Radiomics-based prediction of response to multikinase inhibitors in radioiodine-refractory differentiated thyroid cancer patients.

Abstract

6077 Background: Antiangiogenic tyrosine kinase inhibitors (TKIs) represent the first-line treatment for radioiodine-refractory differentiated thyroid cancer (RR-DTC). Currently, no predictive factors for the activity of these drugs are available. We investigated whether radiomics may have a predictive role in this setting. Methods: We retrospectively identified patients (pts) affected by metastatic RR-DTC, treated with TKIs between July 2008 and January 2020 at our Institution, with availability of computed tomography (CT) scans at baseline and after at least 2 courses of TKI. Response to TKIs was evaluated according to RECIST v1.1. Pts with complete or partial response at the first radiological evaluation were considered responders (R), pts with stable or progressive disease non-responders (NR). A dedicated radiologist segmented the target lesions as regions of interest (ROIs). Radiomic features related to multiple categories (shape and size, first order statistics, textural features) were extracted from each ROI and computed using the PyRadiomics library v. 3.0. A semi-supervised form of principal component analysis estimated principal components that were then used for response classification through a k nearest neighbors (kNN) classifier. The quality of the model was assessed through train-validation-test split (55% of the data used as training set, 25% as validation set, 20% as test set), repeated 100 times. Performance of the predictive models was quantified with the mean Area Under the ROC Curve (AUC) obtained in the test set. Results: A total of 51 pts with metastatic RR-DTC who had received lenvatinib (n=37), sorafenib (n=4), axitinib (n=3), or vandetanib (n=7) were analyzed. Median age was 64.6 years, with a male prevalence (72.5%). Metastatic sites were lung (84.3%), bone (35.3%), brain (9.9%). Median time from TKI treatment start to the first radiological evaluation was 2.77 months, 24 pts (47%) were R (all partial responses) and 27 (52.9%) NR. In the radiomic analysis, 851 features were computed and 4-19 principal components were selected. Models’ performance of prediction of early response to TKIs is presented in Table. For each value of AUC, the corresponding 95% confidence interval is reported in brackets. Conclusions: Radiomics predicted the response to TKIs of RR-DTC pts with an accuracy of 71%. Radiomics technique has the potential to enable clinicians to anticipate the probability of response to TKIs at baseline, directing toward the most suitable patient-tailored therapeutic path. Prospective studies may further validate these preliminary findings.[Table: see text]