Radiomic nomogram for discriminating parotid pleomorphic adenoma from parotid adenolymphoma based on grayscale ultrasonography

Yi Mao; LiPing Jiang; Jing-Ling Wang; Fang-Qun Chen; Wie-Ping Zhang; Zhi-Xing Liu; Chen Li

doi:10.3389/fonc.2023.1268789

Radiomic nomogram for discriminating parotid pleomorphic adenoma from parotid adenolymphoma based on grayscale ultrasonography

Front Oncol. 2024 Jan 11:13:1268789. doi: 10.3389/fonc.2023.1268789. eCollection 2023.

Authors

Yi Mao^#¹, LiPing Jiang^#¹, Jing-Ling Wang¹, Fang-Qun Chen¹, Wie-Ping Zhang¹, Zhi-Xing Liu^#^{1

2}, Chen Li^#¹

Affiliations

¹ Department of Ultrasound, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
² Department of Ultrasound, GanJiang New District Peoples Hospital, Nanchang, Jiangxi, China.

^# Contributed equally.

Abstract

Objectives: To differentiate parotid pleomorphic adenoma (PA) from adenolymphoma (AL) using radiomics of grayscale ultrasonography in combination with clinical features.

Methods: This retrospective study aimed to analyze the clinical and radiographic characteristics of 162 cases from December 2019 to March 2023. The study population consisted of a training cohort of 113 patients and a validation cohort of 49 patients. Grayscale ultrasonography was processed using ITP-Snap software and Python to delineate regions of interest (ROIs) and extract radiomic features. Univariate analysis, Spearman's correlation, greedy recursive elimination strategy, and least absolute shrinkage and selection operator (LASSO) correlation were employed to select relevant radiographic features. Subsequently, eight machine learning methods (LR, SVM, KNN, RandomForest, ExtraTrees, XGBoost, LightGBM, and MLP) were employed to build a quantitative radiomic model using the selected features. A radiomic nomogram was developed through the utilization of multivariate logistic regression analysis, integrating both clinical and radiomic data. The accuracy of the nomogram was assessed using receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and the Hosmer-Lemeshow test.

Results: To differentiate PA from AL, the radiomic model using SVM showed optimal discriminatory ability (accuracy = 0.929 and 0.857, sensitivity = 0.946 and 0.800, specificity = 0.921 and 0.897, positive predictive value = 0.854 and 0.842, and negative predictive value = 0.972 and 0.867 in the training and validation cohorts, respectively). A nomogram incorporating rad-Signature and clinical features achieved an area under the ROC curve (AUC) of 0.983 (95% confidence interval [CI]: 0.965-1) and 0.910 (95% CI: 0.830-0.990) in the training and validation cohorts, respectively. Decision curve analysis showed that the nomogram and radiomic model outperformed the clinical-factor model in terms of clinical usefulness.

Conclusion: A nomogram based on grayscale ultrasonic radiomics and clinical features served as a non-invasive tool capable of differentiating PA and AL.

Keywords: nomogram; parotid tumor; radiomics; ultrasonography; wavelet transformation.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study is supported by the Key Research and Development Program of Jiangxi Province (20203BBGL73196PSS) and Youth Science Foundation of Jiangxi Province (GJJ210218) and Key Science and Technology Research Project in Jiangxi Province Department of Education (GJJ2200114).