Development of Artificial Intelligence Models for Segmentation and Characterization of Prostate Cancer: a Single-center Retrospective Observational Study.

Prostate Cancer Clinical Trial

Official title:

Development of Artificial Intelligence Models for Segmentation and Characterization of Prostate Cancer: a Single-center Retrospective Observational Study.

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06168864
• Other study ID #: AI_Pca
• Status: Completed
• Start date: January 6, 2020
• Est. completion date: June 1, 2022

Study information

• Verified date: December 2023
• Source: IRCCS San Raffaele
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 350
• Est. completion date: June 1, 2022
• Est. primary completion date: June 1, 2022
• Gender: Male
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Patients with histological diagnosis of prostate cancer;

• Patients who performed a PET exam with 68 Ga-PMSA.

Exclusion Criteria:

• CT and MR images with artifacts that preclude interpretation of results.

Related Conditions & MeSH terms

• Prostate Cancer
• Prostatic Neoplasms

Intervention

Diagnostic Test:
• Artificial intelligence models for segmentation and characterization of prostate cancer
rtificial intelligence algorithms for the automatic segmentation of prostate cancer lesions on medical images.

Locations

Country	Name	City	State
Italy	Irccs San Raffaele	Milano

Sponsors (1)

Lead Sponsor	Collaborator
IRCCS San Raffaele

Country where clinical trial is conducted

Italy, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Artificial intelligence algorithms for the classification of prostate cancer lesions on medical images.	PET images from enrolled patients will be used to create models that investigate the ability of artificial intelligence to automate tumor segmentation tasks.	2 years