Health Management Center, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China.
Department of Radiology, The University of Hong Kong - Shenzhen Hospital, No.1, Haiyuan Road, Futian District, Shenzhen, 518000, China.
Eur Radiol. 2021 Jun;31(6):4042-4052. doi: 10.1007/s00330-020-07483-4. Epub 2020 Nov 19.
Preoperative differentiation between benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT) is important for treatment decisions. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the preoperative differentiation of BPGT from MPGT.
A total of 115 patients (80 in training set and 35 in external validation set) with BPGT (n = 60) or MPGT (n = 55) were enrolled. Radiomics features were extracted from T1-weighted and fat-saturated T2-weighted images. A radiomics signature model and a radiomics score (Rad-score) were constructed and calculated. A clinical-factors model was built based on demographics and MRI findings. A radiomics nomogram model combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The diagnostic performance of the three models was evaluated and validated using ROC curves on the training and validation datasets.
Seventeen features from MR images were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature had an AUC value of 0.952 in the training set and 0.938 in the validation set. Decision curve analysis showed that the nomogram outperformed the clinical-factors model in terms of clinical usefulness.
The above-described radiomics nomogram performed well for differentiating BPGT from MPGT, and may help in the clinical decision-making process.
• Differential diagnosis between BPGT and MPGT is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, clinical data, and MRI features facilitates differentiation of BPGT from MPGT with improved diagnostic efficacy.
术前区分腮腺良性肿瘤（BPGT）和恶性腮腺肿瘤（MPGT）对于治疗决策非常重要。本研究旨在开发和验证一种基于 MRI 的放射组学列线图，用于术前区分 BPGT 和 MPGT。
共纳入 115 例 BPGT（n=60）或 MPGT（n=55）患者（训练集 80 例，外部验证集 35 例）。从 T1 加权和脂肪饱和 T2 加权图像中提取放射组学特征。构建并计算放射组学特征模型和放射组学评分（Rad-score）。基于人口统计学和 MRI 发现构建临床因素模型。使用多变量逻辑回归分析构建结合 Rad-score 和独立临床因素的放射组学列线图模型。使用 ROC 曲线在训练集和验证集上评估和验证三个模型的诊断性能。
从 MR 图像中提取了 17 个特征来构建放射组学特征。纳入临床因素和放射组学特征的放射组学列线图在训练集和验证集的 AUC 值分别为 0.952 和 0.938。决策曲线分析表明，该列线图在临床实用性方面优于临床因素模型。
上述放射组学列线图在区分 BPGT 和 MPGT 方面表现良好，可能有助于临床决策过程。
• 常规影像学检查很难对 BPGT 和 MPGT 进行鉴别诊断。• 一种结合放射组学特征、临床数据和 MRI 特征的放射组学列线图有助于提高区分 BPGT 和 MPGT 的诊断效能。
