Improving the diagnosis of common parotid tumors via the combination of CT image biomarkers and clinical parameters

• Published in: BMC medical imaging Vol. 20; no. 1; p. 38
• Main Authors: Zhang, Dan, Li, Xiaojiao, Lv, Liang, Yu, Jiayi, Yang, Chao, Xiong, Hua, Liao, Ruikun, Zhou, Bi, Huang, Xianlong, Liu, Xiaoshuang, Tang, Zhuoyue
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 15.04.2020; BioMed Central; BMC
• Subjects: Adenoma; Age; Biological markers; Biomarkers; Blood vessels; CAT scans; Chi-square test; Clinical parameters; Computed tomography; Computer industry; Data analysis; Datasets; Diagnosis; Diagnostic systems; Energy; Entropy; Feature extraction; Gender; Image biomarkers; Mathematical models; Medical diagnosis; Medical imaging; Medical imaging equipment; Medical records; Multivariate analysis; Oral cancer; Parameters; Parotid tumors; Patients; Software; Statistical tests; Surgery; Tumors; United States; Taiwan; United States > US; Clinical parameters; Parotid tumors; Image biomarkers
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-020-00442-x

Our study aims to develop and validate diagnostic models of the common parotid tumors based on whole-volume CT textural image biomarkers (IBMs) in combination with clinical parameters at a single institution. The study cohort was composed of 51 pleomorphic adenoma (PA) patients and 42 Warthin tumor (WT) patients. Clinical parameters and conventional image features were scored retrospectively and textural IBMs were extracted from CT images of arterial phase. Independent-samples t test or Chi-square test was used for evaluating the significance of the difference among clinical parameters, conventional CT image features, and textural IBMs. The diagnostic performance of univariate model and multivariate model was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC). Significant differences were found in clinical parameters (age, gender, disease duration, smoking), conventional image features (site, maximum diameter, time-density curve, peripheral vessels sign) and textural IBMs (mean, uniformity, energy, entropy) between PA group and WT group (P<0.05). ROC analysis showed that clinical parameter (age) and quantitative textural IBMs (mean, energy, entropy) were able to categorize the patients into PA group and WT group, with the AUC of 0.784, 0.902, 0.910, 0.805, respectively. When IBMs were added in clinical model, the multivariate models including age-mean and age-energy performed significantly better than the univariate models with the improved AUC of 0.940, 0.944, respectively (P<0.001). Both clinical parameter and CT textural IBMs can be used for the preoperative, noninvasive diagnosis of parotid PA and WT. The diagnostic performance of textural IBM model was obviously better than that of clinical model and conventional image model in this study. While the multivariate model consisted of clinical parameter and textural IBM had the optimal diagnostic performance, which would contribute to the better selection of individualized surgery program.