  The objective of this study was to develop an automated pipeline that
enhances thyroid disease classification using thyroid scintigraphy images,
aiming to decrease assessment time and increase diagnostic accuracy. Anterior
thyroid scintigraphy images from 2,643 patients were collected and categorized
into diffuse goiter (DG), multinodal goiter (MNG), and thyroiditis (TH) based
on clinical reports, and then segmented by an expert. A ResUNet model was
trained to perform auto-segmentation. Radiomic features were extracted from
both physician (scenario 1) and ResUNet segmentations (scenario 2), followed by
omitting highly correlated features using Spearman's correlation, and feature
selection using Recursive Feature Elimination (RFE) with XGBoost as the core.
All models were trained under leave-one-center-out cross-validation (LOCOCV)
scheme, where nine instances of algorithms were iteratively trained and
validated on data from eight centers and tested on the ninth for both scenarios
separately. Segmentation performance was assessed using the Dice similarity
coefficient (DSC), while classification performance was assessed using metrics,
such as precision, recall, F1-score, accuracy, area under the Receiver
Operating Characteristic (ROC AUC), and area under the precision-recall curve
(PRC AUC). ResUNet achieved DSC values of 0.84$\pm$0.03, 0.71$\pm$0.06, and
0.86$\pm$0.02 for MNG, TH, and DG, respectively. Classification in scenario 1
achieved an accuracy of 0.76$\pm$0.04 and a ROC AUC of 0.92$\pm$0.02 while in
scenario 2, classification yielded an accuracy of 0.74$\pm$0.05 and a ROC AUC
of 0.90$\pm$0.02. The automated pipeline demonstrated comparable performance to
physician segmentations on several classification metrics across different
classes, effectively reducing assessment time while maintaining high diagnostic
accuracy. Code available at: https://github.com/ahxmeds/thyroidiomics.git.
