Clinical trial NCT06336499: Risk Stratification of Orbital Tumors Based on MRl and Artificial Intelligence
Official title: Risk Stratification of Orbital Tumors Based on MRl and Artificial Intelligence
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Orbital Neoplasms
Keywords: Multiparametric MRI
Orbital Neoplasms
AI
Lead sponsor: Beijing Tongren Hospital
Sponsor class: OTHER
First posted: 2024-03-28
Enrollment: 600
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
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.

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.

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:

1. 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.
2. 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.
3. 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.

Eligibility criteria: 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.

Primary outcomes:
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: Pre-operation
Secondary outcomes:
measure: 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.
timeFrame: Pre-operation
measure: 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.
timeFrame: Pre-operation
measure: 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.
timeFrame: Pre-operation
measure: 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.
timeFrame: Pre-operation
Other outcomes:
Locations:
Study officials:
affiliation: Department of Radiology, Beijing Tongren Hospital, Capital Medical University
name: Junfang Xian, M.D., Ph.D.
role: STUDY_CHAIR
References: