Pre-operative Surgical Difficulty Stratification Using Predicted Tumor Perfusion and Consistency
NCT ID: NCT06664190
Last Updated: 2024-10-29
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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ACTIVE_NOT_RECRUITING
200 participants
OBSERVATIONAL
2022-08-01
2025-06-01
Brief Summary
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We aim to develop machine learning models that combines radiomic features developed from both conventional and advanced sequences to predict the perfusion and consistency of PMAs. Furthermore, we aim to demonstrate the clinically applicability of these models by constructing a MR-PIT stratification (Multiparametric Radiomic derived and tumor Perfusion and consIsTency based surgical difficulty stratification), which correlated with the surgical strategy and outcomes.
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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Advanced sequences, such as arterial spin labeling (ASL) and diffusion-weighted imaging (DWI)
Advanced sequences, such as arterial spin labeling (ASL) and diffusion-weighted imaging (DWI)
Eligibility Criteria
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Inclusion Criteria
* Functional and non-functional pituitary tumors
Exclusion Criteria
ALL
No
Sponsors
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Huashan Hospital
OTHER
Responsible Party
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Zhaoyun Zhang
Professor
Locations
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Huashan Hospital
Shanghai, Shanghai Municipality, China
Countries
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Other Identifiers
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KY2022-709
Identifier Type: -
Identifier Source: org_study_id
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