An Ultrasound Radiomics Model for the Early Diagnosis of Cervical Cancer

Abstract

We evaluated whether image-radiomics features extracted from ultrasound with integrated genomic data of single nucleotide polymorphisms (SNPs) associated with CIN susceptibility and clinical features could improve the differential diagnosis of high-grade intraepithelial disease (HSIL) and stage IA CC. Models were developed from ultrasound-derived radiomic features, SNPs data and clinical variables. After random 7:3 allocation into training and validation sets, clinical and SNPs datasets were each screened by univariable then multivariable logistic regression to build separate predictors. Ultrasound radiomics features were reduced with Max-relevance and min-redundancy (mRMR) and least absolute contraction and selection operator (LASSO) to generate an ultrasound radiomic score, which was subsequently used with clinical and SNPs data to establish the combined model. For the differentiation of HSIL and early CC models, only the ultrasound radiomics model showed higher classification efficiency, which the performance in the validation cohort (AUC: 0.885 [95%CI: 0.751–1.000]) than the method combining ultrasound radiomics score, clinical data and SNPs data with an AUC value of 0.850[95%CI: 0.713–0.987]. The model developed and constructed in this study, based on ultrasound radiomics, demonstrates potential for differentiating HSIL from Stage IA CC and exhibits significant clinical application value.