{"title":"甲状腺乳头状癌颈淋巴结转移的风险因素分析与风险预测","authors":"Dandan Tian, Xiaoqin Li, Zhongzhi Jia","doi":"10.2147/CMAR.S485708","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>To analyze the risk factors of cervical lymph node metastasis (LNM) of thyroid papillary carcinoma (PTC) and construct the prediction model.</p><p><strong>Methods: </strong>Clinical data of 1105 patients with pathologically confirmed PTC in our hospital from February 2019 to May 2024 were retrospectively analyzed, and randomly divided into a training set and validation set according to the proportion of 7:3. With cervical central LNM (CLNM) and lateral LNM (LLNM) as outcome variables respectively, ultrasound characteristics were analyzed and C-TIRADS scores were performed Combined with the general situation of the patient, preoperative serum thyroglobulin (Tg) level, BRAFV600E (hereinafter referred to as BRAF) gene mutation and other characteristics of the patient, analysis was conducted to determine the independent risk factors for cervical CLNM and LLNM of PTC, and establish Nomogram prediction models. The test data set is used to validate the model. The area under the ROC curve (AUC) and the decision curve analysis (DCA) were used to evaluate the prediction efficiency of the model.</p><p><strong>Results: </strong>The analysis shows that male, age < 55 years old, tumor diameter ≥ 1 cm, capsular invasion, positive serum thyroglobulin (Tg), BRAF gene mutation type and C-TIRADS score are independent risk factors for cervical CLNM in PTC (P < 0.05). Tumor diameter ≥ 1 cm, capsular invasion, tumor located at the upper pole and presence of CLNM are independent risk factors for LLNM in PTC. Based on the above risk factors, Nomogram prediction models for CLNM and LLNM are constructed respectively. The AUC of the CLNM prediction model is 91.5%. LLNM model is 96.1%.</p><p><strong>Conclusion: </strong>Ultrasound indicators, C-TIRADS score combined with BRAF gene status, Tg and clinical indicators of patients have important value in predicting cervical CLNM and LLNM in PTC. The Nomogram prediction models constructed based on the above indicators can effectively predict the risk of LNM in PTC.</p>","PeriodicalId":9479,"journal":{"name":"Cancer Management and Research","volume":"16 ","pages":"1571-1585"},"PeriodicalIF":2.5000,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11566207/pdf/","citationCount":"0","resultStr":"{\"title\":\"Analysis of Risk Factors and Risk Prediction for Cervical Lymph Node Metastasis in Thyroid Papillary Carcinoma.\",\"authors\":\"Dandan Tian, Xiaoqin Li, Zhongzhi Jia\",\"doi\":\"10.2147/CMAR.S485708\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>To analyze the risk factors of cervical lymph node metastasis (LNM) of thyroid papillary carcinoma (PTC) and construct the prediction model.</p><p><strong>Methods: </strong>Clinical data of 1105 patients with pathologically confirmed PTC in our hospital from February 2019 to May 2024 were retrospectively analyzed, and randomly divided into a training set and validation set according to the proportion of 7:3. With cervical central LNM (CLNM) and lateral LNM (LLNM) as outcome variables respectively, ultrasound characteristics were analyzed and C-TIRADS scores were performed Combined with the general situation of the patient, preoperative serum thyroglobulin (Tg) level, BRAFV600E (hereinafter referred to as BRAF) gene mutation and other characteristics of the patient, analysis was conducted to determine the independent risk factors for cervical CLNM and LLNM of PTC, and establish Nomogram prediction models. The test data set is used to validate the model. The area under the ROC curve (AUC) and the decision curve analysis (DCA) were used to evaluate the prediction efficiency of the model.</p><p><strong>Results: </strong>The analysis shows that male, age < 55 years old, tumor diameter ≥ 1 cm, capsular invasion, positive serum thyroglobulin (Tg), BRAF gene mutation type and C-TIRADS score are independent risk factors for cervical CLNM in PTC (P < 0.05). Tumor diameter ≥ 1 cm, capsular invasion, tumor located at the upper pole and presence of CLNM are independent risk factors for LLNM in PTC. Based on the above risk factors, Nomogram prediction models for CLNM and LLNM are constructed respectively. The AUC of the CLNM prediction model is 91.5%. LLNM model is 96.1%.</p><p><strong>Conclusion: </strong>Ultrasound indicators, C-TIRADS score combined with BRAF gene status, Tg and clinical indicators of patients have important value in predicting cervical CLNM and LLNM in PTC. The Nomogram prediction models constructed based on the above indicators can effectively predict the risk of LNM in PTC.</p>\",\"PeriodicalId\":9479,\"journal\":{\"name\":\"Cancer Management and Research\",\"volume\":\"16 \",\"pages\":\"1571-1585\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11566207/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer Management and Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/CMAR.S485708\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Management and Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/CMAR.S485708","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
Analysis of Risk Factors and Risk Prediction for Cervical Lymph Node Metastasis in Thyroid Papillary Carcinoma.
Background: To analyze the risk factors of cervical lymph node metastasis (LNM) of thyroid papillary carcinoma (PTC) and construct the prediction model.
Methods: Clinical data of 1105 patients with pathologically confirmed PTC in our hospital from February 2019 to May 2024 were retrospectively analyzed, and randomly divided into a training set and validation set according to the proportion of 7:3. With cervical central LNM (CLNM) and lateral LNM (LLNM) as outcome variables respectively, ultrasound characteristics were analyzed and C-TIRADS scores were performed Combined with the general situation of the patient, preoperative serum thyroglobulin (Tg) level, BRAFV600E (hereinafter referred to as BRAF) gene mutation and other characteristics of the patient, analysis was conducted to determine the independent risk factors for cervical CLNM and LLNM of PTC, and establish Nomogram prediction models. The test data set is used to validate the model. The area under the ROC curve (AUC) and the decision curve analysis (DCA) were used to evaluate the prediction efficiency of the model.
Results: The analysis shows that male, age < 55 years old, tumor diameter ≥ 1 cm, capsular invasion, positive serum thyroglobulin (Tg), BRAF gene mutation type and C-TIRADS score are independent risk factors for cervical CLNM in PTC (P < 0.05). Tumor diameter ≥ 1 cm, capsular invasion, tumor located at the upper pole and presence of CLNM are independent risk factors for LLNM in PTC. Based on the above risk factors, Nomogram prediction models for CLNM and LLNM are constructed respectively. The AUC of the CLNM prediction model is 91.5%. LLNM model is 96.1%.
Conclusion: Ultrasound indicators, C-TIRADS score combined with BRAF gene status, Tg and clinical indicators of patients have important value in predicting cervical CLNM and LLNM in PTC. The Nomogram prediction models constructed based on the above indicators can effectively predict the risk of LNM in PTC.
期刊介绍：
Cancer Management and Research is an international, peer reviewed, open access journal focusing on cancer research and the optimal use of preventative and integrated treatment interventions to achieve improved outcomes, enhanced survival, and quality of life for cancer patients. Specific topics covered in the journal include:
◦Epidemiology, detection and screening
◦Cellular research and biomarkers
◦Identification of biotargets and agents with novel mechanisms of action
◦Optimal clinical use of existing anticancer agents, including combination therapies
◦Radiation and surgery
◦Palliative care
◦Patient adherence, quality of life, satisfaction
The journal welcomes submitted papers covering original research, basic science, clinical & epidemiological studies, reviews & evaluations, guidelines, expert opinion and commentary, and case series that shed novel insights on a disease or disease subtype.
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