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Risk Stratification of Orbital Tumors Based on MRl and Artificial Intelligence

NCT06336499 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 600

Last updated 2024-03-29

No results posted yet for this study

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 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

  • Orbital Neoplasms

Interventions

OTHER

Multi-parametric MRI and image analysis by deep learning or machine learning algorithms

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.

Sponsors & Collaborators

  • Beijing Tongren Hospital

    lead OTHER

Principal Investigators

  • Junfang Xian, M.D., Ph.D. · Department of Radiology, Beijing Tongren Hospital, Capital Medical University

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2012-01-01
Primary Completion
2022-10-31
Completion
2023-12-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06336499 on ClinicalTrials.gov