Background

Preoperative prognostic biomarkers to guide individualized therapy are still in demand in esophageal squamous cell cancer (ESCC). Some studies reported that radiomic analysis based on CT images has been successfully performed to predict individual survival in EC. The aim of this study was to assess whether combining radiomics features from primary tumor and regional lymph nodes predicts overall survival (OS) better than using single-region features only, and to investigate the incremental value of the dual-region radiomics signature.

Methods

In this retrospective study, three radiomics signatures were built from preoperative enhanced CT in a training cohort (n = 200) using LASSO Cox model. Associations between each signature and survival was assessed on a validation cohort (n = 107). Prediction accuracy for the three signatures was compared. By constructing a clinical nomogram and a radiomics-clinical nomogram, incremental prognostic value of the radiomics signature over clinicopathological factors in OS prediction was assessed in terms of discrimination, calibration, reclassification and clinical usefulness.

Results

The dual-region radiomic signature was an independent factor, significantly associated with OS (HR: 1.869, 95% CI: 1.347, 2.592, P = 1.82e-04), which achieved better OS (C-index: 0.611) prediction either than the single-region signature (C-index:0.594-0.604). The resulted dual-region radiomics-clinical nomogram achieved the best discriminative ability in OS prediction (C-index:0.700). Compared with the clinical nomogram, the radiomics-clinical nomogram improved the calibration and classification accuracy for OS prediction with a total net reclassification improvement (NRI) of 26.9% (P=0.008) and integrated discrimination improvement (IDI) of 6.8% (P<0.001).

Conclusion

The dual-region radiomic signature is an independent prognostic marker and outperforms single-region signature in OS for ESCC patients. Integrating the dual-region radiomics signature and clinicopathological factors improves OS prediction.

中文翻译：

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双区域影像组学特征：整合原发肿瘤和淋巴结计算机断层扫描特征可提高食管鳞状细胞癌的生存预测

背景

食管鳞状细胞癌（ESCC）仍然需要术前预后生物标志物来指导个体化治疗。一些研究报告称，基于 CT 图像的放射组学分析已成功用于预测 EC 中的个体存活率。本研究的目的是评估结合来自原发肿瘤和区域淋巴结的放射组学特征是否比仅使用单区域特征更好地预测总生存 (OS)，并研究双区域放射组学特征的增量值。

方法

在这项回顾性研究中，使用 LASSO Cox 模型从训练队列（n = 200）中的术前增强 CT 构建了三个放射组学特征。在验证队列 (n = 107) 上评估了每个特征与生存之间的关联。比较了三个签名的预测准确性。通过构建临床列线图和放射组学-临床列线图，在鉴别、校准、重新分类和临床有用性方面评估放射组学特征对 OS 预测中临床病理因素的增量预后价值。

结果

双区域放射组学特征是一个独立因素，与 OS 显着相关（HR：1.869，95% CI：1.347, 2.592，P  = 1.82e-04），这比 OS（C 指数：0.611）预测更好单区域签名（C-index：0.594-0.604）。得到的双区域放射组学-临床列线图在 OS 预测中实现了最佳判别能力 (C-index:0.700)。与临床诺模图相比，放射组学-临床诺模图提高了 OS 预测的校准和分类精度，总净重分类改进 (NRI) 为 26.9% ( P = 0.008)，综合鉴别改进 (IDI) 为 6.8% ( P < 0.001）。

结论

双区域放射组学特征是一种独立的预后标志物，在 ESCC 患者的 OS 中优于单区域特征。整合双区域放射组学特征和临床病理因素可提高 OS 预测。