Radiomic and Pathomic Study of Pituitary Adenoma Using Machine Learning

Study Overview
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Brief Summary
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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.

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

Conditions
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Pituitary Neoplasms

Intervention / Treatment
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* Diagnostic Test: Artificial intelligence model


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: All patients with pituitary adenoma Exclusion Criteria: Patients who were not able to sign the informed consent

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Diagnostic Test: Artificial intelligence model|Results of artificial intelligence model will be compared with the gold standard|

What is the study measuring?
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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 |