{"title":"预测甲状腺乳头状癌的远处转移:综合性别、组织学、双侧性和甲状腺外展的术后Nomogram。","authors":"Jiaxi Wang, Zhiyu Li, Chuang Chen","doi":"10.2147/IJGM.S547317","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Papillary thyroid carcinoma (PTC) generally has a good prognosis, but distant metastasis (DM) significantly reduces survival. Existing predictive models for DM have limited accuracy. This study aimed to identify independent risk factors for DM in PTC and develop a clinical prediction model using routine pathological parameters.</p><p><strong>Methods: </strong>We retrospectively analyzed a cohort of 4127 PTC patients who underwent surgery between 2017 and 2022. Patients were divided into DM (n = 30) and non-DM (n = 4097) groups. Key variables, including sex, age, pathological subtype, tumor size, bilaterality, multifocality, extrathyroidal extension (ETE), and lymph node metastasis (LNM), were collected. We used univariate and multivariate logistic regression to identify independent predictors. A nomogram model was built and its performance was evaluated using ROC curves and other metrics.</p><p><strong>Results: </strong>Univariate analysis identified male sex (OR = 0.362, <i>p</i> = 0.006), solid variant (OR = 36.509, <i>p</i> < 0.001), Multifocal (OR = 0.247, <i>p</i> < 0.001), bilaterality (OR = 2.847, <i>p</i> = 0.004), and ETE (OR = 4.360, <i>p</i> = 0.016) as significant risk factors. Multivariate analysis confirmed male sex (OR = 0.434, <i>p</i> = 0.029), solid variant (OR = 23.483, <i>p</i> < 0.001), bilaterality (OR = 1.309, <i>p</i> = 0.047), and ETE (OR = 3.094, <i>p</i> = 0.012) as independent predictors. The nomogram model showed a moderate discriminative ability with an AUC of 0.737, a sensitivity of 66.7%, and a specificity of 68.7%.</p><p><strong>Conclusion: </strong>In this large-scale Chinese cohort study, we identified male sex, solid variant, bilaterality, and ETE as independent risk factors for PTCDM. The resulting model offers a practical tool for postoperative risk assessment, which can help guide customized surveillance and treatment for high-risk patients. Future research should focus on validating this model with external and multicenter cohorts and incorporating molecular biomarkers to further improve its predictive accuracy.</p>","PeriodicalId":14131,"journal":{"name":"International Journal of General Medicine","volume":"18 ","pages":"5409-5419"},"PeriodicalIF":2.0000,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439841/pdf/","citationCount":"0","resultStr":"{\"title\":\"Predicting Distant Metastasis in Papillary Thyroid Carcinoma: A Postoperative Nomogram Integrating Sex, Histology, Bilaterality, and Extrathyroidal Extension.\",\"authors\":\"Jiaxi Wang, Zhiyu Li, Chuang Chen\",\"doi\":\"10.2147/IJGM.S547317\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Papillary thyroid carcinoma (PTC) generally has a good prognosis, but distant metastasis (DM) significantly reduces survival. Existing predictive models for DM have limited accuracy. This study aimed to identify independent risk factors for DM in PTC and develop a clinical prediction model using routine pathological parameters.</p><p><strong>Methods: </strong>We retrospectively analyzed a cohort of 4127 PTC patients who underwent surgery between 2017 and 2022. Patients were divided into DM (n = 30) and non-DM (n = 4097) groups. Key variables, including sex, age, pathological subtype, tumor size, bilaterality, multifocality, extrathyroidal extension (ETE), and lymph node metastasis (LNM), were collected. We used univariate and multivariate logistic regression to identify independent predictors. A nomogram model was built and its performance was evaluated using ROC curves and other metrics.</p><p><strong>Results: </strong>Univariate analysis identified male sex (OR = 0.362, <i>p</i> = 0.006), solid variant (OR = 36.509, <i>p</i> < 0.001), Multifocal (OR = 0.247, <i>p</i> < 0.001), bilaterality (OR = 2.847, <i>p</i> = 0.004), and ETE (OR = 4.360, <i>p</i> = 0.016) as significant risk factors. Multivariate analysis confirmed male sex (OR = 0.434, <i>p</i> = 0.029), solid variant (OR = 23.483, <i>p</i> < 0.001), bilaterality (OR = 1.309, <i>p</i> = 0.047), and ETE (OR = 3.094, <i>p</i> = 0.012) as independent predictors. The nomogram model showed a moderate discriminative ability with an AUC of 0.737, a sensitivity of 66.7%, and a specificity of 68.7%.</p><p><strong>Conclusion: </strong>In this large-scale Chinese cohort study, we identified male sex, solid variant, bilaterality, and ETE as independent risk factors for PTCDM. The resulting model offers a practical tool for postoperative risk assessment, which can help guide customized surveillance and treatment for high-risk patients. Future research should focus on validating this model with external and multicenter cohorts and incorporating molecular biomarkers to further improve its predictive accuracy.</p>\",\"PeriodicalId\":14131,\"journal\":{\"name\":\"International Journal of General Medicine\",\"volume\":\"18 \",\"pages\":\"5409-5419\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439841/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of General Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/IJGM.S547317\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of General Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/IJGM.S547317","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
摘要
背景:甲状腺乳头状癌(PTC)通常预后良好,但远处转移(DM)显著降低生存率。现有的DM预测模型精度有限。本研究旨在确定PTC中DM的独立危险因素,并利用常规病理参数建立临床预测模型。方法:我们回顾性分析了2017年至2022年间接受手术的4127例PTC患者。患者分为糖尿病组(n = 30)和非糖尿病组(n = 4097)。关键变量包括性别、年龄、病理亚型、肿瘤大小、双侧、多灶性、甲状腺外展(ETE)和淋巴结转移(LNM)。我们使用单变量和多变量逻辑回归来确定独立的预测因子。建立nomogram模型,并以ROC曲线等指标评价模型的性能。结果:单因素分析发现男性性别(OR = 0.362, p = 0.006)、实体变异(OR = 36.509, p < 0.001)、多灶性(OR = 0.247, p < 0.001)、双侧(OR = 2.847, p = 0.004)和ETE (OR = 4.360, p = 0.016)是显著的危险因素。多因素分析证实,男性性别(OR = 0.434, p = 0.029)、实体变异(OR = 23.483, p < 0.001)、双侧性(OR = 1.309, p = 0.047)和ETE (OR = 3.094, p = 0.012)是独立预测因素。nomogram model具有中等的判别能力,AUC为0.737,灵敏度为66.7%,特异度为68.7%。结论:在这项大规模的中国队列研究中,我们确定了男性、固体变异、双侧性和ETE是PTCDM的独立危险因素。该模型为术后风险评估提供了实用工具,有助于指导高危患者的个性化监测和治疗。未来的研究应侧重于通过外部和多中心队列验证该模型,并结合分子生物标志物进一步提高其预测准确性。
Predicting Distant Metastasis in Papillary Thyroid Carcinoma: A Postoperative Nomogram Integrating Sex, Histology, Bilaterality, and Extrathyroidal Extension.
Background: Papillary thyroid carcinoma (PTC) generally has a good prognosis, but distant metastasis (DM) significantly reduces survival. Existing predictive models for DM have limited accuracy. This study aimed to identify independent risk factors for DM in PTC and develop a clinical prediction model using routine pathological parameters.
Methods: We retrospectively analyzed a cohort of 4127 PTC patients who underwent surgery between 2017 and 2022. Patients were divided into DM (n = 30) and non-DM (n = 4097) groups. Key variables, including sex, age, pathological subtype, tumor size, bilaterality, multifocality, extrathyroidal extension (ETE), and lymph node metastasis (LNM), were collected. We used univariate and multivariate logistic regression to identify independent predictors. A nomogram model was built and its performance was evaluated using ROC curves and other metrics.
Results: Univariate analysis identified male sex (OR = 0.362, p = 0.006), solid variant (OR = 36.509, p < 0.001), Multifocal (OR = 0.247, p < 0.001), bilaterality (OR = 2.847, p = 0.004), and ETE (OR = 4.360, p = 0.016) as significant risk factors. Multivariate analysis confirmed male sex (OR = 0.434, p = 0.029), solid variant (OR = 23.483, p < 0.001), bilaterality (OR = 1.309, p = 0.047), and ETE (OR = 3.094, p = 0.012) as independent predictors. The nomogram model showed a moderate discriminative ability with an AUC of 0.737, a sensitivity of 66.7%, and a specificity of 68.7%.
Conclusion: In this large-scale Chinese cohort study, we identified male sex, solid variant, bilaterality, and ETE as independent risk factors for PTCDM. The resulting model offers a practical tool for postoperative risk assessment, which can help guide customized surveillance and treatment for high-risk patients. Future research should focus on validating this model with external and multicenter cohorts and incorporating molecular biomarkers to further improve its predictive accuracy.
期刊介绍:
The International Journal of General Medicine is an international, peer-reviewed, open access journal that focuses on general and internal medicine, pathogenesis, epidemiology, diagnosis, monitoring and treatment protocols. The journal is characterized by the rapid reporting of reviews, original research and clinical studies across all disease areas.
A key focus of the journal is the elucidation of disease processes and management protocols resulting in improved outcomes for the patient. Patient perspectives such as satisfaction, quality of life, health literacy and communication and their role in developing new healthcare programs and optimizing clinical outcomes are major areas of interest for the journal.
As of 1st April 2019, the International Journal of General Medicine will no longer consider meta-analyses for publication.