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.
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.
Study Type
OBSERVATIONAL
Enrollment
600
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.
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.
Time frame: Pre-operation
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.
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.
Sensitivity is calculated as the ratio of true positives to the sum of true positives and false negatives.
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.
Specificity is calculated as the ratio of true negatives to the sum of true negatives and false positives.
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.
Accuracy is calculated as the ratio of the sum of true positives and true negatives to the total number of cases.
Time frame: Pre-operation
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