Clinical trial NCT05228197: Imperial Prostate 6 - Cancer Histology Artificial Intelligence Reliability Study.
Official title: A Study to Assess the Clinical and Cost-effectiveness of the Galen Prostate Artificial Intelligence Histology System in Diagnosing Clinically Important Prostate Cancer on Prostate Biopsy Tissue.
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Prostatic Neoplasms
Keywords: Prostate Cancer
Prostate-Specific Antigen
Artificial Intelligence
Histology
Lead sponsor: Imperial College London
Sponsor class: OTHER
Collaborators: name: National Institute for Health Research, United Kingdom
First posted: 2022-02-08
Enrollment: 750
Sex: MALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: PROBABILITY_SAMPLE
Study population: The target population is patients referred for a prostate biopsy as a result of an elevated serum PSA or abnormal digital rectal exam, who are undergoing or have already undergone prostate biopsies.

Brief summary: The primary objective is to determine whether the Galen Prostate AI system has sufficient diagnostic accuracy and health economic value to be used for triage of pathology slides within the NHS.

Detailed description: In the UK, about 80-100,000 men every year undergo prostate biopsy to diagnose prostate cancer. This equates to approximately 4 million histology slides; this is estimated to increase to 160,000-200,000 men and up to 6 million slides by 2030 due to rising numbers of men being tested for prostate cancer.

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.

Eligibility criteria: Inclusion Criteria:

* Patients with a prostate (either cis-male gender or trans-female gender with no prior hormone use at all).
* 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:

* Unwilling or unable to give consent.
* 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.

Primary outcomes:
measure: Galen Prostate AI
timeFrame: Maximum 6 weeks following enrolment
measure: Composite Health Outcome (Cost-Consequence Analysis)
timeFrame: Maximum 6 weeks following enrolment
measure: Composite Health Outcome (Cost-Utility Analysis)
timeFrame: Maximum 6 weeks following enrolment
Secondary outcomes:
measure: Galen Prostate AI (1)
timeFrame: Maximum 6 weeks following enrolment
measure: Galen Prostate AI (2)
timeFrame: Maximum 6 weeks following enrolment
measure: Galen Prostate AI (3)
timeFrame: Maximum 6 weeks following enrolment
measure: Galen Prostate AI (4)
timeFrame: Maximum 6 weeks following enrolment
measure: Galen Prostate AI (5)
timeFrame: Maximum 6 weeks following enrolment
measure: Galen Prostate AI (6)
timeFrame: Maximum 6 weeks following enrolment
measure: Galen Prostate AI (7)
timeFrame: Maximum 6 weeks following enrolment
measure: Galen Prostate AI (8)
timeFrame: Maximum 6 weeks following enrolment
measure: Pathology Reporting
timeFrame: Maximum 6 weeks following enrolment
measure: Databank Link
timeFrame: Maximum 6 weeks following enrolment
measure: Consent to Linkage
timeFrame: Maximum 6 weeks following enrolment
measure: Cost-Effectiveness
timeFrame: Maximum 6 weeks following enrolment
Other outcomes:
Locations:
city: Coventry
country: United Kingdom
facility: University Hospitals Coventry and Warwickshire Nhs Trust
city: London
country: United Kingdom
facility: Chelsea and Westminster Hospital Nhs Foundation Trust - Chelsea
city: London
country: United Kingdom
facility: Chelsea and Westminster Hospital Nhs Foundation Trust - West Middlesex
city: London
country: United Kingdom
facility: Imperial College Healthcare Nhs Trust
city: London
country: United Kingdom
facility: University College London Hospitals Nhs Foundation Trust
city: Southampton
country: United Kingdom
facility: University Hospital Southampton Nhs Foundation Trust
Study officials:
affiliation: Imperial College London
name: Hashim U Ahmed
role: PRINCIPAL_INVESTIGATOR
References: