Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-26 @ 10:34 AM
Ignite Modification Date:
2025-12-26 @ 10:34 AM
NCT ID:
NCT06725628
Brief Summary:
Establish a predictive model for assessing neck lymph node metastasis of papillary thyroid carcinoma based on metabolomics, proteomics, and imaging omics data, exploring an ideal protocal for the precise diagnosis and treatment of papillary thyroid carcinoma."
Detailed Description:
This study is a multicenter, observational cohort study aimed at assessing the accuracy and effectiveness of the ThyMPR-CLNM multi-omics model in predicting CLNM in patients diagnosed with stage T1 PTC. The design incorporates the following critical components:
The study enrolled 2000 patients diagnosed with stage T1 PTC from Hangzhou Traditional Chinese Medical Hospital, affiliated with Zhejiang Chinese Medical University, between Dec.2024 and Dec.2026. Fresh frozen tumor tissue, serum samples, and preoperative ultrasound images were collected from participants. These samples were utilized for comprehensive multi-omics analyses, including metabolomic and proteomic profiling, as well as ultrasound radiomic feature extraction. To minimize selection bias and balance covariates, propensity score matching was performed in two rounds, establishing a discovery set and a validation set with matched groups based on the propensity scores calculated through logistic regression. This ensured comparable groups for subsequent analyses. The study involved analyzing the collected samples through advanced techniques such as liquid chromatography-mass spectrometry (LC-MS) for metabolomic and proteomic analyses, and Pyradiomics for extracting radiomics features from ultrasound images. Differentially expressed metabolites, proteins, and radiomic features were identified and integrated for the development of the ThyMPR-CLNM prediction model. The Least Absolute Shrinkage and Selection Operator (LASSO) regression technique was utilized to construct the ThyMPR-CLNM model based on identified features from the multi-omics analyses. The model's performance was subsequently validated using an independent dataset. Statistical evaluations were performed using R software to determine the model's accuracy, sensitivity, specificity, and AUC values. Comparisons with conventional diagnostic methods were conducted to highlight the ThyMPR-CLNM model's advantages.
Study:
NCT06725628
Study Brief:
Protocol Section:
NCT06725628