Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, China.
Cancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University, Nantong, China.
Br J Radiol. 2022 Feb 1;95(1130):20210918. doi: 10.1259/bjr.20210918. Epub 2021 Dec 15.
The present study explored the value of preoperative CT radiomics in predicting lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC).
A retrospective analysis of 294 pathologically confirmed ESCC patients undergoing surgical resection and their preoperative chest-enhanced CT arterial images were used to delineate the target area of the lesion. All patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Radiomics features were extracted from single-slice, three-slice, and full-volume regions of interest (ROIs). The least absolute shrinkage and selection operator (LASSO) regression method was applied to select valuable radiomics features. Radiomics models were constructed using logistic regression method and were validated using leave group out cross-validation (LGOCV) method. The performance of the three models was evaluated using the receiver characteristic curve (ROC) and decision curve analysis (DCA).
A total of 1218 radiomics features were separately extracted from single-slice ROIs, three-slice ROIs, and full-volume ROIs, and 16, 13 and 18 features, respectively, were retained after optimization and screening to construct a radiomics prediction model. The results showed that the AUC of the full-volume model was higher than that of the single-slice and three-slice models. According to LGOCV, the full-volume model showed the highest mean AUC for the training cohort and the validation cohort.
The full-volume radiomics model has the best predictive performance and thus can be used as an auxiliary method for clinical treatment decision making.
LVI is considered to be an important initial step for tumor dissemination. CT radiomics features correlate with LVI in ESCC and can be used as potential biomarkers for predicting LVI in ESCC.
本研究探讨了术前 CT 放射组学在预测食管鳞状细胞癌（ESCC）中淋巴管血管侵犯（LVI）的价值。
回顾性分析了 294 例经手术切除并经病理证实的 ESCC 患者，对其术前胸部增强 CT 动脉图像进行描绘，以确定病变的目标区域。所有患者均按 7:3 的比例随机分为训练队列和验证队列。从单层面、三层面和全层面感兴趣区（ROI）中提取放射组学特征。应用最小绝对值收缩和选择算子（LASSO）回归方法选择有价值的放射组学特征。应用逻辑回归方法构建放射组学模型，并采用留群外验证（LGOCV）方法进行验证。使用受试者工作特征曲线（ROC）和决策曲线分析（DCA）评估三种模型的性能。
分别从单层面 ROI、三层面 ROI 和全层面 ROI 中提取了 1218 个放射组学特征，经优化筛选后分别保留了 16、13 和 18 个特征，构建了放射组学预测模型。结果表明，全层面模型的 AUC 高于单层面和三层面模型。根据 LGOCV，全层面模型在训练队列和验证队列中的平均 AUC 最高。
全层面放射组学模型具有最佳的预测性能，因此可以作为临床治疗决策的辅助方法。
LVI 被认为是肿瘤扩散的重要初始步骤。CT 放射组学特征与 ESCC 中的 LVI 相关，可作为预测 ESCC 中 LVI 的潜在生物标志物。
