Ignite Creation Date:
2025-12-26 @ 10:34 AM
Ignite Modification Date:
2025-12-26 @ 10:34 AM
Study NCT ID:
NCT06725628
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-12-10
First Post:
2024-11-22
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Multiomics Study of Biological Behavior of Lymph Node Metastasis in Papillary Thyroid Carcinoma
Sponsor:
Tianhan Zhou
Organization:
Study Overview
Official Title:
Multiomics Study of Biological Behavior of Lymph Node Metastasis in Papillary Thyroid Carcinoma
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-12
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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.
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 Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
Secondary ID Infos
| Secondary ID | Type | Domain | Link | View |
|---|---|---|---|---|
| 2022ZA119 | OTHER_GRANT | Huang Hai | View |