Patient selection
This retrospective study was approved by the local ethics review board. The informed consent was waived for this single-institution study due to its retrospective nature. All data of patients were used confidentially and anonymously. The research involved no more than minimal risk to the patients. Meanwhile, the waiver did not adversely affect the rights and welfare of the patients. Clinical and image data of all patients were obtained through medical record system and follow-up. Between January 2016 and May 2017, patients with PA or WT who underwent surgery were eligible and identified from the institution’s database.
The inclusion criteria were as follows: (1) confirmed PA or WT with postoperative pathological diagnosis; (2) contrast-enhanced CT images of neck containing parotid gland obtained within two weeks prior to surgery; and (3) maximum diameter of lesions ≥1.0cm. The exclusion criteria were as follows: (1) CT images with obvious artifacts, such as false teeth artifacts, motion artifacts, etc; and (2) lesions with scarcely solid components which are difficult for texture analysis. As a result, a total of 122 patients were identified, and 29 patients were excluded (Fig. 1). The final study population comprised 93 patients.
Image acquisition
CT scans were performed using 64/128-multidetector scanners (LightSpeed VCT; GE Healthcare, Waukesha, WI, USA) and the parameters were as follows: tube current, 150 mA; tube voltage, 120 kVp; section thickness, 5 mm; section interval, 5 mm.
The scanning ranges from the base of the skull to the entrance to the thorax. The enhanced images were obtained after intravenous injection of 80–100 mL of nonionic contrast material (320 mg/ mL; Iopamidol, Shanghai Bracco Sine Pharmaceutical Co., Ltd., Shanghai, China) at an injection rate of 3.0 mL/s, followed by a 50 mL saline chaser. The contrast-enhanced CT images were obtained at 35 and 120 seconds after contrast material injection in arterial phase and balanced phase, respectively.
Clinical parameters
All clinical parameters including age, gender, disease duration, and smoking status were collected from medical record system.
Conventional CT image features
All conventional CT image features were derived from the original CT image data, including tumor site (in the parotid tail or not), maximum diameter, time-density curve (washout type or not), and peripheral vessels sign, which defined as increased tortuous vascular shadows clinging to the edge of the lesion.
CT textural image biomarkers
CT images of arterial phase of all patients were stored in Digital Imaging and Communications in Medicine (DICOM) format and uploaded to ITK-SNAP software for three-dimensional manual segmentation of the region of interest (ROI). An ROI was manually drawn to cover the tumor as large as possible, keeping a distance of 1mm from the boundary and carefully avoiding the retromandibular vein and too much parotid parenchyma into the lesion, which may lead to a misunderstanding of the internal structure of the tumor and affect the accuracy of texture analysis. The ROI of each case was manually drawn by a head and neck radiologist who did not have any knowledge about the clinical information of patients, and then the segmentation was checked by a senior radiologist. Areas of tumor heterogeneity, including cystic change or necrosis, were not excluded, for the information captured with texture analysis could potentially contribute to tumor discrimination and classification. An in-house software, Matlab2017b (Mathworks, Natick, MA, USA), was used to extract the texture parameters automatically. Six frequently-used texture parameters obtained from the gray-level histogram were included in the study, namely uniformity (a measure of the sum of the squares of each intensity value), mean (the average gray level intensity within the ROI), energy (a measure of the magnitude of voxel values in an image), entropy (the distribution of gray levels within the VOI), skewness (the histogram asymmetry degree around the mean), and kurtosis (a measurement of the histogram sharpness). An overview of the textural IBM extraction process and analysis is shown in Fig. 2.
Data analysis
The data were analyzed by SPSS 22.0 software (IBM, Chicago). Kolmogorov-Smirnov test was used for intra-group normality test, and Levene test was used for intra-group variance homogeneity test. Parameters with normal distribution and homogeneity of variances were expressed as mean±standard deviation, and independent-samples t test was adopted for data analysis. Parameters that did not satisfy normal distribution and uneven variance were expressed by median and interquartile spacing. The two-independent-samples Mann-Whitney U Test was used for data analysis. Qualitative data were presented as ratios, which were analyzed by chi-square test. As to clinical parameter (gender) - textural IBM models, a two-component diagnostic model was fitted with binary Logistic regression analysis. The diagnostic performance of each index was tested via receiver operating characteristic (ROC) analysis. Cutoff values were established by calculating the maximal Youden index (Youden index=sensitivity+specifcity-1). A P value was considered significant if it was less than 0.05.
Step 1 Clinical model
Potential clinical parameters that were considered for their diagnostic ability in the PA and WT datasets included age (>median vs.≤median), gender (female vs. male), disease duration (>median vs.≤median), and smoking status (yes vs. no).
Step 2 conventional image model
Potential conventional CT image features that were considered for their diagnostic ability in the PA and WT datasets included tumor site (in the parotid tail, which can be defined as inferior 2 cm of the superficial lobe of the gland, vs. not), maximum diameter (>median vs. ≤median), time-density curve (TDC) (washout type vs. not), peripheral vessels sign, which is defined as increased tortuous vascular shadow clinging to the edge of lesion in the arterial phase (yes vs. no).
Step 3 textural IBM model
Six texture features namely mean, uniformity, energy, entropy, skewness and kurtosis were calculated and selected. The potential textural IBMs were analyzed for their diagnostic power, and the median values (>median vs.≤median) in the WT and PA datasets were regarded as the threshold value in the univariate analysis.
Step 4 Combined models
According to the ROC analysis and AUC, the potential clinical parameters, conventional image features, and textural IBMs were included in the multivariate analysis to create combined models, and the optimal one was selected out.