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

NCT06468332 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 400

Last updated 2025-01-31

No results posted yet for this study

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.

Conditions

Sponsors & Collaborators

  • Azienda Ospedaliero Universitaria Maggiore della Carita

    lead OTHER

Principal Investigators

  • Carlotta Palumbo, MD · University of Piemonte Orientale

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
MALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-10-30
Primary Completion
2025-12-31
Completion
2030-12-31

Countries

  • Italy

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06468332 on ClinicalTrials.gov