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.

Inclusion Criteria:

• Adults patients, aged between 18 and 80 years
• Signed an informed consent form (ICF) indicating that the subject or his closest relative understands the purpose of and procedures required for the study and is willing to participate in the study; subjects must be willing to allow MRI anonymized revision and processing and biopsy/Radical Prostatectomy (RP) material genomic/transcriptomic and exploratory analyses. Moreover they must be willing to adhere to normal clinical follow-up.
• Availability of MRI conducted prior to RP, with at least T2 weighted image (T2W), diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) sequences in accordance with the American College of Radiology standards for Prostate Imaging-Reporting and Data System (PI-RADS) evaluation. This criterion is not mandatory for the metastatic cohort.
• Availability of formalin-fixed paraffin-embedded (FFPE) radical prostatectomy specimen for genomic, transcriptomic and exploratory analyses. Prostate or metastasis biopsy FFPE are acceptable for the metastatic cohort.
• Histological diagnosis of adenocarcinoma of the prostate
• Availability of PSA dosage and clinical evaluation of the tumor (via digital rectal exam [DRE]) in the 3 months preceding surgery (except for the metastatic cohort, where surgery does not apply).
• Availability of at least one postoperative prostate specific antigen (PSA) in between 3 and 8 weeks after surgery (except for the metastatic cohort, where surgery does not apply).
• Minimal follow-up duration of 2 years (or until death) after surgery (or after diagnosis for the metastatic patient).

Exclusion Criteria:

• Bilateral orchiectomy
• Neoadjuvant hormone therapy or any prostate cancer-directed therapy before radical prostatectomy
• History of pelvic radiation before RP.
• Active malignancy in the last 24 months, excluding Prostate Cancer, Non-muscle-invasive Bladder Cancer (NMIBC), cured skin cancer (excluding melanoma) or other malignancies with minimal risk of recurrence.
• Active surveillance lasting more than 12 months

multi-centre cohort of patients from division of urology