Development of Artificial Intelligence Models for Segmentation and Characterization of Prostate Cancer: a Single-center Retrospective Observational Study.
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.
Study Overview
Status
Conditions
Study Type
Enrollment (Actual)
Contacts and Locations
Study Locations
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Milano, Italy, 20132
- IRCCS San Raffaele
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
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.
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Artificial intelligence algorithms for the classification of prostate cancer lesions on medical images.
Time Frame: 2 years
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PET images from enrolled patients will be used to create models that investigate the ability of artificial intelligence to automate tumor segmentation tasks.
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2 years
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Collaborators and Investigators
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- AI_Pca
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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