Pituitary NeoplasmsNCT05108064
Radiomic and Pathomic Study of Pituitary Adenoma Using Machine Learning
An observational study of Pituitary Neoplasms, run by Huashan Hospital at 1 site in China.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Pituitary Neoplasms
- What is being tested?
- Artificial intelligence model
- How many people
- 1,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2019to 31 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: All patients with pituitary adenoma who were not able to sign the informed consent.
Who may be able to join
- All patients with pituitary adenoma
Who cannot join
- Patients who were not able to sign the informed consent
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Huashan Hospital
Shanghai, Shanghai Municipality, 200040
Yao Zhao, 86-13916872553, zhaoyaohs@vip.sina.com
Yao Zhao
About this study
Official title: 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.
- Conditions
- Pituitary Neoplasms
- Being tested
- Artificial intelligence model
- Medical subject headings
- Pituitary Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence modelDiagnostic test
Results of artificial intelligence model will be compared with the gold standard
What is being measured
The primary outcome is the main question the study is designed to answer.
The risk of refractory pituitary adenoma
Predicting the development of refractory pituitary adenoma after the first surgery
Measured: 10 years
Secondary outcomes (5)
Predicting Gamma Knife efficacy
Predicting endocrine remission after Gamma Knife surgery in Growth Hormone secreting pituitary adenoma
Measured: 5 years
Predicting immunostaining
Predicting immunostaining in patients with non-functioning pituitary adenoma using H\&E stained images
Measured: Two weeks after surgery
Predicting recurrence
Predicting relapse or regrowth of a non-functioning pituitary adenoma after the first surgery
Measured: 10 years
Predicting endocrinopathy
Predicting endocrinopathy which warrant replacement after pituitary adenoma resection
Measured: 10 years
Predicting surgical difficulty and complications
Predicting surgical difficulty and complications using pre-surgical radiomic features
Measured: Two weeks after surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Nov 2021
- Study start1 Jan 2019
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pituitary NeoplasmsStudies of the same condition, open and under way.
- More trials run by Huashan HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Huashan Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Zhaoyun Zhang, Huashan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05108064
- Sponsor's protocol ID
- KY2021-005
- Status last verified
- Sep 2022
- First submitted
- 2 Aug 2021
- Last updated
- 29 Sep 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 29 Sep 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning Modeling the Risk of Refractory Pituitary Adenoma Using Radiomic and Pathomic Data
- ClinicalTrials.gov ID
- NCT05108064
- Acronym / sponsor ID
- KY2021-005
- Conditions
- Pituitary Neoplasms
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Huashan HospitalUniversity, hospital or other
- Study start
- 1 Jan 2019 (actual)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 4 Nov 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Other
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- All patients with pituitary adenoma who were not able to sign the informed consent.
Key inclusion criteria (1 listed)
- All patients with pituitary adenoma
Key exclusion criteria (1 listed)
- Patients who were not able to sign the informed consent
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial intelligence modelResults of artificial intelligence model will be compared with the gold standard | Diagnostic test | – |
Primary outcome
The risk of refractory pituitary adenoma
Time frame: 10 years
Predicting the development of refractory pituitary adenoma after the first surgery
5 secondary outcomes
Predicting Gamma Knife efficacy
Time frame: 5 years
Predicting immunostaining
Time frame: Two weeks after surgery
Predicting recurrence
Time frame: 10 years
Predicting endocrinopathy
Time frame: 10 years
Predicting surgical difficulty and complications
Time frame: Two weeks after surgery
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Sep 2022.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pituitary Neoplasms, run by Huashan Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 29 Sep 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The risk of refractory pituitary adenoma (time frame: 10 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Sep 2022.