Clinical trial · Observational
Risk Stratification of Orbital Tumors Based on MRl and Artificial Intelligence
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Orbital Neoplasms | Orbital Neoplasm | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Multi-parametric MRI and image analysis by deep learning or machine learning algorithms | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Malignant orbital tumors
- description
- Patients with malignant orbital tumors (lymphoma, melanoma, ...) diagnosed by pathological confirmation.
- interventionNames
- Other: Multi-parametric MRI and image analysis by deep learning or machine learning algorithms
- label
- Benign orbital tumors
- description
- Patients with benign orbital tumors (cavernous hemangioma, inflammatory pseudotumor, ...) diagnosed by pathological confirmation.
- interventionNames
- Other: Multi-parametric MRI and image analysis by deep learning or machine learning algorithms
Primary outcomes (1)
- 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.
- timeFrame
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * The patients with orbital tumors who underwent pre-operative multiparametricMRl (mp-MRl) at Beijing Tongren Hospital from 2015 to 2022. Exclusion Criteria: * The patients without pre-operative multiparametric MRl (mp-MRl) or clear pathological diagnosis.
References
Publications (0)
Data not yet available