Radiomic and Pathomic Study of Pituitary Adenoma Using Machine Learning

Pituitary Neoplasms Clinical Trial

Official title:

Machine Learning Modeling the Risk of Refractory Pituitary Adenoma Using Radiomic and Pathomic Data

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05108064
• Other study ID #: KY2021-005
• Status: Recruiting
• 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
• 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

Related Conditions & MeSH terms

• Adenoma
• Pituitary Diseases
• Pituitary Neoplasms

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