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NCT05108064

Radiomic and Pathomic Study of Pituitary Adenoma Using Machine Learning

Status unknown Last updated 29 September 2022
What this trial tests

trial testing Artificial intelligence model in Pituitary Neoplasms in 1,000 participants. Status unknown.

Timeline
1 January 2019
Primary endpoint
31 December 2024
31 December 2024

Quick facts

Lead sponsorHuashan Hospital
StatusStatus unknown
Study typeOBSERVATIONAL
Enrollment1,000
Start date1 January 2019
Primary completion31 December 2024
Estimated completion31 December 2024
Sites1 location across China

Drugs / interventions tested

Conditions studied

Sponsor

Huashan Hospital

Who can join

18 and older, any sex, with Pituitary Neoplasms. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

1 peer-reviewed publication reference this trial (live from Europe PMC):

  1. Translational Bioinformatics Applied to the Study of Complex Diseases.
    Casotti MC, Meira DD, Alves LNR, Bessa BGO, et al · · 2023 · cited 12× · PMID 36833346 · DOI 10.3390/genes14020419

Verify or expand the search:

Other trials of Artificial intelligence model

Trials testing the same drug.

Other recruiting trials for Pituitary Neoplasms

Currently open trials in the same condition.

Other Huashan Hospital trials

Trials by the same sponsor.

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