Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Department of Radiology, Kunshan Third People's Hospital, Kunshan, Jiangsu, China.
Med Phys. 2023 Feb;50(2):947-957. doi: 10.1002/mp.16042. Epub 2022 Nov 12.
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.
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.
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.
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.
准确的术前诊断腮腺肿瘤对于制定最佳个体化手术计划至关重要。本研究旨在探讨基于增强 CT 图像的放射组学列线图在区分两种最常见的良性腮腺肿瘤中的诊断性能。
本研究纳入了 110 例经病理证实的腮腺肿瘤患者，其中 76 例为多形性腺瘤（PA），34 例为腺淋巴瘤（AL）。从静脉期增强 CT 图像中提取放射组学特征。建立放射组学模型并计算放射组学评分（Rad-score）。评估临床因素，包括临床数据和 CT 特征，以建立临床因素模型。最后，构建一个包含 Rad-score 和独立临床因素的列线图。生成受试者工作特征（ROC）曲线，并计算每个模型在训练和验证队列中的曲线下面积（AUC）以量化每个模型的判别性能。进行决策曲线分析（DCA）以评估每个模型的临床实用性。
放射组学模型在训练队列中具有良好的判别能力[AUC，0.89；95%置信区间（CI），0.80-0.98]和验证队列（AUC，0.89；95%CI，0.77-1.00）。放射组学列线图在训练队列（AUC，0.98；95%CI，0.96-1.00）和验证队列（AUC，0.95；95%CI，0.88-1.00）中均具有出色的判别能力，并且在训练队列中比临床因素模型（AUC，0.93；95%CI，0.88-0.99）具有更好的判别效果（p<0.05）。DCA 表明，结合 Rad-score 和临床因素的 CT 基放射组学列线图提供了比临床因素模型和放射组学模型更优的临床实用性。
基于 CT 的放射组学列线图结合 Rad-score 和临床因素对鉴别腮腺 PA 与 AL 具有出色的预测能力，这可能有助于放射科医生和临床医生进行准确的鉴别诊断并制定适当的治疗策略。
