Health Management Center, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China.
Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China.
Eur Radiol. 2021 May;31(5):2886-2895. doi: 10.1007/s00330-020-07421-4. Epub 2020 Oct 30.
Preoperative differentiation between benign lymphoepithelial lesion (BLEL) and mucosa-associated lymphoid tissue lymphoma (MALToma) in the parotid gland is important for treatment decisions. The purpose of this study was to develop and validate a CT-based radiomics nomogram combining radiomics signature and clinical factors for the preoperative differentiation of BLEL from MALToma in the parotid gland.
A total of 101 patients with BLEL (n = 46) or MALToma (n = 55) were divided into a training set (n = 70) and validation set (n = 31). Radiomics features were extracted from non-contrast CT images, a radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factor model. A radiomics nomogram combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The performance levels of the nomogram, radiomics signature, and clinical model were evaluated and validated on the training and validation datasets, and then compared among the three models.
Seven features were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature showed favorable predictive value for differentiating parotid BLEL from MALToma, with AUCs of 0.983 and 0.950 for the training set and validation set, respectively. Decision curve analysis showed that the nomogram outperformed the clinical factor model in terms of clinical usefulness.
The CT-based radiomics nomogram incorporating the Rad-score and clinical factors showed favorable predictive efficacy for differentiating BLEL from MALToma in the parotid gland, and may help in the clinical decision-making process.
• Differential diagnosis between BLEL and MALToma in parotid gland is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of BLEL from MALToma with improved diagnostic efficacy.
术前区分腮腺良性淋巴上皮病变（BLEL）和黏膜相关淋巴组织淋巴瘤（MALToma）对于治疗决策非常重要。本研究旨在开发和验证一种基于 CT 的放射组学列线图，结合放射组学特征和临床因素，用于术前区分腮腺中的 BLEL 和 MALToma。
共纳入 101 例 BLEL（n=46）或 MALToma（n=55）患者，分为训练集（n=70）和验证集（n=31）。从非增强 CT 图像中提取放射组学特征，构建放射组学特征，计算放射组学评分（Rad-score）。评估并记录患者的临床资料和 CT 表现，以建立临床因素模型。使用多变量逻辑回归分析构建放射组学列线图，将 Rad-score 和独立的临床因素结合起来。在训练集和验证集上评估和验证列线图、放射组学特征和临床模型的性能水平，然后在三个模型之间进行比较。
使用 7 个特征构建了放射组学特征。纳入临床因素和放射组学特征的放射组学列线图对区分腮腺 BLEL 和 MALToma 具有良好的预测价值，训练集和验证集的 AUC 分别为 0.983 和 0.950。决策曲线分析显示，在临床实用性方面，该列线图优于临床因素模型。
基于 CT 的放射组学列线图，将 Rad-score 和临床因素结合起来，对区分腮腺中的 BLEL 和 MALToma 具有良好的预测效果，可能有助于临床决策过程。
• 通过常规影像学方法很难对腮腺中的 BLEL 和 MALToma 进行鉴别诊断。• 一种将放射组学特征、人口统计学和 CT 表现相结合的放射组学列线图有助于提高诊断效能，从而区分 BLEL 和 MALToma。
