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NCT06168864
Development of Artificial Intelligence Models for Segmentation and Characterization of Prostate Cancer: a Single-center Retrospective Observational Study.
trial testing Artificial intelligence models for segmentation and characterization of prostate cancer in Prostate Cancer in 350 participants. Completed in 1 June 2022.
1 June 2022
Quick facts
| Lead sponsor | IRCCS San Raffaele |
|---|---|
| Status | Completed |
| Study type | OBSERVATIONAL |
| Enrollment | 350 |
| Start date | 6 January 2020 |
| Primary completion | 1 June 2022 |
| Estimated completion | 1 June 2022 |
| Sites | 1 location across Italy |
Drugs / interventions tested
- Artificial intelligence models for segmentation and characterization of prostate cancer
Conditions studied
- Prostate Cancer — all drugs for Prostate Cancer →
Sponsor
IRCCS San Raffaele — full company profile →
Who can join
18 and older, male only, with Prostate Cancer. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
Prostate cancer is the second most common cancer in the male population. This pathology represents an oncological and public health problem especially in developed countries, due to a greater presence of elderly men in the population. Medical imaging plays a central role in the staging and restaging of prostate disease. Magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET) are among the methods commonly used in normal clinical practice for the characterization of prostate cancer. To date, the study of these images is limited to a qualitative visual analysis, however there is increasing evidence relating to the usefulness of introducing a quantitative (or semi-quantitative) analysis of biomedical images. The current increase in available imaging data, and their quality, allows the application of artificial intelligence methods also in the medical field for the automation of tasks (e.g. automatic segmentation) and classification (e.g. tumor aggressiveness). The extraction of quantitative data, and more generally the study of tumor lesions, requires manual segmentation by one or more doctors. This process requires very long times as each image must be processed individually; furthermore, the result also depends on the level of experience of the doctor carrying out the segmentation and this could create a source of heterogeneity, affecting the reproducibility of the segmentation. AI-based automatic segmentation methods can be applied to medical images for the localization of tumor lesions, thus exceeding the limits of manual segmentation.
Publications & conference data
No peer-reviewed publications indexed yet for this trial. Completed trials usually publish results within 12-18 months.
Verify or expand the search:
- PubMed search for NCT06168864
- Europe PMC full search
- ASCO Meeting Library
- ESMO Meeting Library
- bioRxiv preprints
- medRxiv preprints
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Related trials
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Currently open trials in the same condition.
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- NCT07426094 — PRO-BOOST-N: Prostate-First Versus Combined Prostate and Nodal Dose Escalation in PSMA PET-Staged Node-Positive Prostate · Phase 2, PHASE3 · recruiting
Other IRCCS San Raffaele trials
Trials by the same sponsor.
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT06168864 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by IRCCS San Raffaele
- Last refreshed: 13 December 2023
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT06168864.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing