Actively Recruiting
Artificial Intelligence-based Platform, Integrating Pathologic, Imaging and Molecular Profiles of Prostate Cancer
Led by Azienda Ospedaliero Universitaria Maggiore della Carita · Updated on 2025-01-31
400
Participants Needed
1
Research Sites
317 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
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
Official Title
Artificial Intelligence-based Platform, Integrating Pathologic, Imaging and Molecular Profiles of Prostate Cancer
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Adult male patients aged between 18 and 80 years
- Signed informed consent form showing understanding of study and willingness to participate
- Willing to allow anonymized MRI review and genomic/transcriptomic analyses of biopsy or prostatectomy samples
- Availability of MRI prior to surgery including T2 weighted, diffusion-weighted imaging, and dynamic contrast-enhanced sequences (not mandatory for metastatic patients)
- Availability of formalin-fixed paraffin-embedded radical prostatectomy specimen or biopsy for metastatic patients for genetic analyses
- Histological diagnosis of prostate adenocarcinoma
- Availability of PSA measurement and clinical tumor evaluation within 3 months before surgery (not for metastatic patients)
- At least one postoperative PSA measurement between 3 and 8 weeks after surgery (not for metastatic patients)
- Minimum follow-up duration of 2 years or until death after surgery or diagnosis for metastatic patients
You will not qualify if you...
- History of bilateral orchiectomy
- Received neoadjuvant hormone therapy or any prostate cancer-directed therapy before radical prostatectomy
- History of pelvic radiation before surgery
- Active malignancy within last 24 months except prostate cancer, non-muscle-invasive bladder cancer, cured skin cancer (excluding melanoma), or other low-risk malignancies
- Active surveillance lasting more than 12 months
AI-Screening
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Trial Site Locations
Total: 1 location
1
AOU Maggiore della Carità, Urology Division
Novara, Italy, 28100
Actively Recruiting
Research Team
C
Carlotta Palumbo, MD
CONTACT
L
Livia Salmi, PhD
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
5
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