Radiomic and Pathomic Study of Pituitary Adenoma Using Machine Learning

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

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.

Study Overview

• Status: Recruiting
• Conditions: Pituitary Neoplasms
• Intervention / Treatment: Diagnostic test: Artificial intelligence model

• Study Type: Observational
• Enrollment (Anticipated): 1000

Contacts and Locations

Study Locations

• China
  • Shanghai
    • Shanghai, Shanghai, China, 200040
      • Recruiting
      • Huashan Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

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

Description

Inclusion Criteria:

• All patients with pituitary adenoma

Exclusion Criteria:

• Patients who were not able to sign the informed consent

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The risk of refractory pituitary adenoma	Predicting the development of refractory pituitary adenoma after the first surgery	10 years

Secondary Outcome Measures

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

Collaborators and Investigators

• Sponsor: Huashan Hospital

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2019
• Primary Completion (Anticipated): December 31, 2024
• Study Completion (Anticipated): December 31, 2024

Study Registration Dates

• First Submitted: August 2, 2021
• First Submitted That Met QC Criteria: October 24, 2021
• First Posted (Actual): November 4, 2021

Study Record Updates

• Last Update Posted (Actual): September 29, 2022
• Last Update Submitted That Met QC Criteria: September 28, 2022
• Last Verified: September 1, 2022

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Neoplasms by Histologic Type; Neoplasms; Neoplasms by Site; Neoplasms, Glandular and Epithelial; Endocrine System Diseases; Endocrine Gland Neoplasms; Hypothalamic Diseases; Hypothalamic Neoplasms; Supratentorial Neoplasms; Brain Neoplasms; Central Nervous System Neoplasms; Nervous System Neoplasms; Adenoma; Pituitary Neoplasms; Pituitary Diseases
• Other Study ID Numbers: KY2021-005

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No