Radiomic and Pathomic Study of Pituitary Adenoma Using Machine Learning

NCT ID: NCT05108064

Last Updated: 2022-09-29

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 1000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2019-01-01
• Study Completion Date: 2024-12-31

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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: OTHER

Interventions

Artificial intelligence model

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

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• All patients with pituitary adenoma

Exclusion Criteria

• Patients who were not able to sign the informed consent

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Huashan Hospital

OTHER

Sponsor Role: lead

Responsible Party

Zhaoyun Zhang

Clinical Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Huashan Hospital

Shanghai, Shanghai Municipality, China

Site Status: RECRUITING

Countries

China

Facility Contacts

Yao Zhao

Role: primary

zhaoyaohs@vip.sina.com

86-13916872553

Other Identifiers

KY2021-005

Identifier Type: -

Identifier Source: org_study_id