Orbital NeoplasmsNCT06336499
Risk Stratification of Orbital Tumors Based on MRl and Artificial Intelligence
An observational study of Orbital Neoplasms, run by Beijing Tongren Hospital.
Trial summary
Orbital tumors can be categorized into benign and malignant tumors, and there are significant variations in their biological behavior, treatment, and prognosis. This study aims to enhance the accurate diagnosis and risk stratification of orbital tumors using artificial intelligence (AI) technology and multiparameter… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Orbital Neoplasms
- What is being tested?
- Multi-parametric MRI and image analysis by deep learning or machine learning algorithms
- How many people
- 600 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2012to 31 Dec 2023
- 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: Patients diagnosed with malignant or benign orbital tumors confirmed by pathology, who underwent multiparametric MRl (mp-MRl) at BeiiingTongren Hospital from 2015 to 2022, were included in this research. Otherwise, patients lacking a definitive pathological diagnosis or pre-operative multiparametric MRl (mp-MRl) were excluded from this investigation.
Who may be able to join
- The patients with orbital tumors who underwent pre-operative multiparametricMRl (mp-MRl) at Beijing Tongren Hospital from 2015 to 2022.
Who cannot join
- The patients without pre-operative multiparametric MRl (mp-MRl) or clear pathological diagnosis.
About this study
Orbital tumors can be categorized into benign and malignant tumors, and there are significant variations in their biological behavior, treatment, and prognosis. This study aims to enhance the accurate diagnosis and risk stratification of orbital tumors using artificial intelligence (AI) technology and multiparameter magnetic resonance imaging (MRI) data. It further explores the intrinsic relationship between MRI and the differential diagnosis of benign and malignant orbital tumors, as well as the pathological subtypes of malignant tumors and Ki-67 expression levels. This research aims to aid in guiding personalized diagnosis and treatment decision-making for patients with orbital tumors while promoting the practical application and incorporation of AI technology.
Read the detailed description
Although orbital tumors are less common than other eye-related diseases, they can be extremely detrimental to patients. Not only can they cause physical disfigurement, but they can also lead to functional impairments such as diminished vision and restricted eye movement. Orbital tumors can be categorized as either benign or malignant, and there are significant disparities in their biological behavior, treatment approaches, outcomes, and prognosis, which complicates the processes of differential diagnosis and treatment selection. For malignant lesions, the treatment plans and prognosis of patients vary due to the different pathological types and stages. Hence, there is a pressing clinical necessity to devise accurate diagnostic methods for orbital tumors. Multiparametric magnetic resonance imaging (mp-MRI) currently stands as the leading non-invasive imaging technique for diagnosing orbital tumors. This study is centered on precise diagnosis of orbital tumor risk stratification, utilizing artificial intelligence algorithm technology to explore the inherent connection between MRI images and the distinguishing diagnosis of benign and malignant orbital tumors, histological types and Ki-67 expression levels of malignant tumors. It aims to integrate clinical information and quantitative MRI features to construct prediction models, aid in guiding individual diagnosis and treatment decisions for patients with orbital tumors and facilitate the application and advancement of artificial intelligence technology. Specifically, the research objectives are outlined as follows:
- Establishing a deep learning-based automatic segmentation model for orbital tumors using a multi-sequence MRI dataset from multiple centers, thereby reducing the time required for manual delineation and proving beneficial for subsequent analysis.
- Developing a model for identifying malignant and benign orbital tumors using multiple machine learning algorithms combined with multi-sequence MRI dataset, with the aim of providing more precise information for distinguishing between these two entities.
- Constructing robust diagnostic models using machine learning or deep learning approaches with quantitative multi-sequence MRI features to identify the histological type and Ki-67 expression levels of malignant orbital tumors, with the purpose of enhancing detection rates and accuracy, thereby achieving risk stratification for patients with malignant orbital tumors.
- Conditions
- Orbital Neoplasms
- Being tested
- Multi-parametric MRI and image analysis by deep learning or machine learning algorithms
- Keywords
- Multiparametric MRI, Orbital Neoplasms, AI
- Medical subject headings
- Orbital Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Multi-parametric MRI and image analysis by deep learning or machine learning algorithmsOther
Diagnosis models are established using quantitative features extracted from the multi-parametric MRI images and further processed by appropriate deep learning or machine learning algorithms.
| Study group | Role | What they receive |
|---|---|---|
| Malignant orbital tumors | Patients with malignant orbital tumors (lymphoma, melanoma, ...) diagnosed by pathological confirmation. Receives: Other: Multi-parametric MRI and image analysis by deep learning or machine learning algorithms | |
| Benign orbital tumors | Patients with benign orbital tumors (cavernous hemangioma, inflammatory pseudotumor, ...) diagnosed by pathological confirmation. Receives: Other: Multi-parametric MRI and image analysis by deep learning or machine learning algorithms |
What is being measured
The primary outcome is the main question the study is designed to answer.
The area under the curve of Receiver Operating Characteristic of the diagnostic models for the differential diagnosis of malignant and benign orbital tumors, high and low grades of histological types, and levels of Ki-67 expression in malignant ones.
The area under the ROC curve is calculated by integrating the ROC curve, which plots Sensitivity against 1 - Specificity.
Measured: Pre-operation
Secondary outcomes (4)
The area under the Precision-Recall curve of the diagnostic models for the differential diagnosis of malignant and benign orbital tumors, high and low grades of histological types, and high and low levels of Ki-67 expression in malignant orbital tumors.
The area under the precision-recall curve is determined by integrating the Precision-Recall curve, which plots Precision against Recall.
Measured: Pre-operation
Sensitivity of the diagnostic models for the differential diagnosis of malignant and benign orbital tumors, high and low grades of histological types, and high and low levels of Ki-67 expression in malignant orbital tumors.
Sensitivity is calculated as the ratio of true positives to the sum of true positives and false negatives.
Measured: Pre-operation
Specificity of the diagnostic models for the differential diagnosis of malignant and benign orbital tumors, high and low grades of histological types, and high and low levels of Ki-67 expression in malignant orbital tumors.
Specificity is calculated as the ratio of true negatives to the sum of true negatives and false positives.
Measured: Pre-operation
Accuracy of the diagnostic models for the differential diagnosis of malignant and benign orbital tumors, high and low grades of histological types, and high and low levels of Ki-67 expression in malignant orbital tumors.
Accuracy is calculated as the ratio of the sum of true positives and true negatives to the total number of cases.
Measured: Pre-operation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Mar 2024
- Study start1 Jan 2012
- Primary completion31 Oct 2022
- Study completion31 Dec 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Orbital NeoplasmsStudies of the same condition, open and under way.
- More trials run by Beijing Tongren 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
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 600 participants (actual)
Sponsor and investigators
- Lead sponsor
- Beijing Tongren Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Junfang Xian, M.D., Ph.D., Study chair, Department of Radiology, Beijing Tongren Hospital, Capital Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06336499
- Sponsor's protocol ID
- TREC2023-KY107
- Status last verified
- Mar 2024
- First submitted
- 22 Mar 2024
- Last updated
- 29 Mar 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 29 Mar 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Risk Stratification of Orbital Tumors Based on MRl and Artificial Intelligence
- ClinicalTrials.gov ID
- NCT06336499
- Acronym / sponsor ID
- TREC2023-KY107
- Conditions
- Orbital Neoplasms
- Keywords
- Multiparametric MRI, Orbital Neoplasms, AI
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 600 participants (actual)
- Lead sponsor
- Beijing Tongren HospitalUniversity, hospital or other
- Study start
- 1 Jan 2012 (actual)
- Primary completion
- 31 Oct 2022 (actual)
- Study completion
- 31 Dec 2023 (actual)
- First posted
- 28 Mar 2024 (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.
Orbital tumors can be categorized into benign and malignant tumors, and there are significant variations in their biological behavior, treatment, and prognosis. This study aims to enhance the accurate diagnosis and risk stratification of orbital tumors using artificial intelligence (AI) technology and multiparameter magnetic resonance imaging (MRI) data. It further explores the intrinsic relationship between MRI and the differential diagnosis of benign and malignant orbital tumors, as well as the pathological subtypes of malignant tumors and Ki-67 expression levels. This research aims to aid in guiding personalized diagnosis and treatment decision-making for patients with orbital tumors while promoting the practical application and incorporation of AI technology.
Detailed description
Although orbital tumors are less common than other eye-related diseases, they can be extremely detrimental to patients. Not only can they cause physical disfigurement, but they can also lead to functional impairments such as diminished vision and restricted eye movement. Orbital tumors can be categorized as either benign or malignant, and there are significant disparities in their biological behavior, treatment approaches, outcomes, and prognosis, which complicates the processes of differential diagnosis and treatment selection. For malignant lesions, the treatment plans and prognosis of patients vary due to the different pathological types and stages. Hence, there is a pressing clinical necessity to devise accurate diagnostic methods for orbital tumors. Multiparametric magnetic resonance imaging (mp-MRI) currently stands as the leading non-invasive imaging technique for diagnosing orbital tumors. This study is centered on precise diagnosis of orbital tumor risk stratification, utilizing artificial intelligence algorithm technology to explore the inherent connection between MRI images and the distinguishing diagnosis of benign and malignant orbital tumors, histological types and Ki-67 expression levels of malignant tumors. It aims to integrate clinical information and quantitative MRI features to construct prediction models, aid in guiding individual diagnosis and treatment decisions for patients with orbital tumors and facilitate the application and advancement of artificial intelligence technology. Specifically, the research objectives are outlined as follows:
- Establishing a deep learning-based automatic segmentation model for orbital tumors using a multi-sequence MRI dataset from multiple centers, thereby reducing the time required for manual delineation and proving beneficial for subsequent analysis.
- Developing a model for identifying malignant and benign orbital tumors using multiple machine learning algorithms combined with multi-sequence MRI dataset, with the aim of providing more precise information for distinguishing between these two entities.
- Constructing robust diagnostic models using machine learning or deep learning approaches with quantitative multi-sequence MRI features to identify the histological type and Ki-67 expression levels of malignant orbital tumors, with the purpose of enhancing detection rates and accuracy, thereby achieving risk stratification for patients with malignant orbital tumors.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Malignant orbital tumors | – | Other: Multi-parametric MRI and image analysis by deep learning or machine learning algorithmsPatients with malignant orbital tumors (lymphoma, melanoma, ...) diagnosed by pathological confirmation. |
| Benign orbital tumors | – | Other: Multi-parametric MRI and image analysis by deep learning or machine learning algorithmsPatients with benign orbital tumors (cavernous hemangioma, inflammatory pseudotumor, ...) diagnosed by pathological confirmation. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients diagnosed with malignant or benign orbital tumors confirmed by pathology, who underwent multiparametric MRl (mp-MRl) at BeiiingTongren Hospital from 2015 to 2022, were included in this research. Otherwise, patients lacking a definitive pathological diagnosis or pre-operative multiparametric MRl (mp-MRl) were excluded from this investigation.
Key inclusion criteria (1 listed)
- The patients with orbital tumors who underwent pre-operative multiparametricMRl (mp-MRl) at Beijing Tongren Hospital from 2015 to 2022.
Key exclusion criteria (1 listed)
- The patients without pre-operative multiparametric MRl (mp-MRl) or clear pathological diagnosis.
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 |
|---|---|---|
| Multi-parametric MRI and image analysis by deep learning or machine learning algorithmsDiagnosis models are established using quantitative features extracted from the multi-parametric MRI images and further processed by appropriate deep learning or machine learning algorithms. | Other | Benign orbital tumors, Malignant orbital tumors |
Primary outcome
The area under the curve of Receiver Operating Characteristic of the diagnostic models for the differential diagnosis of malignant and benign orbital tumors, high and low grades of histological types, and levels of Ki-67 expression in malignant ones.
Time frame: Pre-operation
The area under the ROC curve is calculated by integrating the ROC curve, which plots Sensitivity against 1 - Specificity.
4 secondary outcomes
The area under the Precision-Recall curve of the diagnostic models for the differential diagnosis of malignant and benign orbital tumors, high and low grades of histological types, and high and low levels of Ki-67 expression in malignant orbital tumors.
Time frame: Pre-operation
Sensitivity of the diagnostic models for the differential diagnosis of malignant and benign orbital tumors, high and low grades of histological types, and high and low levels of Ki-67 expression in malignant orbital tumors.
Time frame: Pre-operation
Specificity of the diagnostic models for the differential diagnosis of malignant and benign orbital tumors, high and low grades of histological types, and high and low levels of Ki-67 expression in malignant orbital tumors.
Time frame: Pre-operation
Accuracy of the diagnostic models for the differential diagnosis of malignant and benign orbital tumors, high and low grades of histological types, and high and low levels of Ki-67 expression in malignant orbital tumors.
Time frame: Pre-operation
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Mar 2024.
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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.
Junfang Xian, M.D., Ph.D. study chair
Department of Radiology, Beijing Tongren Hospital, Capital Medical University
Questions about this trial
What is this clinical trial studying?
An observational study of Orbital Neoplasms, run by Beijing Tongren Hospital.
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: finished, as of the sponsor's last update on 29 Mar 2024. Status can change between updates, so confirm with the study contact before making plans.
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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The area under the curve of Receiver Operating Characteristic of the diagnostic models for the differential diagnosis of malignant and benign orbital tumors, high and low grades of histological types, and levels of Ki-67 expression in malignant ones. (time frame: Pre-operation). 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 Mar 2024.