Multiomics Study of Biological Behavior of Lymph Node Metastasis in Papillary Thyroid Carcinoma
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 2,000
- 主要终点
- Lymph node metastasis status
研究概览
简要总结
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."
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Pathological confirmation of PTC.
- •Patients who underwent primary surgery accompanied by central neck lymph node dissection.
- •Tumors measuring less than 2 cm in diameter.
- •Postoperative pathological reports including detailed information on the number of lymph nodes dissected and the number of metastatic lymph nodes.
- •Availability of comprehensive preoperative thyroid ultrasound images for analysis.
排除标准
- •Postoperative pathological diagnosis indicating sub-types of PTC.
- •Tumor invasion into adjacent anatomic structures such as the sternothyroid muscle, surrounding soft tissues, trachea, esophagus, or laryngeal nerve.
- •History of neck trauma, previous tumor surgery, or adjuvant chemoradiotherapy.
- •Fewer than three lymph nodes dissected during surgery.
- •Concurrent acute inflammatory conditions or other hematologic disorders.
结局指标
主要结局
Lymph node metastasis status
时间窗: Record lymph node metastasis status until the end of the surgery, followed by a one-year follow-up until the conclusion of the study.
Patients with thyroid cancer undergo thyroid cancer radical surgery according to the guidelines, with pathological results including the number of lymph node metastases and metastasis status, based on the final pathological diagnosis.
次要结局
- Surgical complications and postoperative recurrence(Record surgical complications until the end of the surgery, followed by a one-year follow-up until the conclusion of the study.)
研究者
Tianhan Zhou
Clinical Professor
Zhejiang Chinese Medical University