Blood Profiles in the Prediction of Radioiodine Refractory Papillary Thyroid Cancer: A Case–Control Study

Abstract

Purpose

Although most patients with papillary thyroid cancer can be cured by surgery and I-131 ablation, a small proportion will progress to radioactive iodine refractory (RAIR) thyroid cancer. The prediction of RAIR in its early stages can improve patient prognosis. The aim of this article is to evaluate the blood biomarkers in patients with RAIR and to establish a prediction model.

Patients and Methods

Data collected from patients with thyroid cancer that were enrolled from Jan. 2017 to Dec. 2021 were screened. RAIR was defined based on the criteria in the 2015 American Thyroid Association guidelines. The blood biomarkers from the study participants at three admissions timepoints (surgery and first and secondary I-131 ablations) were compared using both parametric and nonparametric tests to identify predictive factors for RAIR. Binary logistic regression analysis was used to construct a prediction model using parameters associated with surgical procedure decision. The model was then assessed with receiver operating characteristic curves.

Results

Thirty-six patients were included in the data analysis. Sixteen blood variables, including the low density lipoprotein-cholesterol-total cholesterol ratio, neutrophils, thyroglobulins, thyroglobulin antibody, thyroid peroxidase antibody, anion gap, etc., were revealed to be predictors for RAIR. The prediction model, which incorporated two parameters, reached an area under the curve of 0.861 (p<0.001).

Conclusion

Conventional blood biomarkers can be used in the prediction of early-stage RAIR. In addition, a prediction model incorporating multiple biomarkers can improve the predictive accuracy.

Keywords:

• thyroid carcinoma
• radioiodine refractoriness
• scoring system
• multivariable
• PTC

Abbreviations

95% CI, 95% confidence interval; AGPK, anion gap; Akt, protein kinase B; ATA, American Thyroid Association; AUC, area under the curve; BASO#, basophil count; BMI, body mass index; CBC, complete blood count; CCL2, C-motif chemokine ligand 2; CMP, comprehensive metabolic panel; CT, computed tomography; ETE, extrathyroidal extension; FDP, fibrin/fibrinogen degradation products; G-CSF, granulocyte colony stimulating factor; IQR, interquartile range; LDL-Ch/TCh, low density lipoprotein cholesterol-to-total cholesterol ratio; LIS, laboratory information system; LRP4, LDL receptor-related protein 4; MAPK, mitogen-activated protein kinase; MRI, magnetic resonance imaging; Na, serum sodium; Neu#, neutrophil count; NIS, sodium-iodide symporter; OR, odds ratio; PET, positron emission tomography; PTC, papillary thyroid cancer; RAI, radioactive iodine; RAIR, radioactive iodine refractoriness; ROC, receiver operating characteristic; SD, standard deviation; TCO2, total carbon dioxide, Tg2/Tg1, stimulated thyroglobulin at the second I-131-to-stimulated thyroglobulin at the first I-131 ratio; TG-Ab, thyroglobulin antibody; TGB, thyroglobulin; TPO-Ab, thyroid peroxidase antibody; TT, thrombin time; WBC, white blood cell; VitD3, 25-hydroxyvitamin D3.

Data Sharing Statement

All data generated or analyzed during this study are available from the corresponding author on reasonable requests.

Acknowledgments

We wish to thank Professor Jun Liang for her assistance and advice on the patient selection protocol.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

Prof. Dr Chuang Chen reports grants from Beijing Xisike Clinical Oncology Research Foundation, grants from Fundamental Research Funds for the Central Universities, grants from Science and Technology Major Project of Hubei Province, during the conduct of the study. The authors report no conflicts of interest in this work.

Additional information

Funding

This research was supported by the grants from Beijing Xisike Clinical Oncology Research Foundation (Y-SY201901-0189), the Fundamental Research Funds for the Central Universities (2042019kf0229), the Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies) (2019AEA170).