Imperial Prostate 6 - Cancer Histology Artificial Intelligence Reliability Study.
NCT ID: NCT05228197
Last Updated: 2025-01-22
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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ACTIVE_NOT_RECRUITING
750 participants
OBSERVATIONAL
2022-03-11
2025-04-30
Brief Summary
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Detailed Description
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Health Education England and the Royal College of Pathology point to a significant pathology work-force shortage with only 3% of departments having adequate staffing levels and a 10% vacancy rate filled by locums costing £26M every year. By 2021, there will be a 3% decrease of the pathology consultant workforce (40 full-time pathologists); a period of time in which other specialties are expected to see a 13% increase. However, to meet the rising numbers of referrals to pathology departments, it is projected that there will need to be a 3-5% annual growth in the number of pathologists.
Inter-observer variability can occur between pathologists in terms of reporting a diagnosis of clinically important and clinically unimportant prostate cancer by as much as 20% although the differences are smaller when highly expert uro-pathologists are compared. This can lead to inappropriate management of cases.
Galen Prostate AI is a CE-marked deep learning AI-algorithm for prostate needle biopsies that can identify cell types, tissue structures and morphological features for cancer diagnosis. The technology is based on multi-layered convolutional neural networks (CNNs) designed for image classification in which whole-slide imaging is analysed for the detection of tissue areas and then benign versus cancer versus other pathology classification. Compared to almost all competitors, Galen Prostate AI has been tested in \~10 times more tissue samples. Further, Galen Prostate AI is the only algorithm that extends beyond cancer detection/grading to other clinically relevant features (e.g., perineural invasion, high-grade prostatic intraepithelial neoplasia \[PIN\], inflammation). This AI-algorithm is believed to be the only one in routine clinical deployment - demonstrating technical feasibility and with proven clinical utility.
The proposed study will perform validation in the NHS, for the first time. It is important to stress that this type of algorithm has never been tested on a UK-based population, and in particular, a population that includes a cohort of MRI targeted biopsies, which is now the new diagnostic strategy as it detects clinically relevant prostate cancer in higher percentages than the routine systematic biopsy.
The study is the first and only to address the performance of the AI-based prostate algorithm that extends beyond cancer detection and Gleason grading, by measuring amount of cancer and detecting clinically meaningful features such as perineural invasion in addition to multiple benign structures (e.g. HGPIN, atrophy, inflammation). Given the clinical relevance for such features in the diagnosis process, a study addressing their validation and performance is not only novel, but critical for implementation in routine clinical use.
Conditions
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Study Design
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COHORT
CROSS_SECTIONAL
Study Groups
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Calibration Stage
Patients referred to hospital urology departments by their GP due to a clinical suspicion of prostate cancer (elevated serum prostate specific antigen \[PSA\], abnormal feeling prostate on rectal examination). These patients are normally recommended to undergo a prostate MRI as part of standard care.
Patients will need to meet the Inclusion/Exclusion criteria but in addition, purposive identification of cases with a variety and representative sample of different pathology features are needed for this stage (e.g. normal glands, cancer glands, high-grade PIN, inflammation).
Biopsy & Imaging
H\&E stained prostate biopsy slides from standard of care treatment
Validation Stage
Patients referred to hospital urology departments by their GP due to a clinical suspicion of prostate cancer (elevated serum prostate specific antigen \[PSA\], abnormal feeling prostate on rectal examination). These patients are normally recommended to undergo a prostate MRI as part of standard care.
Patients will need to meet the Inclusion/Exclusion.
Biopsy & Imaging
H\&E stained prostate biopsy slides from standard of care treatment
Interventions
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Biopsy & Imaging
H\&E stained prostate biopsy slides from standard of care treatment
Eligibility Criteria
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Inclusion Criteria
* Age 18 years or above.
* Undergoing prostate biopsy as a result of an elevated serum PSA or abnormal digital rectal exam, who have undergone a pre-biopsy multi-parametric MRI and advised to undergo prostate biopsies.
(Please note: the Calibration stage requires patients who have already undergone a biopsy and the pathology has been processed over the prior 0 to 12 months).
Exclusion Criteria
* Any duration or type or dose of androgen deprivation therapy in the 6 months prior to screening.
* Any prior radiotherapy to the prostate or pelvis (including the prostate) or ablation or chemical treatment of the prostate for treating cancer: these types of treatment affect the anatomy of prostate tissue microstructure for which Galen Prostate AI is not currently validated. NB: any treatment for benign enlargement of the prostate is permitted.
18 Years
MALE
No
Sponsors
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National Institute for Health Research, United Kingdom
OTHER_GOV
Imperial College London
OTHER
Responsible Party
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Principal Investigators
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Hashim U Ahmed
Role: PRINCIPAL_INVESTIGATOR
Imperial College London
Locations
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University Hospitals Coventry and Warwickshire Nhs Trust
Coventry, , United Kingdom
Chelsea and Westminster Hospital Nhs Foundation Trust - Chelsea
London, , United Kingdom
Chelsea and Westminster Hospital Nhs Foundation Trust - West Middlesex
London, , United Kingdom
Imperial College Healthcare Nhs Trust
London, , United Kingdom
University College London Hospitals Nhs Foundation Trust
London, , United Kingdom
University Hospital Southampton Nhs Foundation Trust
Southampton, , United Kingdom
Countries
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Other Identifiers
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21CX6823
Identifier Type: -
Identifier Source: org_study_id
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