI study designs and validation methods. Diagnostic assessments of AI systems often lack preregistered protocols and rigorous external cohort sampling, essential for reliable evidence of their safety and accuracy. This study protocol covers the retrospective validation of an AI system for prostate biopsy assessment. The primary objective of the study is to develop a high-performing and robust AI model for diagnosis and Gleason scoring of prostate cancer in core needle biopsies, and at scale evaluate whether it can generalise to fully external data from independent patients, pathology laboratories and digitalisation platforms. The secondary objectives cover AI performance in estimating cancer extent and detecting cribriform prostate cancer and perineural invasion. This protocol outlines the steps for data collection, predefined partitioning of data cohorts for AI model training and validation, model development and predetermined statistical analyses, ensuring systematic development and comprehensive validation of the system. The protocol adheres to Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis+AI (TRIPOD+AI), Protocol Items for External Cohort Evaluation of a Deep Learning System in Cancer Diagnostics (PIECES), Checklist for AI in Medical Imaging (CLAIM) and other relevant best practices. Data collection and usage were approved by the respective ethical review boards of each participating clinical laboratory, and centralised anonymised data handling was approved by the Swedish Ethical Review Authority. The study will be conducted in agreement with the Helsinki Declaration. The findings will be disseminated in peer-reviewed publications (open access).