Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China.
Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, 4365 Kangxin Road, Shanghai, 201315, China.
Cancer Imaging. 2020 Dec 11;20(1):86. doi: 10.1186/s40644-020-00364-5.
Thymic epithelial tumors (TETs) are the most common primary tumors in the anterior mediastinum, which have considerable histologic heterogeneity. This study aimed to develop and validate a nomogram based on computed tomography (CT) and texture analysis (TA) for preoperatively predicting the pathological classifications for TET patients.
Totally TET 172 patients confirmed by postoperative pathology between January 2011 to April 2019 were retrospectively analyzed and randomly divided into training (n = 120) and validation (n = 52) cohorts. Preoperative clinical factors, CT signs and texture features of each patient were analyzed, and prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and the DeLong test. The clinical application value of the models was determined via the decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and validated using the calibration plots.
Totally 87 patients with low-risk TET (LTET) (types A, AB, B1) and 85 patients with high-risk TET (HTET) (types B2, B3, C) were enrolled in this study. We separately constructed 4 prediction models for differentiating LTET from HTET using clinical, CT, texture features, and their combination. These 4 prediction models achieved AUCs of 0.66, 0.79, 0.82, 0.88 in the training cohort and 0.64, 0.82, 0.86, 0.94 in the validation cohort, respectively. The DeLong test and DCA showed that the Combined model, consisting of 2 CT signs and 2 texture parameters, held the highest predictive efficiency and clinical utility (p < 0.05). A prediction nomogram was subsequently developed using the 4 independently risk factors from the Combined model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions for differentiating TET classifications.
A prediction nomogram incorporating both the CT and texture parameters was constructed and validated in our study, which can be conveniently used for the preoperative individualized prediction of the simplified histologic subtypes in TET patients.
胸腺瘤（TETs）是前纵隔最常见的原发性肿瘤，具有显著的组织学异质性。本研究旨在建立并验证一种基于计算机断层扫描（CT）和纹理分析（TA）的列线图，用于术前预测 TET 患者的病理分类。
回顾性分析了 2011 年 1 月至 2019 年 4 月期间经术后病理证实的 172 例 TET 患者，将其随机分为训练队列（n=120）和验证队列（n=52）。分析每位患者的术前临床资料、CT 征象和纹理特征，并采用最小绝对值收缩和选择算子（LASSO）回归分析建立预测模型。采用受试者工作特征曲线（ROC）下面积（AUC）和 DeLong 检验评估和比较模型的性能。通过决策曲线分析（DCA）确定模型的临床应用价值。然后，基于预测效率和临床实用性最佳的模型建立列线图，并通过校准图进行验证。
本研究共纳入 87 例低危 TET（LTET）（A型、AB 型、B1 型）患者和 85 例高危 TET（HTET）（B2 型、B3 型、C 型）患者。我们分别使用临床资料、CT 征象、纹理特征及其组合构建了 4 个预测模型，用于区分 LTET 和 HTET。这些模型在训练队列中的 AUC 分别为 0.66、0.79、0.82、0.88，在验证队列中的 AUC 分别为 0.64、0.82、0.86、0.94。DeLong 检验和 DCA 表明，包含 2 个 CT 征象和 2 个纹理参数的联合模型具有最高的预测效率和临床实用性（p<0.05）。随后，我们基于联合模型中的 4 个独立风险因素建立了预测列线图。校准曲线表明，该列线图在区分 TET 分类方面具有较好的实际观察与预测的一致性。
本研究构建并验证了一种包含 CT 和纹理参数的预测列线图，可用于 TET 患者术前简化组织学亚型的个体化预测。
