摘要
目的分析血清甲状腺球蛋白(Tg)及甲状腺球蛋白抗体(TgAb)对分化型甲状腺癌切除术后经^(131)I治疗患者预后的预测价值。方法选择2017年1月—2019年12月新疆医科大学第一附属医院甲状腺外科手术治疗分化型甲状腺癌患者191例,术后接受131 I治疗,并在治疗后进行为期1年的随访,根据随访结果将患者分为预后良好组及预后不良组,比较2组患者临床资料,甲状腺功能指标[三碘甲状腺原氨酸(T_(3))、甲状腺素(T_(4))、游离三碘甲状腺原氨酸(FT_(3))、游离甲状腺素(FT_(4))、促甲状腺素(TSH)、Tg、TgAb]、中性粒细胞明胶酶相关脂质运载蛋白(NGAL)及影像学指标(淋巴结转移、多灶转移、转移灶大小、转移灶数量),采用多因素Logistic回归模型分析影响患者预后的因素,采用受试者工作特征曲线(ROC)分析影响患者预后指标的预测价值。结果随访过程中失访8例,最终纳入患者183例,其中预后良好127例,预后不良56例。预后不良组肿瘤分期Ⅲ~Ⅳ期、多灶转移、病灶>1 cm比例及血清Tg、TgAb、NGAL水平高于预后良好组[χ^(2)(t)/P=12.601/<0.001、4.165/0.042、7.741/0.005、10.657/<0.001、10.592/<0.001、8.586/<0.001];而2组患者T_(3)、T_(4)、FT_(3)、FT_(4)、TSH及淋巴结转移、转移灶数量等指标比较,差异无统计学意义(P均>0.05)。多因素Logistic回归分析显示,高Tg、高TgAb、高NGAL水平及多灶转移是患者预后的独立危险因素[OR(95%CI)=1.114(1.060~1.172)、1.016(1.007~1.025)、1.108(1.042~1.178)、68.700(2.712~1740.439],而肿瘤分期Ⅰ~Ⅱ期是患者预后的独立保护因素[OR(95%CI)=0.026(0.001~0.696)]。ROC曲线显示,血清Tg、TgAb、NGAL及三者联合预测患者预后的曲线下面积(AUC)分别为0.908、0.852、0.805、0.977,三者联合预测价值高于单项指标(Z=3.329、4.013、4.881,P均<0.001)。结论血清Tg联合TgAb能有效对分化型甲状腺癌术后患者行^(131)I治疗的预后进行预测,具有较高的诊断价值及诊断效能。
Objective To analyze the predictive value of serum thyroglobulin(Tg)and thyroglobulin antibody(TgAb)on the prognosis of patients with differentiated thyroid carcinoma treated with ^(131)I after resection.Methods One hundred and ninety-one patients with differentiated thyroid cancer were treated by thyroid surgery in the First Affiliated Hospital of Xinjiang Medical University from January 2017 to December 2019.They received ^(131)I treatment after surgery and were followed up for 1 year after treatment.According to the follow-up results,the patients were divided into two groups:the group with good prognosis and the group with poor prognosis.The clinical data of the patients in the two groups were compared,and the indexes of thyroid function[triiodothyronine(T_(3)),thyroxine(T_(4))Free triiodothyronine(FT_(3)),free thyroxine(FT_(4)),thyrotropin(TSH),Tg,TgAb],neutrophil gelatinase-associated lipid carrier protein(NGAL)and imaging indicators(lymph node metastasis,multi-focus metastasis,size of metastasis,number of metastasis).The factors affecting the prognosis of patients were analyzed by using multivariate logistic regression model,The predictive value of the prognostic indicators of patients was analyzed by the ROC.Results During the follow-up,8 patients were lost and 183 patients were eventually included,including 127 patients with good prognosis and 56 patients with poor prognosis.In the poor prognosis group,the tumor stageⅢtoⅣ,multiple metastasis,the proportion of lesions>1 cm,and the serum Tg,TgAb,NGAL levels were higher than those in the good prognosis group[χ^(2)(t)/P=12.601/<0.001,4.165/0.042,7.741/0.005,10.657/<0.001,10.592/<0.001,8.586/<0.001];There was no significant difference between the two groups in T_(3),T_(4),FT_(3),FT_(4),TSH,lymph node metastasis and the number of metastatic foci(P>0.05).Multivariate logistic regression analysis showed that high Tg,high TgAb,high NGAL level and multifocal metastasis were independent risk factors for the prognosis of patients[OR(95%CI)=1.114(1.060-1.172),1.016(1.007-1.025),1.108(1.042-1.178),68.700(2.712-1740.439)],while tumor stage I-II was an independent protective factor for the prognosis of patients[OR(95%CI)=0.026(0.001-0.696)].The ROC curve showed that the area under the curve(AUC)of serum Tg,TgAb,NGAL and their combination to predict the prognosis of patients were 0.908,0.852,0.805 and 0.977,respectively.The combined predictive value of the three indicators was higher than that of single indicators(Z=3.329,4.013,4.881,P<0.001).Conclusion The combination of serum Tg and TgAb can effectively predict the prognosis of patients with differentiated thyroid cancer undergoing 131 I treatment after operation,which has high diagnostic value and diagnostic efficacy.
作者
巴雅
祖拉亚提·库尔班
娜姿·依力哈木
谢彬
刘立水
Baya;Zulayati Kuerban;Nazi Yilihamu;Xie Bin;Liu Lishui(Department of Nuclear Medicine,The First Affiliated Hospital of Xinjiang Medical University,Xinjiang Province,Urumqi 830054,China;不详)
出处
《疑难病杂志》
CAS
2023年第2期144-148,154,共6页
Chinese Journal of Difficult and Complicated Cases
基金
新疆维吾尔自治区自然科学基金资助项目(2017D01C354)。