甲状腺微小乳头状癌射频消融术后转归的Nomogram预测模型构建
Validated Nomogram Predicting Clinical Outcome after Radiofrequency Ablation in the Treatment of Papillary Thyroid Microcarcinoma
查看参考文献19篇
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文摘
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目的建立超声引导下射频消融(RFA)治疗甲状腺微小乳头状癌(PTMC)患者短期疗效的预测模型。方法回顾性分析2018年1月1日至12月31日在中国人民解放军总医院第一医学中心超声科初次行RFA治疗的159例PTMC患者的术前及随访资料,以RFA术后12个月内消融灶存在状态为终点事件,通过Logistic回归筛选出消融灶未完全消失影响因素,建立评分模型。结果多变量分析结果显示,性别(P = 0.017) 、年龄(P = 0.047)和钙化灶大小(P = 0.049)是建立模型的最强预测因素,肿瘤最大径和RFA消耗总能量是建立模型的次要相关因素。在建模中确认了模型的准确性(AUC = 0.762),在验证组中确定了模型的良好预测效能(AUC = 0.740) 。结论基于PTMC患者RFA术后1年内消融灶未完全消失的影响因素建立了量化模型,可以准确预测PTMC患者RFA术后短期疗效,为临床准确评估RFA短期疗效及对患者术后恢复情况予以解释提供依据。 |
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其他语种文摘
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Objective To establish a prediction model for the short-term efficacy of percutaneous ultrasound- guided radiofrequency ablation (RFA) in the treatment of papillary thyroid microcarcinoma (PTMC). Methods We retrospectively analyzed the preoperative and follow-up data of 159 patients with PTMC who underwent percutaneous ultrasound-guided RFA treatment in the Department of Ultrasound,the First Medical Center of Chinese PLA General Hospital from January to December in 2018. The association with 12-month tumor status (end event) was evaluated by multivariate logistic regression model. A nomogram was built to predict the risk of tumors which did not disappear completely within 12 months after RFA. Results We found that gender (P = 0.017),age (P = 0.047),and calcification (P = 0.049) were the strongest predictors for establishing the model. The tumor maximum diameter and RFA energy were the secondary relevant factors for establishing the model. The constructed model showed good performance in both training cohort (AUC = 0.762) and validation cohort (AUC = 0.740). Conclusion A quantitative model was established for predicting the tumor status within one year after treatment of PTMC by RFA,which can accurately predict the short-term efficacy of RFA and provide a clinical basis for explaining the recovery results of patients. |
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来源
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中国医学科学院学报
,2021,43(3):322-327 【核心库】
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DOI
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10.3881/j.issn.1000-503X.13813
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关键词
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甲状腺微小乳头状癌
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超声
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射频消融
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预测模型
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地址
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1.
南开大学医学院影像医学与核医学专业, 天津, 300071
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中国人民解放军总医院第一医学中心超声诊断科, 北京, 100853
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-503X |
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学科
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临床医学;肿瘤学 |
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基金
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国家自然科学基金
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北京市自然科学基金
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文献收藏号
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CSCD:7009038
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