Multiomics Study of Biological Behavior of Lymph Node Metastasis in Papillary Thyroid Carcinoma

Tianhan Zhou

Status: Not yet enrolling

Conditions: Papillary Thyroid Carcinoma; Lymph Node Cancer Metastatic

Treatments: Diagnostic Test: Thy_CLNM_multi_omics

Study type: Observational

Funder types: Other

Identifiers

NCT06725628

2022ZA119 (Other Grant/Funding Number)

2022KY153-CX1

Details and patient eligibility

About

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."

Full 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.

Enrollment: 2,000 estimated patients

Sex: All

Ages: 18 to 80 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

1. Pathological confirmation of PTC.
2. Patients who underwent primary surgery accompanied by central neck lymph node dissection.
3. Tumors measuring less than 2 cm in diameter.
4. Postoperative pathological reports including detailed information on the number of lymph nodes dissected and the number of metastatic lymph nodes.
5. Availability of comprehensive preoperative thyroid ultrasound images for analysis.

Exclusion criteria

1. Postoperative pathological diagnosis indicating sub-types of PTC.
2. Tumor invasion into adjacent anatomic structures such as the sternothyroid muscle, surrounding soft tissues, trachea, esophagus, or laryngeal nerve.
3. History of neck trauma, previous tumor surgery, or adjuvant chemoradiotherapy.
4. Fewer than three lymph nodes dissected during surgery.
5. Concurrent acute inflammatory conditions or other hematologic disorders.

Trial design

2,000 participants in 1 patient group

LNM group and NLNM group

Description:

The primary objective of this study is to evaluate the accuracy of the ThyMPR-CLNM model in predicting central lymph node metastasis (CLNM) in patients with stage T1 papillary thyroid carcinoma (PTC).

Treatment:

Diagnostic Test: Thy_CLNM_multi_omics