Risk Stratification of Orbital Tumors Based on MRl and Artificial Intelligence

NCT06336499 | Completed

• 1 other identifier: TREC2023-KY107
• Study Type: observational
• Target: 600
• Locations: 0 countries
• Sites: N/A

Brief 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

Keywords

Multiparametric MRI; Orbital Neoplasms; AI

Outcome Measures

Primary Outcomes (1)

• 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.

  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.

  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.

  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.

  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.

  Pre-operation

Study Arms (2)

Malignant orbital tumors

Patients with malignant orbital tumors (lymphoma, melanoma, ...) diagnosed by pathological confirmation.

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.

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

Interventions

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

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.

Benign orbital tumors; Malignant orbital tumors

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• The patients with orbital tumors who underwent pre-operative multiparametricMRl (mp-MRl) at Beijing Tongren Hospital from 2015 to 2022.

You may not qualify if:

• The patients without pre-operative multiparametric MRl (mp-MRl) or clear pathological diagnosis.

Sponsors & Collaborators

• Beijing Tongren Hospital: lead

MeSH Terms

Conditions

Orbital Neoplasms

Interventions

Machine Learning Algorithms

Condition Hierarchy (Ancestors)

Skull Neoplasms; Bone Neoplasms; Neoplasms by Site; Neoplasms; Eye Neoplasms; Bone Diseases; Musculoskeletal Diseases; Eye Diseases; Orbital Diseases

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Officials

• Junfang Xian, M.D., Ph.D.

  Department of Radiology, Beijing Tongren Hospital, Capital Medical University

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 22, 2024
• First Posted: March 28, 2024
• Study Start: January 1, 2012
• Primary Completion: October 31, 2022
• Study Completion: December 31, 2023
• Last Updated: March 29, 2024

Record last verified: 2024-03