CT-based radiomics with various classifiers for histological differentiation of parotid gland tumors

Abstract

ObjectiveThis study assessed whether radiomics features could stratify parotid gland tumours accurately based on only noncontrast CT images and validated the best classifier of different radiomics models.MethodsIn this single-centre study, we retrospectively recruited 249 patients with a diagnosis of pleomorphic adenoma (PA), Warthin tumour (WT), basal cell adenoma (BCA) or malignant parotid gland tumours (MPGTs) from June 2020 to August 2022. Each patient was randomly classified into training and testing cohorts at a ratio of 7:3, and then, pairwise comparisons in different parotid tumour groups were performed. CT images were transferred to 3D-Slicer software and the region of interest was manually drawn for feature extraction. Feature selection methods were performed using the intraclass correlation coefficient, t test and least absolute shrinkage and selection operator. Five common classifiers, namely, random forest (RF), support vector machine (SVM), logistic regression (LR), K-nearest neighbours (KNN) and general Bayesian network (Gnb), were selected to build different radiomics models. The receiver operating characteristic curve, area under the curve (AUC), accuracy, sensitivity, specificity and F-1 score were used to assess the prediction performances of these models. The calibration of the model was calculated by the Hosmer–Lemeshow test. DeLong’s test was utilized for comparing the AUCs.ResultsThe radiomics model based on the RF, SVM, Gnb, LR, LR and RF classifiers obtained the highest AUC in differentiating PA from MPGTs, WT from MPGTs, BCA from MPGTs, PA from WT, PA from BCA, and WT from BCA, respectively. Accordingly, the AUC and the accuracy of the model for each classifier were 0.834 and 0.71, 0.893 and 0.79, 0.844 and 0.79, 0.902 and 0.88, 0.602 and 0.68, and 0.861 and 0.94, respectively.ConclusionOur study demonstrated that noncontrast CT-based radiomics could stratify refined pathological types of parotid tumours well but could not sufficiently differentiate PA from BCA. Different classifiers had the best diagnostic performance for different parotid tumours. Our study findings add to the current knowledge on the differential diagnosis of parotid tumours.

Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast‐enhanced CT imaging: A multicenter study

“…Several studies have utilized CT imaging to discern benign PTs, showcasing high diagnostic performance. 40,14,39 Although the diagnostic efficiency of the combined model in this study is somewhat lower than certain models in the aforementioned omics-related studies, the current application of radiomics in clinical practice remains intricate, characterized by substantial labor and economic costs. Furthermore, given the rarity of PTs and the lack of extensive data support, diagnostic models constructed based on radiomics still grapple with issues of stability and robustness.…”

“…This aligns with the findings of Yu et al., 39 who reported similar results in their investigation into the differentiation of benign and malignant PTs. Several studies have utilized CT imaging to discern benign PTs, showcasing high diagnostic performance 40,14,39 . Although the diagnostic efficiency of the combined model in this study is somewhat lower than certain models in the aforementioned omics‐related studies, the current application of radiomics in clinical practice remains intricate, characterized by substantial labor and economic costs.…”

Differentiation of benign and malignant parotid gland tumors based on the fusion of radiomics and deep learning features on ultrasound images

“…Lu et al. ( 20 ) conducted radiomics analysis of PGTs employing five common machine learning classifiers based on plain CT images and observed variations in optimal classification efficacy among different subtypes of PGTs across these classifiers. Notably, the RandomForest model achieved the highest AUC (0.834) in distinguishing between BPGTs and MPGTs, indicating that model performance may be influenced by key tumor features as well as algorithmic characteristics inherent to each classifier.…”

“…In this study, through univariate and multivariate logistic regression analysis, irregular shape, poorly-defined margin, and absence of posterior acoustic enhancement were identified as independent Radiomics is the process that converts digital medical images into high-dimensional, mineable data. Numerous domestic and international studies have investigated its application in distinguishing PGTs (18)(19)(20)(21). Qi et al (19) conducted a study to differentiate between BPGTs and MPGTs, as well as different subtypes of benign tumors.…”

“…By utilizing machine learning models, radiomics facilitates non-invasive assessment of various biological behaviors associated with tumors, making it widely applicable in early diagnosis, prognosis prediction, and treatment evaluation (15)(16)(17). While several scholars have conducted radiomics research on PGTs using CT and MRI images (18)(19)(20), there is limited literature based on US images (21).…”