Development and validation of CT‐based radiomics nomogram for the classification of benign parotid gland tumors

Abstract

Purpose Accurate preoperative diagnosis of parotid tumor is essential for the formulation of optimal individualized surgical plans. The study aims to investigate the diagnostic performance of radiomics nomogram based on contrast‐enhanced computed tomography (CT) images in the differentiation of the two most common benign parotid gland tumors. Methods One hundred and ten patients with parotid gland tumors including 76 with pleomorphic adenoma (PA) and 34 with adenolymphoma (AL) confirmed by histopathology were included in this study. Radiomics features were extracted from contrast‐enhanced CT images of venous phase. A radiomics model was established and a radiomics score (Rad‐score) was calculated. Clinical factors including clinical data and CT features were assessed to build a clinical factor model. Finally, a nomogram incorporating the Rad‐score and independent clinical factors was constructed. Receiver operator characteristics (ROC) curve was generated and the area under the ROC curve (AUC) was calculated to quantify the discriminative performance of each model on both the training and validation cohorts. Decision curve analysis (DCA) was conducted to evaluate the clinical usefulness of each model. Results The radiomics model showed good discrimination in the training cohort [AUC, 0.89; 95% confidence interval (CI), 0.80–0.98] and validation cohort (AUC, 0.89; 95% CI, 0.77–1.00). The radiomics nomogram showed excellent discrimination in the training cohort (AUC, 0.98; 95% CI, 0.96–1.00) and validation cohort (AUC, 0.95; 95% CI, 0.88–1.00) and displayed better discrimination efficacy compared with the clinical factor model (AUC, 0.93; 95% CI, 0.88–0.99) in the training cohort (p < 0.05). The DCA demonstrated that the combined radiomics nomogram provided superior clinical usefulness than clinical factor model and radiomics model. Conclusions The CT‐based radiomics nomogram combining Rad‐score and clinical factors exhibits excellent predictive capability for differentiating parotid PA from AL, which might hold promise in assisting radiologists and clinicians in the exact differential diagnosis and formulation of appropriate treatment strategy.

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

“…Previous studies have shown that radiomic research using CT and MR images performs well in differentiating PA and AL. Zheng ( 25 ) gathered 76 instances of PA and 34 cases of AL and built a model based on CT images with an AUC of 0.89 and an accuracy of 83.3%. Song ( 26 ) built a T1-2WI model based on MR images with an AUC of 0.90 and an accuracy of 86% after collecting 140 instances of PA and 112 cases of AL.…”

“…Therefore, this prognostic model has certain clinical applicability. Zheng ( 25 ) developed and validated a novel prognostic model using a nomogram-based approach to differentiate between PA and AL. This model incorporated the CT Rad-score and independent clinical factors.…”

CT study of radiomics features predicting Ki-67 expression level in peripheral lung cancer

“…By analyzing images using radiomics method, molecular level information contained in images could be obtained. It could assist clinicians to make independent treatment plans for patients [19]. Chest enhanced CT images were analyzed by radiomics, and different image features had signi cant effect in predicting the expression level of molecular biological marker Ki-67, and could guide clinical treatment and prognosis.…”

Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach

“…Cerebral alveolar echinococcosis is a rare parasitic disease, but it is still a severe public health issue in many parts of the world. We believe that radiomics based machine learning is a novel tool to investigate this disease, which have been proved as a powerful approach in other elds [33][34][35][36] .…”