Congenital biliary dilatation necessitates timely intervention owing to potential complications. This study endeavors to enhance diagnostic precision using quantitative three-dimensional morphological characteristics. Objectives involve developing models to differentiate congenital from secondary biliary dilatation and identify intrahepatic involvement. Employing machine learning, robust diagnostic models aim to elevate clinical detection rates and improve accuracy.
Congenital biliary dilatation is a primary anomaly affecting the biliary tract. It can involve the extrahepatic bile ducts, intrahepatic bile ducts, or the entire biliary system, including the common bile duct. Patients with congenital biliary dilatation exhibit abnormal expansion of the bile duct system, which can lead to complications such as bile duct stones, pancreatic inflammation, and even bile duct cancer. Timely and accurate diagnosis, followed by surgical intervention to remove the dilated bile duct lesion, is crucial for the treatment of choledochal dilation. However, the differentiation of congenital biliary dilatation in clinical practice poses challenges, primarily due to the limitations of subjective physician experience and macroscopic imaging features, making it difficult to achieve high sensitivity in discerning congenital biliary dilatation. Particularly, in distinguishing between congenital biliary dilatation and secondary biliary dilatation, the similarities of the bile ducts limit the precision of clinical decisions. Therefore, this study aims to address the current challenges in the differential diagnosis of congenital biliary dilatation and secondary biliary dilatation by quantitatively describing the morphology of dilated bile ducts. Moreover, this study plans to build a predictive model of intrahepatic bile duct dilatation to provide more comprehensive clinical support. Specifically, the research objectives are outlined as follows: 1. Establish a diagnostic model for congenital biliary dilatation utilizing three-dimensional morphological characteristics, especially quantitative shape- and diameter-based characteristics, to enhance the accurate discrimination between congenital biliary dilatation and secondary biliary dilatation. 2. Develop a model for identifying intrahepatic involvement of congenital biliary dilatation, aiming to provide more precise information for surgical planning and supportive treatment. 3. Construct robust diagnostic models using machine learning with quantitative three-dimensional morphological characteristics, aiming to increase clinical detection rates and accuracy, thereby achieving risk stratification for patients with biliary dilatation.
Study Type
OBSERVATIONAL
Enrollment
550
Diagnosis models are established using quantitative morphological characteristics extracted from the 3D reconstructed bile duct from contrast-enhanced computed tomography (CECT) images.
Beijing Tsinghua Chang Gung Hospital
Beijing, Beijing Municipality, China
Area under the Receiver Operating Characteristic curve (AUROC) of the diagnostic models for the differential diagnosis of congenital and secondary biliary dilatation and the identification of intrahepatic involvement
The area under the ROC curve is calculated by integrating the ROC curve, which plots Sensitivity against 1 - Specificity.
Time frame: Pre-operation
Area under the Precision-Recall curve (AUPRC) of the diagnostic models for the differential diagnosis of congenital and secondary biliary dilatation and the identification of intrahepatic involvement
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 congenital and secondary biliary dilatation and the identification of intrahepatic involvement
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 congenital and secondary biliary dilatation and the identification of intrahepatic involvement
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 congenital and secondary biliary dilatation and the identification of intrahepatic involvement
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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