Photo Credit: Claudio Ventrella

The following is a summary of “Imprinted gene detection effectively improves the diagnostic accuracy for papillary thyroid carcinoma,” published in the March 2024 issue of Oncology by Chen et al.

Papillary thyroid carcinoma (PTC) is the most prevalent form of thyroid carcinoma, yet despite advancements in diagnostic methods, diagnostic challenges persist for specific nodules. This study addressed this gap by developing and validating a robust prediction model to optimize PTC diagnosis.

The development and validation cohorts, drawn from two centers between August 2019 and February 2022, involved 152 thyroid nodules assessed through postoperative pathological examination. They incorporated patient data spanning general information, cytopathology, imprinted gene detection, and ultrasound features to construct a predictive model for PTC. Employing multivariate logistic regression analysis with a bidirectional elimination approach, researchers identified key predictors and established the model.

The comprehensive prediction model integrates predictors such as nodule composition, microcalcification, imprinted gene detection, and cytopathology. Demonstrating exceptional performance, the model yielded an area under the curve (AUC) of 0.98, with a sensitivity of 97.0%, specificity of 89.5%, and accuracy of 94.4%. Furthermore, robust performance was affirmed through internal and external validations. Notably, imprinted gene detection’s novel inclusion significantly enhanced PTC diagnosis.

In conclusion, this study presents a validated, comprehensive prediction model for PTC, complemented by a visualized nomogram for clinical utility. Leveraging imprinted gene detection, the prediction model effectively improves PTC diagnosis, particularly for cases undetermined by current diagnostic modalities.

Source: bmccancer.biomedcentral.com/articles/10.1186/s12885-024-12032-z