Development of Artificial Intelligence Models for Segmentation and Characterization of Prostate Cancer: a Single-center Retrospective Observational Study.
NCT ID: NCT06168864
Last Updated: 2023-12-13
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
350 participants
OBSERVATIONAL
2020-01-06
2022-06-01
Brief Summary
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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.
Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Interventions
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Artificial intelligence models for segmentation and characterization of prostate cancer
rtificial intelligence algorithms for the automatic segmentation of prostate cancer lesions on medical images.
Eligibility Criteria
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Inclusion Criteria
* Patients who performed a PET exam with 68 Ga-PMSA.
Exclusion Criteria
18 Years
MALE
No
Sponsors
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IRCCS San Raffaele
OTHER
Responsible Party
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Chiti Arturo
Professor in Diagnostic Imaging and Radiotherapy Faculty of Medicine and Surgery, Vita-Salute San Raffaele University Director, Department of Nuclear Medicine, IRCCS Ospedale San Raffaele
Locations
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Irccs San Raffaele
Milan, , Italy
Countries
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Other Identifiers
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AI_Pca
Identifier Type: -
Identifier Source: org_study_id