Radiomic and Pathomic Study of Pituitary Adenoma Using Machine Learning

NCT05108064 | Unknown

• 1 other identifier: KY2021-005
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief Summary

Refractory pituitary adenoma is characterized by invasive tumor growth, continuous growth and/or hormone hypersecretion in spite of standardized multi-modal treatment such as surgeries, medications or radiations. Quality of life or even lives are threatened by these tumors. According to the 2017 World Health Organization's new classification guideline of pituitary adenoma, patients have to suffer from symptoms or complications caused by these tumors, to bear a heavy financial burden, and to accept additional therapeutic side effects when the diagnosis of "refractory pituitary adenoma" is made. If refractory pituitary adenoma could be predicted at early stage, these patients would be able to have a more frequent clinical follow-up, receive multiple effective treatment as early as possible, or even be enrolled in clinical trials of investigational medications, so as to prevent or delay the recurrence or persistent of the tumor growth. Therefore, the unmet clinical need falls into an early prediction system for refractory pituitary adenomas, which could provide accurate guidance for subsequent treatment in the early stage. The investigators have constructed a pituitary adenoma database including clinical data, radiological images, pathological images and genetic information. The investigators are proposing a study using machine learning to extract features from these multi-dimensional, multi-omics data, which could be further used to train a prediction model for the risk of refractory pituitary adenoma. The proposed model would also be validated in another prospectively collected database. The established model would be able to identify potential medication targets and provide guidance for personalized therapy of refractory pituitary adenoma.

Conditions

Pituitary Neoplasms

Outcome Measures

Primary Outcomes (1)

• The risk of refractory pituitary adenoma

  Predicting the development of refractory pituitary adenoma after the first surgery

  10 years

Secondary Outcomes (5)

• Predicting Gamma Knife efficacy

  5 years

• Predicting immunostaining

  Two weeks after surgery

• Predicting recurrence

  10 years

• Predicting endocrinopathy

  10 years

• Predicting surgical difficulty and complications

  Two weeks after surgery

Interventions

Artificial intelligence model DIAGNOSTIC_TEST

Results of artificial intelligence model will be compared with the gold standard

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All patients with pituitary adenoma who were not able to sign the informed consent.

You may qualify if:

• All patients with pituitary adenoma

You may not qualify if:

• Patients who were not able to sign the informed consent

Sponsors & Collaborators

• Huashan Hospital: lead

Study Sites (1)

Huashan Hospital

Shanghai, Shanghai Municipality, 200040, China

RECRUITING

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: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Professor

Study Record Dates

• First Submitted: August 2, 2021
• First Posted: November 4, 2021
• Study Start: January 1, 2019
• Primary Completion: December 31, 2024
• Study Completion: December 31, 2024
• Last Updated: September 29, 2022

Record last verified: 2022-09

Locations

CN (1)