中、高危分化型甲状腺癌患者术后 131I治疗反应及预后的影响因素

PDF (PC)

中、高危分化型甲状腺癌患者术后 ¹³¹I治疗反应及预后的影响因素

Influencing factors for postoperative ¹³¹I treatment response and prognosis in patients with intermediate- and high-risk differentiated thyroid cancer

摘要/Abstract

参考文献 26

多维度评价

摘要/Abstract

引用本文

使用本文

参考文献 26

相关文章 2

多维度评价

本文评价

推荐阅读 0

doi:10.6040/j.issn.1671-7554.0.2024.0824

摘要： 目的通过最优尺度回归分析和联合诊断ROC模型分析中、高危分化型甲状腺癌(differentiated thyroid cancer, DTC)患者术后¹³¹I治疗反应及预后的预测因素,为制定临床治疗方案提供参考依据。方法回顾性分析414例中高危DTC术后已行¹³¹I治疗患者的临床资料。根据复发风险分为中危组(n=275)和高危组(n=139)。通过χ2检验比较中危和高危组患者的等级资料;采用 Mann-Whitney检验比较中危组和高危组患者的计数资料;Medcalc软件绘制ROC曲线并比较各组预测因素的诊断效能。结果中危组和高危组患者在年龄、T分期、M分期、治疗次数、累积剂量、促甲状腺激素、抗甲状腺球蛋白抗体、甲状腺球蛋白差异有统计学意义(P<0.01)。中危组治疗反应的有效预测因素是性别、治疗次数及累积剂量,其中累积剂量的曲线下面积(area under the curve, AUC)最大,但小于三者联合诊断的AUC;中、高危组中有累积剂量、T分期及M分期,其中累积剂量的AUC最大,且同样小于三者联合诊断的AUC。中、高危组死亡风险的有效预测因素是T分期和M分期,其中M分期的AUC最大,T分期和M分期联合诊的断AUC略大于M分期。结论预测中、高危DTC患者治疗反应的最有效单一因素为累积剂量,预测死亡风险的最有效单一因素为M分期;各预测因素联合诊断的效能均高于单一因素,提示积极的¹³¹I治疗有利于改善中高危DTC患者的预后。

关键词: 分化型甲状腺癌, 中高危患者, ¹³¹I治疗, 最优尺度回归分析, 联合诊断

Abstract: Objective To identify the influencing factors for the response to postoperative ¹³¹I treatment and prognosis in patients with intermediate and high-risk differentiated thyroid cancer(DTC)by employing optimal scale regression analysis and joint diagnostic ROC model analysis, so as to provide a reference basis for the development of clinical treatment plans. Methods A retrospective analysis was conducted on the clinical data of 414 patients with intermediate and high-risk differentiated thyroid cancer(DTC)who had undergone postoperative ¹³¹I treatment. The patients were divided into an intermediate-risk group(n=275)and a high-risk group(n=139)based on the risk of recurrence. Chi-square tests were used to compare the categorical data between the intermediate-risk and high-risk groups; the Mann-Whitney test was employed to compare the numerical data between the two groups. The Medcalc software was used to plot ROC curves and compare the diagnostic efficacy of predictive factors among the groups. Results Patients in the intermediate-risk and high-risk groups showed statistically significant differences in age, T stage, M stage, number of treatments, cumulative dose, thyroid-stimulating hormone, anti-thyroglobulin antibodies, and thyroglobulin levels(P<0.01). The effective predictive factors for treatment response in the intermediate-risk group were gender, number of treatments, and cumulative dose, with the cumulative dose having the largest area under the curve(AUC), but it was still smaller than the AUC of the combined diagnostic model of all three factors. In both the intermediate and high-risk groups, the cumulative dose, T stage, and M stage were predictive factors, with the cumulative dose having the largest AUC, which was also smaller than the AUC of the combined diagnostic model of the three factors. The effective predictive factors for mortality risk in the intermediate and high-risk groups were T stage and M stage, with M stage having the largest AUC. The AUC for the combined diagnosis of T stage and M stage was slightly larger than that of M stage alone. Conclusion The most effective single factor for predicting the treatment response in intermediate and high-risk DTC patients is the cumulative dose, while the most effective single factor for predicting the risk of death is the M stage. The efficacy of the combined diagnostic model of all predictive factors is higher than that of any single factor, suggesting that aggressive ¹³¹I treatment is beneficial for improving the prognosis of intermediate and high-risk DTC patients.

Key words: Differentiated thyroid cancer, Intermediate- and high-risk patients, ¹³¹I treatment, Optimal scale regression analysis, Combined diagnosis

中图分类号:

R365

赵芸慕兰,高海燕. 中、高危分化型甲状腺癌患者术后 ¹³¹I治疗反应及预后的影响因素[J]. 山东大学学报 (医学版), 2025, 63(2): 21-28.

ZHAO Yunmulan, GAO Haiyan. Influencing factors for postoperative ¹³¹I treatment response and prognosis in patients with intermediate- and high-risk differentiated thyroid cancer[J]. Journal of Shandong University (Health Sciences), 2025, 63(2): 21-28.

[1]	姚承志, 张敏, 曾雨可, 等. 中国甲状腺癌发病和死亡趋势分析与预测[J]. 中华流行病学杂志, 2023, 44(6): 917-923. YAO Chengzhi, ZHANG Min, ZENG Yuke, et al. Analysis and prediction of thyroid cancer morbidity and mortality trends in China [J]. Chinese Journal of Epidemiology, 2023, 44(6): 917-923.
[2]	Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2018, 68(6): 394-424.
[3]	Haugen BR, Alexander EK, Bible KC, et al. 2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the American Thyroid Association guidelines task force on thyroid nodules and differentiated thyroid cancer[J]. Thyroid, 2016, 26(1): 1-133.
[4]	柴佳威, 朱坤兵, 李亚琼, 等. 隐匿性甲状腺癌: 1例病例报道和文献回顾[J]. 山东大学学报(医学版), 2021, 59(1): 83-87. CHAI Jiawei, ZHU Kunbing, LI Yaqiong, et al. Occult thyroid carcinoma: a report of a rare case and review of literature[J]. Journal of Shandong University(Health Sciences), 2021, 59(1): 83-87.
[5]	郑荣寿, 孙可欣, 张思维, 等. 2015年中国恶性肿瘤流行情况分析[J]. 中华肿瘤杂志, 2019, 41(1): 19-28. ZHENG Rongshou, SUN Kexin, ZHANG Siwei, et al. Report of cancer epidemiology in China, 2015[J]. Chinese Journal of Oncology, 2019, 41(1): 19-28.
[6]	Wang ZP, Chang QG, Zhang HY, et al. A clinical predictive model of central lymph node metastases in papillary thyroid carcinoma[J]. Front Endocrinol, 2022, 13: 856278. doi:10.3389/fendo.2022.856278.
[7]	曹玉茹, 杨年华. 基于SPSS最优尺度的回归方法[J]. 统计与决策, 2019, 35(12): 72-74. CAO Yuru, YANG Nianhua. Regression method based on SPSS optimal scale[J]. Statistics & Decision, 2019, 35(12): 72-74.
[8]	王辉, 孙仙, 徐生刚, 等. 最优尺度回归分析在老年消化道肿瘤患者住院费用影响因素中的应用[J]. 中国老年学杂志, 2015, 35(11): 3123-3124. WANG Hui, SUN Xian, XU Shenggang, et al. Application of optimal scale regression analysis in influencing factors of hospitalization expenses of elderly patients with digestive tract tumors[J]. Chinese Journal of Gerontology, 2015, 35(11): 3123-3124.
[9]	Qu Y, Huang R, Li L. Low- and high-dose radioiodine therapy for low-/ intermediate-risk differentiated thyroid cancer: a preliminary clinical trial[J]. Ann Nucl Med, 2017, 31(1): 71-83.
[10]	中华医学会核医学分会. 131I治疗分化型甲状腺癌指南(2021版)[J]. 中华核医学与分子影像杂志, 2021, 41(4): 218-241. Chinese Society of Nuclear Medicine. Guidelines for radioiodine therapy of differentiated thyroid cancer(2021 edition)[J]. Chinese Journal of Nuclear Medicine and Molecular Imaging, 2021, 41(4): 218-241.
[11]	Knappe L, Giovanella L. Life after thyroid cancer: the role of thyroglobulin and thyroglobulin antibodies for postoperative follow-up[J]. Expert Rev Endocrinol Metab, 2021, 16(6): 273-279.
[12]	Prpic M, Kust D, Kruljac I, et al. Prediction of radioactive iodine remnant ablation failure in patients with differentiated thyroid cancer: a cohort study of 740 patients[J]. Head Neck, 2017, 39(1): 109-115.
[13]	Prpic M, Kruljac I, Kust D, et al. Re-ablation I-131 activity does not predict treatment success in low- and intermediate-risk patients with differentiated thyroid carcinoma[J]. Endocrine, 2016, 52(3): 602-608.
[14]	Campennì A, Ruggeri RM, Siracusa M, et al. Early preablation rhTSH-stimulated thyroglobulin predicts outcome of differentiated thyroid cancer(DTC)patients[J]. Eur J Nucl Med Mol Imaging, 2021, 48(8): 2466-2475.
[15]	Grani G, Zatelli MC, Alfò M, et al. Real-world performance of the American thyroid association risk estimates in predicting 1-year differentiated thyroid cancer outcomes: a prospective multicenter study of 2000 patients[J]. Thyroid, 2021, 31(2): 264-271.
[16]	Chirayath SR, Menon UV, Nair V, et al. Factors determining risk categories in differentiated thyroid carcinoma: study of an Indian cohort[J]. Indian J Endocrinol Metab, 2022, 26(3): 269-274.
[17]	Kwon SY, Lee SW, Kong EJ, et al. Clinicopathologic risk factors of radioactive iodine therapy based on response assessment in patients with differentiated thyroid cancer: a multicenter retrospective cohort study[J]. Eur J Nucl Med Mol Imaging, 2020, 47: 561-571. doi: 10.1007/s00259-019-04634-8.
[18]	Zhao T, Liang J, Guo ZQ, et al. Serum thyrotropin level of 30 μIU/mL is inadequate for preablative thyroglobulin to serve as a prognostic marker for differentiated thyroid cancer[J]. Endocrine, 2016, 53(1): 166-173.
[19]	Zhao T, Liang J, Li TJ, et al. Value of serial preablative thyroglobulin measurements: can we address the impact of thyroid remnants?[J]. Nucl Med Commun, 2016, 37(6): 632-639.
[20]	Calò PG, Conzo G, Raffaelli M, et al. Total thyroidectomy alone versus ipsilateral versus bilateral prophylactic central neck dissection in clinically node-negative differentiated thyroid carcinoma: a retrospective multicenter study[J]. Eur J Surg Oncol, 2017, 43(1): 126-132.
[21]	Kim HI, Kim TH, Choe J H, et al. Restratification of survival prognosis of N1b papillary thyroid cancer by lateral lymph node ratio and largest lymph node size[J]. Cancer Med, 2017, 6(10): 2244-2251.
[22]	Perrier ND, Brierley JD, Tuttle RM. Differentiated and anaplastic thyroid carcinoma: major changes in the American joint committee on cancer eighth edition cancer staging manual[J]. CA Cancer J Clin, 2018, 68(1): 55-63.
[23]	Fatima N, Zaman MU, Zaman A, et al. Comparable ablation efficiency of 30 and 100 mCi of I-131 for low to intermediate risk thyroid cancers using triple negative criteria[J]. Asian Pac J Cancer Prev, 2016, 17(3): 1115-1118.
[24]	Aghaei A, Ayati N, Shafiei S, et al. Comparison of treatment efficacy 1 and 2 years after thyroid remnant ablation with 1110 versus 5550 MBq of iodine-131 in patients with intermediate-risk differentiated thyroid cancer[J]. Nucl Med Commun, 2017, 38(11): 927-931.
[25]	Abe K, Ishizaki U, Ono T, et al. Low-dose radioiodine therapy for patients with intermediate- to high-risk differentiated thyroid cancer[J]. Ann Nucl Med, 2020, 34(2): 144-151.
[26]	Iizuka Y, Katagiri T, Ogura K, et al. Comparison between the different doses of radioactive iodine ablation prescribed in patients with intermediate-to-high-risk differentiated thyroid cancer[J]. Ann Nucl Med, 2019, 33(7): 495-501.

Viewed

Full text

	From	local

	Times	43
	Rate	100%

Abstract

Just accepted	Online first	Issue

0	0	74

	From	Others

	Times	74
	Rate	100%

Cited

Web of Science	Crossref	ScienceDirect	Search for Citations in Google Scholar >>


This page requires you have already subscribed to WoS.

Shared

Discussed

Just accepted

Online first

Just accepted

Online first

[1]	巨艳丽,王丽华,成芳,黄凤艳,陈学禹,贾红英. 基于机器学习构建放射性碘治疗疗效的预测模型[J]. 山东大学学报 (医学版), 2023, 61(1): 94-99.
[2]	李建周，刘欣，张凌云，金勇君. 术前血清TSH浓度与分化型甲状腺癌的相关性研究[J]. 山东大学学报(医学版), 2011, 49(1): 10-13.