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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05108064
Other study ID # KY2021-005
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date January 1, 2019
Est. completion date December 31, 2024

Study information

Verified date September 2022
Source Huashan Hospital
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.


Recruitment information / eligibility

Status Recruiting
Enrollment 1000
Est. completion date December 31, 2024
Est. primary completion date December 31, 2024
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: - All patients with pituitary adenoma Exclusion Criteria: - Patients who were not able to sign the informed consent

Study Design


Related Conditions & MeSH terms


Intervention

Diagnostic Test:
Artificial intelligence model
Results of artificial intelligence model will be compared with the gold standard

Locations

Country Name City State
China Huashan Hospital Shanghai Shanghai

Sponsors (1)

Lead Sponsor Collaborator
Huashan Hospital

Country where clinical trial is conducted

China, 

Outcome

Type Measure Description Time frame Safety issue
Primary The risk of refractory pituitary adenoma Predicting the development of refractory pituitary adenoma after the first surgery 10 years
Secondary Predicting Gamma Knife efficacy Predicting endocrine remission after Gamma Knife surgery in Growth Hormone secreting pituitary adenoma 5 years
Secondary Predicting immunostaining Predicting immunostaining in patients with non-functioning pituitary adenoma using H&E stained images Two weeks after surgery
Secondary Predicting recurrence Predicting relapse or regrowth of a non-functioning pituitary adenoma after the first surgery 10 years
Secondary Predicting endocrinopathy Predicting endocrinopathy which warrant replacement after pituitary adenoma resection 10 years
Secondary Predicting surgical difficulty and complications Predicting surgical difficulty and complications using pre-surgical radiomic features Two weeks after surgery
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