Predictive Model For Preliminary Assessment of Repeated Radioiodine Therapy Course Based On 131-I Suv

Abstract

Purposes: Current research design is dedicated to 131-I SUV evaluation on post therapeutic scintigrams and setting up predictive model for radioiodine therapy repeated course prescription in differentiated thyroid cancer (DTC). Methods: Study includes 148 patients (f-105, m-43) with DTC treated with 131-iodine. Administered treatment activities of 131I calculated according to clinical features and tumor recurrence risk group. Patients were divided into three groups using ATA 2018 recommendations. Absolute risk groups: low risk (L), medium risk (M), high risk (H). Administered 131-I activity [MBq]: <AL> = 3223±729, <AM> = 3696±456, <AH> = 4589±1078; Tg [ng/ml]: <TgL> = 7,4±1.7 ng/ml, <TgM>= 14,8 ±5,9, <TgH> = 68,3 ±18,5; TgAb [IU/ml]: <TgAbL> = 124,3 ± 81.7, <TgAbM> = 29,2 ±15,9, <TgAbH> = 85,7 ±28,9. All null hypothesis were checked using paired Mann-Whitney U-test. Calibration of system SPECT/CT and evaluation SUVs completed according to protocols designed using Jaszczak phantom Deluxe. Model development based on logistic regression with ROC-analysis, regularization and cross-validation. Results: Reference intervals of SUVpeak and SUVmax calculated for all groups of risk. SUVpeak: low risk >155, medium risk 105-155, high risk 0-105 pL-M=0.069 pL-H=0.0037 pM-H=0.7514; SUVmax: low risk >38, medium risk 29-38, high risk 0-29 pL-M=0.052 pL-H=0.0033 pM-H=0.949. Logit model based on SUVpeak without regularization has: AUC = 0.67 (95%CI 0.33-1.00); Accuracy = 0.82; SE = 0.89; SP = 0.4; PPV = 0.41; NPV = 0.89, cross-validated AUC =0.67 (0.4-0.88), regression coefficients: B0=0.037, B1=0.001. Regularization (SUVpeak<100) lead to AUC = 0.75, (95%CI 0.44-1.00); accuracy = 0.89; SE =0.98; SP = 0.4; PPV = 0.76; NPV = 0.87, cross-validated AUC = 0.513 (0.36-0.71) regression coefficients: B0=0.473, B1=0.003.Conclusion: Study shows that SUV has wide range of values and can be matched with existed model of risk assessment of DTC. Algorithm of image segmentation and evaluation of SUVpeak and SUVmax for SPECT/CT systems was developed. According to the ROC analysis, developed predictive model shows an acceptable performance for further clinical investigation and advancement focused on refining model parameters and introducing additional predictors