Artificial Intelligence-based Platform, Integrating Pathologic, Imaging and Molecular Profiles of Prostate Cancer — PRISM-AI  

Prostate Cancer Clinical Trial

Official title: Development of Artificial Intelligence-based Multiplex Network for Individualized Risk Stratification of Prostate Cancer

Status Not yet recruiting

Clinical Trial Summary

The goal of this observational study is to use an artificial intelligence-based platform, integrating clinical, pathologic, imaging, genomic and transcriptomic profiles of prostate cancer in order to outperform currently available risk-stratification tools. Thus could lead to a better risk assessment of prostate cancer progression and recurrence. A key challenge in managing non-metastatic Prostate Cancer is identifying and distinguishing between men that are likely to progress to clinically significant disease and those whose disease is likely to remain indolent for the remainder of their lifetime, aiming to offer invasive treatment only to patients harboring a disease which would affect cancer specific survival. In the context of a multidisciplinary team of urologists and digital health experts, a two-phases study has been designed. A retrospective cohort of 200 radical prostatectomy patients will be identified within three participating clinical centres. Clinical, pathology, MRI data will be collected and stored in an appropriate anonymised online platform. Whole exome sequences (DNAseq) will be analyzed for each patients (total samples=200) and transcriptome analyses (RNAseq) for both cancer and non-cancer tissues (total samples=400). In parallel, the recruitment of a prospective cohort of 200 biopsy-proven newly PCa patients will start. For these patients, blood and urine samples will be also collected. Data will be collected and genetic analyses (total samples=1,000) will be performed as in the retrospective phase. Patients will be treated and followed according to best clinical practice. Expected Results The retrospective phase would allow to identify genes, pathological features and MRI imaging features that can correlate with PCa biology, in order to create and train the AI-based algorithm. The prospective phase will allow the validation of the prognostic tool, the definition of a novel risk grouping and the evaluation of the prognostic role of biofluid analysis.

Clinical Trial Description

n/a

Related Conditions & MeSH terms

• Prostate Cancer
• Prostatic Neoplasms

• NCT number: NCT06468332
• Study type: Observational
• Source: Azienda Ospedaliero Universitaria Maggiore della Carita
• Contact: Carlotta Palumbo, MD
• Phone: +393491289501
• Email: carlotta.palumbo@uniupo.it
• Status: Not yet recruiting
• Start date: July 2024
• Completion date: December 2030