Purpose: Radioiodine ablation treatment (RAT) is administered to papillary thyroid carcinoma patients post thyroidectomy. Multivariable logistic regression analysis can be applied to predict treatment failure. In this study, we propose a logistic regression model (LRM) to estimate the probability of repeating the treatment more than one time.
Materials and methods: A retrospective review of the last 5 years of RAT data revealed that 30 patients had received the RAT more than one time. Various factors including age, sex, pretreatment serum thyroglobulin (Tg), thyroid-stimulating hormone (TSH) and administered activity were analyzed to predict RAT failure and therefore the necessity to repeat the treatment by administering additional doses of radioiodine.
Results: The administered activity, the patient age, the presence of distant lymph nodes on the whole-body radioiodine scan (WBS) and the level of Tg before the treatment were found to be the predictive variables. The following LRM is proposed: Y = 7.8295 - 0.0012 [Activity in (MBq) - 0.0541 (Age) - 34.3 (Lymph Nodes) - 0.0042 (Tg)]. The prediction accuracy of the LRM was assessed using receiver operating characteristic (ROC) curve by calculating the area under the curve (AUC). We found the AUC = 0.8972.
Conclusion: Patients who are older in age, who receive higher administered radioiodine activity, have higher serum thyroglobulin levels and have lymph node uptake reported in their post-ablation WBS are more likely to have unsuccessful treatment outcome and will repeat the treatment. This LRM could help in adjusting RAT options in order to reduce the repeat rate.