Pre-operative Surgical Difficulty Stratification Using Predicted Tumor Perfusion and Consistency

NCT06664190 | Active Not Recruiting

• 1 other identifier: KY2022-709
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief Summary

Pituitary adenomas (PAs) are among the most prevalent lesions of the sella turcica, accounting for 10%-25% of all intracranial neoplasms. Pituitary macroadenomas (PMAs) are defined with a maximum diameter of over 1 cm. Tumor characteristics are key factors influencing surgical effectiveness and complications of PMAs, with tumor perfusion and consistency identified as major predictive factors in literature. Conventional sequences provide limited information for predicting the perfusion and consistency of pituitary adenomas. Advanced sequences offer additional insights. However, the efficacy of combining radiomic features from multiparametric sequences, incorporating both conventional and advanced sequences, has not yet been proved. 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.

Conditions

Pituitary Adenoma

Outcome Measures

Primary Outcomes (1)

• Extent of resection

  From enrollment to the end of treatment at 12 weeks

Secondary Outcomes (1)

• Severe postoperative complications

  From enrollment to the end of treatment at 12 weeks

Interventions

Advanced sequences, such as arterial spin labeling (ASL) and diffusion-weighted imaging (DWI) DIAGNOSTIC_TEST

Advanced sequences, such as arterial spin labeling (ASL) and diffusion-weighted imaging (DWI)

Eligibility Criteria

• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients histologically diagnosed with pituitary adenomas and who had undergone surgical treatment

You may qualify if:

• patients with tumor more than 2.5 cm of maximal diameter in the coronal plane
• Functional and non-functional pituitary tumors

You may not qualify if:

• incomplete image data

Sponsors & Collaborators

• Huashan Hospital: lead

Study Sites (1)

Huashan Hospital

Shanghai, Shanghai Municipality, 200000, China

Location

MeSH Terms

Conditions

Pituitary Neoplasms

Condition Hierarchy (Ancestors)

Endocrine Gland Neoplasms; Neoplasms by Site; Neoplasms; Hypothalamic Neoplasms; Supratentorial Neoplasms; Brain Neoplasms; Central Nervous System Neoplasms; Nervous System Neoplasms; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Hypothalamic Diseases; Pituitary Diseases; Endocrine System Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: October 27, 2024
• First Posted: October 29, 2024
• Study Start: August 1, 2022
• Primary Completion: January 1, 2025
• Study Completion: June 1, 2025
• Last Updated: October 29, 2024

Record last verified: 2024-10

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)