A multimodal study on predicting extrathyroidal extension of papillary thyroid carcinoma based on radiopathomics

We aimed to develop and validate a radiopathomics model for predicting extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC).

This retrospective study included 388 PTC patients with preoperative ultrasound and 400× cytology images from five medical centers between June 2017 and April 2024. We analyzed ultrasound and cytology images using Python and CellProfiler to extract features. Feature selection was performed using univariate analysis, Spearman correlation, and LASSO regression. The XGBoost algorithm was then used to build radiomics, pathomics, and combined radiopathomics models. The diagnostic performance of the radiopathomics model was compared with that of radiologists in an external validation cohort. Model and radiologist performance was evaluated using the area under the receiver operating characteristic curve (AUC). The radiopathomics model was visualized and interpreted through SHAP analysis.

The radiopathomics model selected 21 features for construction. The AUC of the radiopathomics model was 0.887, 0.857, and 0.873 in the training, internal validation, and external validation cohorts, respectively, exceeding those of the single radiomics model (0.824, 0.787, and 0.804) and the pathomics model (0.809, 0.811, and 0.794). Compared with radiologists, the radiopathomics model improved the mean accuracy from 0.661 to 0.821. SHAP analysis showed that radiomics features played a major role in diagnosing ETE, while pathomics features provided additional support.

The radiopathomics model serves as a promising auxiliary tool for preoperative ETE risk stratification and can help improve radiologists' diagnostic performance.

Not Applicable.