Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge

NCT ID: NCT05489341

Last Updated: 2023-11-18

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 10207 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-02-01
• Study Completion Date: 2023-11-01

Brief Summary

The PI-CAI challenge aims to validate the diagnostic performance of artificial intelligence (AI) and radiologists at clinically significant prostate cancer (csPCa) detection/diagnosis in MRI, with respect to histopathology and follow-up (≥ 3 years) as reference. The study hypothesizes that state-of-the-art AI algorithms, trained using thousands of patient exams, are non-inferior to radiologists reading bpMRI. As secondary end-points, it investigates the optimal AI model for csPCa detection/diagnosis, and the effects of dynamic contrast-enhanced imaging and reader experience on diagnostic accuracy and inter-reader variability.

Detailed Description

Prostate cancer (PCa) is one of the most prevalent cancers in men worldwide. One million men receive a diagnosis and 300,000 die from clinically significant PCa (csPCa) (defined as ISUP≥2), each year, worldwide. Multiparametric magnetic resonance imaging (mpMRI) is playing an increasingly important role in the early diagnosis of prostate cancer, and has been recommended by the European Association of Urology (EAU), prior to biopsies. However, current guidelines for reading prostate mpMRI (i.e. PI-RADS v2.1) follow a semi-quantitative assessment that mandates substantial expertise for proper usage. This can lead to low inter-reader agreement (<50%), sub-optimal interpretation and overdiagnosis.

Modern artificial intelligence (AI) algorithms have paved the way for powerful computer-aided detection and diagnosis (CAD) systems that rival human performance in medical image analysis. Clinical trials are the gold standard for assessing new medications and interventions in a controlled and comparative manner, and the equivalent for developing AI algorithms are international competitions or "grand challenges", where increasingly large datasets are released to public to solve clinically relevant tasks with AI. Grand challenges can address the lack of trust, scientific evidence and adequate validation among AI solutions, by providing the means to compare algorithms against each other using common datasets and a unified experimental setup.

PI-CAI (Prostate Imaging: Cancer AI) is an all-new grand challenge, with over 10,000 carefully-curated prostate MRI exams to validate modern AI algorithms and estimate radiologists' performance at csPCa detection and diagnosis. Key aspects of the study design have been established in conjunction with an international, multi-disciplinary scientific advisory board (16 experts in prostate AI, radiology and urology) -to unify and standardize present-day guidelines, and to ensure meaningful validation of prostate-AI towards clinical translation.

The 2022 edition of PI-CAI will focus on validating AI at automated 3D detection and diagnosis of csPCa in bpMRI. PI-CAI primarily consists of two sub-studies:

• AI Study (Grand Challenge): An annotated multi-center, multi-vendor dataset of 1500 bpMRI exams (including their clinical and acquisition variables) is made publicly available for all participating teams and the research community at large. Teams can use this dataset to develop AI models, and submit their trained algorithms (in Docker containers) for evaluation. At the end of this open development phase, all algorithms are ranked, based on their performance on a hidden testing cohort of 1000 unseen scans. In the closed testing phase, organizers retrain the top-ranking 5 AI algorithms using a larger dataset of 7500-9500 bpMRI scans (including additional training scans from a private dataset). Finally, their performance is re-evaluated on the hidden testing cohort (with rigorous statistical analyses), to determine the top 3 AI algorithms for automated 3D detection and diagnosis of csPCa in bpMRI (i.e. the winners of the grand challenge).
• Reader Study: 50+ international prostate radiologists perform a reader study using a subset of 400 scans from the hidden testing cohort. For each case, radiologists complete their assessments in two rounds. At first, using clinical and acquisition variables plus bpMRI sequences only, enabling head-to-head comparisons against AI trained on the same. And then, using clinical and acquisition variables plus full mpMRI sequences, enabling comparisons between AI and current clinical practice (PI-RADS v2.1). Overall, the goal of this study is to estimate the performance of the average radiologist at detection and diagnosis of csPCa in MRI.

In the end, PI-CAI aims to benchmark state-of-the-art AI algorithms developed in the grand challenge, against prostate radiologists participating in the reader study -to evaluate the clinical viability of modern prostate-AI solutions at csPCa detection and diagnosis in MRI.

Conditions

Prostate Cancer

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Public Training and Development Set (1500 cases)

Available for all participants and researchers, to train and develop AI models. Includes multi-vendor (Siemens Healthineers, Philips Medical Systems) prostate bpMRI cases from three Dutch centers (Radboud University Medical Center, Ziekenhuisgroep Twente, University Medical Center Groningen), acquired between 2012-2021. All data is fully anonymized and made available under a non-commercial CC BY-NC 4.0 license. Includes 328 cases from the PROSTATEx challenge (prostatex.grand-challenge.org). Imaging data has been released via: zenodo.org/record/6624726 (DOI: 10.5281/zenodo.6624726). Lesion annotations of csPCa have been released and are maintained via: github.com/DIAGNijmegen/picai\_labels.

Histopathology and Magnetic Resonance Imaging

Intervention Type: DIAGNOSTIC_TEST

Reference standard establishes histologically-confirmed (ISUP ≥ 2) cases of csPCa as positives, and histopathology- (ISUP ≤ 1) or MRI- (PI-RADS ≤ 2) confirmed cases of indolent PCa or benign tissue as negatives.

Private Training Set (7500-9500 cases)

Used exclusively by the organizers to retrain the top-ranking 5 AI algorithms, with large-scale data. Includes multi-vendor (Siemens Healthineers, Philips Medical Systems) prostate bpMRI cases from three Dutch centers (Radboud University Medical Center, Ziekenhuisgroep Twente, University Medical Center Groningen), acquired between 2012-2021.

Histopathology and Magnetic Resonance Imaging

Intervention Type: DIAGNOSTIC_TEST

Reference standard establishes histologically-confirmed (ISUP ≥ 2) cases of csPCa as positives, and histopathology- (ISUP ≤ 1) or MRI- (PI-RADS ≤ 2) confirmed cases of indolent PCa or benign tissue as negatives.

Hidden Validation and Tuning Cohort (100 cases)

Used for a live, public leaderboard that enables AI model selection and tuning throughout the open development phase of the challenge. Includes multi-vendor (Siemens Healthineers, Philips Medical Systems) prostate bpMRI cases from three Dutch centers (Radboud University Medical Center, Ziekenhuisgroep Twente, University Medical Center Groningen), acquired between 2012-2021, that remain fully hidden throughout the course of the challenge.

Histopathology and Magnetic Resonance Imaging with Follow-Up

Intervention Type: DIAGNOSTIC_TEST

Reference standard establishes histologically-confirmed (ISUP ≥ 2) cases of csPCa as positives, and histopathology- (ISUP ≤ 1) or MRI- (PI-RADS ≤ 2) with follow-up (≥ 3 years) confirmed cases of indolent PCa or benign tissue as negatives.

Hidden Testing Cohort (1000 cases)

Used to benchmark AI, radiologists, and test all hypotheses at the end of the PI-CAI challenge. A subset of 400 cases from this cohort is used to facilitate the PI-CAI: Reader Study. Includes multi-vendor (Siemens Healthineers, Philips Medical Systems) internal testing data (unseen prostate bpMRI cases from three seen Dutch centers {Radboud University Medical Center, Ziekenhuisgroep Twente, University Medical Center Groningen}) and external testing data (unseen prostate bpMRI cases from one unseen Norwegian center {Norwegian University of Science and Technology}), acquired between 2012-2021.

Histopathology and Magnetic Resonance Imaging with Follow-Up

Intervention Type: DIAGNOSTIC_TEST

Reference standard establishes histologically-confirmed (ISUP ≥ 2) cases of csPCa as positives, and histopathology- (ISUP ≤ 1) or MRI- (PI-RADS ≤ 2) with follow-up (≥ 3 years) confirmed cases of indolent PCa or benign tissue as negatives.

Interventions

Histopathology and Magnetic Resonance Imaging with Follow-Up

Reference standard establishes histologically-confirmed (ISUP ≥ 2) cases of csPCa as positives, and histopathology- (ISUP ≤ 1) or MRI- (PI-RADS ≤ 2) with follow-up (≥ 3 years) confirmed cases of indolent PCa or benign tissue as negatives.

Intervention Type: DIAGNOSTIC_TEST

Histopathology and Magnetic Resonance Imaging

Reference standard establishes histologically-confirmed (ISUP ≥ 2) cases of csPCa as positives, and histopathology- (ISUP ≤ 1) or MRI- (PI-RADS ≤ 2) confirmed cases of indolent PCa or benign tissue as negatives.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Men suspected of harboring csPCa, with elevated levels of prostate-specific antigen (≥ 3 ng/mL) and/or abnormal findings on digital rectal exam, who subsequently underwent prostate MRI.

Exclusion Criteria

• Patients who opted-out or did not give permission to reuse clinical data.
• Patients with a history of prior prostate treatment.
• Patients with a history of prior positive csPCa findings in histopathology (ISUP ≥ 2).
• Patients whose prostate MRI exhibit severe artifacts (e.g. heavy warping due to rectal air, metal artifacts from hip prostheses, heavy motion blur), thereby impeding their usage.
• Patients, whose positive histopathology findings (ISUP ≥ 2) cannot be reliably localized on MRI (e.g. MRI-invisible lesions, systematic biopsy diagnostic reports with ambiguous, "random" or missing location information).

• Minimum Eligible Age: 18 Years
• Eligible Sex: MALE
• Accepts Healthy Volunteers: Yes

Sponsors

Ziekenhuisgroep Twente

OTHER

Sponsor Role: collaborator

University Medical Center Groningen

OTHER

Sponsor Role: collaborator

Norwegian University of Science and Technology

OTHER

Sponsor Role: collaborator

Radboud University Medical Center

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Henkjan Huisman, PhD

Role: PRINCIPAL_INVESTIGATOR

Radboud University Medical Center

Locations

RadboudUMC

Nijmegen, Gelderland, Netherlands

Countries

Netherlands

References

Saha A, Bosma JS, Twilt JJ, van Ginneken B, Bjartell A, Padhani AR, Bonekamp D, Villeirs G, Salomon G, Giannarini G, Kalpathy-Cramer J, Barentsz J, Maier-Hein KH, Rusu M, Rouviere O, van den Bergh R, Panebianco V, Kasivisvanathan V, Obuchowski NA, Yakar D, Elschot M, Veltman J, Futterer JJ, de Rooij M, Huisman H; PI-CAI consortium. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study. Lancet Oncol. 2024 Jul;25(7):879-887. doi: 10.1016/S1470-2045(24)00220-1. Epub 2024 Jun 11.

Reference Type: DERIVED

PMID: 38876123 (View on PubMed)

Study Documents

Document Type: Individual Participant Data Set

Imaging for the PI-CAI: Public Training and Development Dataset, containing 1500 fully-anonymized prostate bpMRI scans from 1476 patients, acquired between 2012-2021, at three Dutch centers (Radboud University Medical Center, University Medical Center Groningen, Ziekenhuisgroep Twente).

View Document

Document Type: Study Protocol

Preregistration of the PI-CAI challenge study design, in compliance with BIAS reporting guidelines (https://www.equator-network.org/reporting-guidelines/bias-transparent-reporting-of-biomedical-image-analysis-challenges/).

View Document

Document Type: Analytic Code

Source code for preprocessing prostate MRI data archives.

View Document

Document Type: Analytic Code

Source code for training baseline diagnostic AI models.

View Document

Document Type: Analytic Code

Source code for evaluating csPCa detection and diagnosis performance, and performing all statistical tests with respect to the same.

View Document

Document Type: Individual Participant Data Set

Annotations for the PI-CAI: Public Training and Development Dataset, containing basic clinical and acquisition variables, csPCa annotations and outcomes for 1500 fully-anonymized prostate bpMRI exams from 1476 patients, acquired between 2012-2021, at three Dutch centers (Radboud University Medical Center, University Medical Center Groningen, Ziekenhuisgroep Twente).

View Document

Related Links

https://pi-cai.grand-challenge.org/

Official website.

Other Identifiers

CMO2016-3045-Project-20011

Identifier Type: -

Identifier Source: org_study_id