Journal of Shandong University (Health Sciences) ›› 2023, Vol. 61 ›› Issue (1): 94-99.doi: 10.6040/j.issn.1671-7554.0.2022.1111

• 公共卫生与管理学 • Previous Articles    

Construction of predictive models of radioiodine therapy based on machine learning

JU Yanli1, WANG Lihua1, CHENG Fang1, HUANG Fengyan1, CHEN Xueyu1, JIA Hongying1,2   

  1. 1. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. Center of Evidence-Based Medicine, The Second Hospital, Shandong University, Jinan 250033, Shandong, China
  • Published:2023-01-10

Abstract: Objective To construct the predictive models for radioactive iodine therapy(RAI)efficacy by machine learning algorithms based on clinical data and radiological parameters of patients with differentiated thyroid cancer(DTC). Methods A total of 1,642 DTC patients treated with RAI during Dec. 2015 and Dec. 2020 were collected. The efficacy was evaluated 6 months after RAI and the core features associated with efficacy were screened out for machine-learning modeling. The patients were divided into the training set(n=973)and validation set(n=669)according to the consultation time(July 2019). In the training set, the Logistic model, random forest model, support vector machine model and Adaboost model were constructed, whose performances were evaluated with the area under the receiver operating characteristic curve(AUC), accuracy and specificity. The calibration and decision curves were drawn to evaluate the accuracy and clinical benefits of the models. The external stability was assessed in the validation set. Results The four predictive models had great predictive performance, having higher predictive accuracy and net benefits than the tumor-node-metastasis(TNM)staging and recurrence risk stratification. The Logistic model was the most effective, with an AUC of 0.827 in the training set and 0.869 in the validation set. Conclusion Predictive models for RAI efficacy built on machine learning have great predictive performance. The developed nomogram can realize individualized and accurate prediction for the therapeutic efficacy of RAI for DTC.

Key words: Radioactive iodine therapy, Machine learning, Differentiated thyroid cancer, Predictive model, Therapeutic efficacy

CLC Number: 

  • R736.1
[1] Cabanillas ME, McFadden DG, Durante C. Thyroid cancer [J]. Lancet, 2016, 388(10061): 2783-2795.
[2] Jonklaas J, Sarlis NJ, Litofsky D, et al. Outcomes of patients with differentiated thyroid carcinoma following initial therapy [J]. Thyroid, 2006, 16(12): 1229-1242.
[3] Ito Y, Higashiyama T, Takamura Y, et al. Prognosis of patients with papillary thyroid carcinoma showing postoperative recurrence to the central neck [J]. World J Surg, 2011, 35(4): 767-772.
[4] Yang X, Liang J, Li T, et al. Preablative stimulated thyroglobulin correlates to new therapy response system in differentiated thyroid cancer [J]. J Clin Endocrinol Metab, 2016, 101(3): 1307-1313.
[5] Van CB, Wynants L. Machine learning in medicine [J]. N Engl J Med, 2019, 380(26): 2588. doi: 10.1161/CIRCULATIONAHA.115.001593.
[6] May M. Eight ways machine learning is assisting medicine [J]. Nat Med, 2021, 27(1): 2-3.
[7] Stephen JS, Mireia CO, Suet FC, et al. Multi-omic machine learning predictor of breast cancer therapy response [J]. Nature, 2022, 601(7894): 623-629.
[8] Chaudhary K, Poirion OB, Lu L, et al. Deep learning-based multi-omics integration robustly predicts survival in liver cancer [J]. Clin Cancer Res, 2018, 24(6): 1248-1259.
[9] Kather JN, Pearson AT, Halama N, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer [J]. Nat Med, 2019, 25(7): 1054-1056.
[10] Gould MK, Huang BZ, Tammemagi MC, et al. Machine learning for early lung cancer identification using routine clinical and laboratory data [J]. Am J Respir Crit Care Med, 2021, 204(4): 445-453.
[11] 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.
[12] 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.
[13] Tuttle RM, Haugen B, Perrier ND. Updated American joint committee on cancer/tumor-node-metastasis staging system for differentiated and anaplastic thyroid cancer(eighth edition): what changed and why [J]. Thyroid, 2017, 27(6): 751-756.
[14] Cooper DS, Doherty GM, Haugen BR, et al. Revised American thyroid association management guidelines for patients with thyroid nodules and differentiated thyroid cancer [J]. Thyroid, 2009, 19(11): 1167-1214.
[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] Tuttle RM, Alzahrani AS. Risk stratification in differentiated thyroid cancer: from detection to final follow-up [J]. J Clin Endocrinol Metab, 2019, 104(9): 4087-4100.
[17] 林岩松, 李娇. 2015年美国甲状腺学会《成人甲状腺结节与分化型甲状腺癌诊治指南》解读:分化型甲状腺癌~131I治疗新进展[J]. 中国癌症杂志, 2016, 26(1): 1-12. LIN Yansong, LI Jiao. The interpretation of 2015 American thyroid association management guidelines for adult patients with thyroid nodules and differentiated thyroid carcinoma: new progress in radioactive iodine therapy of differentiated thyroid carcinoma [J]. China Oncology, 2016, 26(1): 1-12.
[18] Shen FC, Hsieh CJ, Huang IC, et al. Dynamic risk estimates of outcome in Chinese patients with well-differentiated thyroid cancer after total thyroidectomy and radioactive iodine remnant ablation [J]. Thyroid, 2017, 27(4): 531-536.
[19] 周倩, 王瑞华, 刘保平, 等. 高危分化型甲状腺癌手术及131I治疗后疗效分类及影响因素分析[J]. 中华核医学与分子影像杂志, 2021, 41(11): 664-669. ZHOU Qian, WANG Ruihua, LIU Baoping, et al. Classification of therapeutic effect and influencing factors in patients with high-risk differentiated thyroid carcinoma after surgery and 131I treat [J]. Chinese Journal of Nuclear Medicine and Molecular Imaging, 2021, 41(11): 664-669.
[20] Prpi c M, Franceschi M, Romi c M, et al. Thyroglobulin as a tumor marker in differentiated thyroid cancer-clinical considerations [J]. Acta Clin Croat, 2018,57(3): 518-527.
[21] Wong KCW, Ng TY, Yu KS, et al. The use of post-ablation stimulated thyroglobulin in predicting clinical outcomes in differentiated thyroid carcinoma-what cut-off values should we use [J]. Clin Oncol(R Coll Radiol), 2019, 31(2): e11-e20.
[22] Jayasekara J, Jonker P, Lin JF, et al. Early postoperative stimulated serum thyroglobulin quantifies risk of recurrence in papillary thyroid cancer [J]. Surgery, 2020, 167(1): 40-45.
[23] Nóbrega G, Cavalcanti M, Leite V, et al. Value of stimulated pre-ablation thyroglobulin as a prognostic marker in patients with differentiated thyroid carcinoma treated with radioiodine [J]. Endocrine, 2022, 76(3): 642-647.
[24] Pan M, Li Z, Jia M, et al. Combination of stimulated thyroglobulin and antithyroglobulin antibody predicts the efficacy and prognosis of(131)I therapy in patients with differentiated thyroid cancer following total thyroidectomy: a retrospective study [J]. Front Endocrinol(Lausanne), 2022, 13: 857057. doi: 10.3389/fendo.2022.857057.
[25] 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.
[1] Li KUANG,Xiaoming XU,Qi ZENG. Review of machine learning used in the field of suicide [J]. Journal of Shandong University (Health Sciences), 2022, 60(4): 10-16.
[2] JIANG Zhen, SUN Jing, ZOU Wen, WANG Changchang, GAO Qi. A comparison study of suicidal behavior predictive models of bipolar disorder patients based on two machine learning algorithms [J]. Journal of Shandong University (Health Sciences), 2022, 60(1): 101-108.
[3] LI Wanwan, ZHOU Wenkai, DONG Shuqing, HE Shiqing, LIU Zhao, ZHANG Jiaxin, LIU Bin. Construct of a risk assessment model of breast cancer immune-related lncRNAs based on the database information [J]. Journal of Shandong University (Health Sciences), 2021, 59(7): 74-84.
[4] TIAN Yaotian, WANG Bao, LI Yeqin, WANG Teng, TIAN Liwen, HAN Bo, WANG Cuiyan. Machine learning models based on interpretive CMR parameters can predict the prognosis of pediatric myocarditis [J]. Journal of Shandong University (Health Sciences), 2021, 59(7): 43-49.
[5] Wei ZHANG,Wenhao TAN,Yibin LI. Locmotion control of quadruped robot based on deep reinforcement learning: review and prospect [J]. Journal of Shandong University (Health Sciences), 2020, 1(8): 61-66.
[6] Qiang WU,Zekun HE,Ju LIU,Xiaomeng CUI,Shuang SUN,Wei SHI. A research on multi-modal MRI analysis based on machine learning for brain glioma [J]. Journal of Shandong University (Health Sciences), 2020, 1(8): 81-87.
[7] LI Pan, LI Yueyue, LI Yanqing. Effect of tailored bowel preparation on bowel preparation quality [J]. Journal of Shandong University (Health Sciences), 2020, 58(3): 113-117.
[8] Haotian LIN,Longhui LI,Jingjing CHEN. Research progress of artificial intelligence in childhood eye diseases [J]. Journal of Shandong University (Health Sciences), 2020, 58(11): 11-16.
[9] YANG Yang, ZHANG Guang, ZHANG Chengqi, SONG Xinhong, XUE Fuzhong, WANG Ping, WANG Li, LIU Yanxun. A prediction model for type 2 diabetes risks: a cohort study based on health examination [J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2016, 54(9): 69-72.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!