A digital pathology model for predicting radioiodine-avid metastases on initial post-therapeutic I scan in patients with papillary thyroid cancer.

Accurate postoperative assessment is critical for optimizing I therapy in patients with papillary thyroid cancer (PTC). This study aimed to develop a pathology model utilizing postoperative digital pathology slides to predict lymph node and/or distant metastases on post-therapeutic I scan after initial I treatment in PTC patients. A retrospective analysis was conducted on 229 PTC patients who underwent total or near-total thyroidectomy and subsequent I treatment after levothyroxine (LT4) withdrawal between January 2022 and August 2023. The pathology model was developed through two stages: patch-level prediction and WSI-level prediction. The clinical model was constructed using statistically significant variables identified from univariate and multivariate logistic regression analysis. Of the 229 patients, 19.6% (45/229) exhibited I-avid metastatic foci in post-therapeutic I scan. Multifactorial analysis identified stimulated thyroglobulin (sTg) as the sole independent risk factor. The AUC of the pathology model in the training and test cohorts were 0.976 (95% CI 0.948-1.000) and 0.805 (95% CI 0.660-0.951), respectively, which were significantly higher than the clinical model (AUC 0.652 and 0.548, Pall < 0.05). This model has the potential to serve as a valuable tool for clinicians in tailoring treatment strategies, thereby optimizing therapeutic outcomes for PTC patients.