Work

Diagnostic efficacy of data mining method based on multimodal ultrasound for papillary thyroid carcinoma

Year: 2024

Type: article

Abstract: Objective The incidence of papillary thyroid caracinoma (PTC) is increasing year by year. Logistic regression model and Chi-squared automatic interaction (CHAID) decision tree based on multimodal ultrasound were established, and the diagnostic efficiency of the two models in PTC was compared. Methods The findings, features and data of routine ultrasound, shear wave elastography (SWE) and contrast-enhanced ultrasonography (CEUS) were prospectively collected in 203 patients. Including: echogenicity, aspect ratio, maximum diameter of tumor, boundary, morphology, focal hyperecho, blood flow grading, maximum elasticity (Emax), minimum elastcity (Emin), mean elasticity (Emean), enhancement degree, enhanced characteristics, distribution of contrast agent, contrast medium arrival time. According to the pathological results, they were divided into PTC group and non-PTC group. CHAID decision tree model and binary Logistic regression model were established, receiver operator characteristic (ROC) curves of the two models were drawn, and diagnostic effectiveness was evaluated by comparing area under curve (AUC). Results Logistic regression showed that hypoechoic or very hypoechoic, aspect ratio ≥1, microcalcification and high SWE value were risk factors for PTC (OR 8.604, 2.154, 2.297, 1.067, respectively, P < 0.05). The CHAID decision tree showed echo, aspect ratio, Emax, contrast agent distribution and infusion time combined to diagnose PTC. ROC curve showed that the AUC of PTC predicted by Logistic regression model and CHAID decision tree model was 0.878 and 0.883, respectively, with no statistical significance (z=0.325, P=0.7456). Conclusion Both Logistic regression model and CHAID decision tree model can play a good role in the diagnosis of PTC based on multi-modal ultrasound, but the diagnostic efficiency of both models is comparable. In conclusion, these two models provide new insights and ideas for PTC diagnosis.

Source: Frontiers in Oncology

DOI: 10.3389/fonc.2024.1439825