基于增强 CT 影像组学特征术前鉴别可切除食管鳞癌肿瘤组织与肿瘤旁、肿瘤远端组织的差异
Contrast-enhanced CT radiomics features to preoperatively identify differences between tumor and proximal tumor-adjacent and tumor-distant tissues of resectable esophageal squamous cell carcinoma.
机构信息
Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1# Maoyuan South Road, Shunqing District, Nanchong, 637000, Sichuan, China.
Department of Radiology, Medical Center Hospital of Qionglai City, 172# Xinglin Road, Linqiong District, Chengdu, 611530, Sichuan, China.
出版信息
Cancer Imaging. 2024 Jan 19;24(1):11. doi: 10.1186/s40644-024-00656-0.
BACKGROUND
Esophagectomy is the main treatment for esophageal squamous cell carcinoma (ESCC), and patients with histopathologically negative margins still have a relatively higher recurrence rate. Contrast-enhanced CT (CECT) radiomics might noninvasively obtain potential information about the internal heterogeneity of ESCC and its adjacent tissues. This study aimed to develop CECT radiomics models to preoperatively identify the differences between tumor and proximal tumor-adjacent and tumor-distant tissues in ESCC to potentially reduce tumor recurrence.
METHODS
A total of 529 consecutive patients with ESCC from Centers A (n = 447) and B (n = 82) undergoing preoperative CECT were retrospectively enrolled in this study. Radiomics features of the tumor, proximal tumor-adjacent (PTA) and proximal tumor-distant (PTD) tissues were individually extracted by delineating the corresponding region of interest (ROI) on CECT and applying the 3D-Slicer radiomics module. Patients with pairwise tissues (ESCC vs. PTA, ESCC vs. PTD, and PTA vs. PTD) from Center A were randomly assigned to the training cohort (TC, n = 313) and internal validation cohort (IVC, n = 134). Univariate analysis and the least absolute shrinkage and selection operator were used to select the core radiomics features, and logistic regression was performed to develop radiomics models to differentiate individual pairwise tissues in TC, validated in IVC and the external validation cohort (EVC) from Center B. Diagnostic performance was assessed using area under the receiver operating characteristics curve (AUC) and accuracy.
RESULTS
With the chosen 20, 19 and 5 core radiomics features in TC, 3 individual radiomics models were developed, which exhibited excellent ability to differentiate the tumor from PTA tissue (AUC: 0.965; accuracy: 0.965), the tumor from PTD tissue (AUC: 0.991; accuracy: 0.958), and PTA from PTD tissue (AUC: 0.870; accuracy: 0.848), respectively. In IVC and EVC, the models also showed good performance in differentiating the tumor from PTA tissue (AUCs: 0.956 and 0.962; accuracy: 0.956 and 0.937), the tumor from PTD tissue (AUCs: 0.990 and 0.974; accuracy: 0.952 and 0.970), and PTA from PTD tissue (AUCs: 0.806 and 0.786; accuracy: 0.760 and 0.786), respectively.
CONCLUSION
CECT radiomics models could differentiate the tumor from PTA tissue, the tumor from PTD tissue, and PTA from PTD tissue in ESCC.
背景
食管切除术是治疗食管鳞状细胞癌（ESCC）的主要方法，而病理检查结果为阴性切缘的患者仍有相对较高的复发率。增强 CT（CECT）放射组学可以无创地获取 ESCC 及其毗邻组织内部异质性的潜在信息。本研究旨在建立 CECT 放射组学模型，以术前识别 ESCC 肿瘤与其毗邻的近端肿瘤组织（PTA）和肿瘤远处组织（PTD）之间的差异，从而有可能降低肿瘤复发的风险。
方法
本研究回顾性分析了来自中心 A（n=447）和中心 B（n=82）的 529 例接受术前 CECT 的 ESCC 连续患者的资料。通过勾画 CECT 相应的感兴趣区（ROI）并应用 3D-Slicer 放射组学模块，分别提取肿瘤、近端肿瘤毗邻（PTA）和近端肿瘤远处（PTD）组织的放射组学特征。来自中心 A 的两两组织（ESCC 与 PTA、ESCC 与 PTD 和 PTA 与 PTD）的患者被随机分配到训练队列（TC，n=313）和内部验证队列（IVC，n=134）。采用单变量分析和最小绝对收缩和选择算子选择核心放射组学特征，并进行逻辑回归建立用于区分 TC 中各个两两组织的放射组学模型，在 IVC 和中心 B 的外部验证队列（EVC）中进行验证。采用受试者工作特征曲线下面积（AUC）和准确性评估诊断性能。
结果
在 TC 中选择 20、19 和 5 个核心放射组学特征后，分别建立了 3 个单独的放射组学模型，这些模型在区分肿瘤与 PTA 组织（AUC：0.965；准确性：0.965）、肿瘤与 PTD 组织（AUC：0.991；准确性：0.958）和 PTA 与 PTD 组织（AUC：0.870；准确性：0.848）方面具有出色的能力。在 IVC 和 EVC 中，这些模型在区分肿瘤与 PTA 组织（AUCs：0.956 和 0.962；准确性：0.956 和 0.937）、肿瘤与 PTD 组织（AUCs：0.990 和 0.974；准确性：0.952 和 0.970）和 PTA 与 PTD 组织（AUCs：0.806 和 0.786；准确性：0.760 和 0.786）方面也表现出良好的性能。
结论
CECT 放射组学模型可区分 ESCC 中的肿瘤与 PTA 组织、肿瘤与 PTD 组织以及 PTA 与 PTD 组织。
Zotero 插件