Discussion
Tumor, PTC with ETE, papillary thyroid cancer, ETE of papillary thyroid carcinoma, PTC
RECURRENCE, PAPILLARY THYROID CANCER, TUMOR
Recurrence and mortality rates for PTC patients with ETE are higher than those without ETE Ultrasound is the preferred imaging technique for diagnosing PTC Based on US (Ultrasound) radiomics, we created four prediction models for ETE of papillary thyroid carcinoma in children Each algorithm for machine learning has its own advantages and disadvantages Our study has several limitations: (1) The algorithm for feature selection also influences the performance of the model. We did not compare the dimensionality reduction algorithms; consequently, the final feature selection may not be optimal. (2) Because of the retrospective nature of the study, case selection bias could potentially confound the results. Tumor boundaries were ill-defined in some instances with PTC. These cases were omitted from the analysis. This study was biased because the majority of the patients had PTC with ETE. (4) The radiomic model we created to distinguish ETE was created and verified at a single medical center. (5) Grayscale ultrasound images were used in our research; however, the radiomic features of multimodal ultrasound will be incorporated into the nomogram in the future. We want to use elastography and contrast-enhanced ultrasound pictures, both of which may provide more radiomic properties than standard 2D scans. (6) The sample size of this study is too small; more extensive multicenter investigations with larger samples are needed.In conclusion, this research uses four machine-learning models of US radiomics to predict the ETE of papillary thyroid cancer in children. LightGBM-based radiomics models outperform the other three most used machine-learning algorithms in radiomics when applied to prediction accuracy.