Qiu Qingtao, Duan Jinghao, Deng Hongbin, Han Zhujun, Gu Jiabing, Yue Ning J, Yin Yong
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Department of Medical Imaging Ultrasonography, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Front Oncol. 2020 Aug 11;10:1398. doi: 10.3389/fonc.2020.01398. eCollection 2020.
Although patients with esophageal squamous cell carcinoma (ESCC) can achieve a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) followed by surgery, one-third of these patients with a pCR may still experience recurrence. The aim of this study is to develop and validate a predictive model to estimate recurrence-free survival (RFS) in those patients who achieved pCR. Two hundred six patients with ESCC were enrolled and divided into a training cohort ( = 146) and a validation cohort ( = 60). Radiomic features were extracted from contrast-enhanced computed tomography (CT) images of each patient. Feature reduction was then implemented in two steps, including a multiple segmentation test and least absolute shrinkage and selection operator (LASSO) Cox proportional hazards regression method. A radiomics signature was subsequently constructed and evaluated. For better prediction performance, a clinical nomogram based on clinical risk factors and a nomogram incorporating the radiomics signature and clinical risk factors was built. Finally, the prediction models were further validated by calibration and the clinical usefulness was examined in the validation cohort to determine the optimal prediction model. The radiomics signature was constructed using eight radiomic features and displayed a significant correlation with RFS. The nomogram incorporating the radiomics signature with clinical risk factors achieved optimal performance compared with the radiomics signature ( < 0.001) and clinical nomogram ( < 0.001) in both the training cohort [C-index (95% confidence interval [CI]), 0.746 (0.680-0.812) vs. 0.685 (0.620-0.750) vs. 0.614 (0.538-0.690), respectively] and validation cohort [C-index (95% CI), 0.724 (0.696-0.752) vs. 0.671 (0.624-0.718) vs. 0.629 (0.597-0.661), respectively]. The calibration curve and decision curve analysis revealed that the radiomics nomogram outperformed the other two models. A radiomics nomogram model incorporating radiomics features and clinical factors has been developed and has the improved ability to predict the postoperative recurrence risk in patients with ESCC who achieved pCR after nCRT followed by surgery.
尽管食管鳞状细胞癌（ESCC）患者在新辅助放化疗（nCRT）后接受手术可实现病理完全缓解（pCR），但这些达到pCR的患者中有三分之一仍可能复发。本研究的目的是开发并验证一种预测模型，以估计那些达到pCR的患者的无复发生存期（RFS）。招募了206例ESCC患者，并将其分为训练队列（n = 146）和验证队列（n = 60）。从每位患者的增强计算机断层扫描（CT）图像中提取放射组学特征。然后分两步进行特征降维，包括多重分割测试和最小绝对收缩与选择算子（LASSO）Cox比例风险回归方法。随后构建并评估了一个放射组学特征。为了获得更好的预测性能，建立了基于临床风险因素的临床列线图以及结合放射组学特征和临床风险因素的列线图。最后，通过校准进一步验证预测模型，并在验证队列中检验临床实用性，以确定最佳预测模型。使用八个放射组学特征构建了放射组学特征，并显示出与RFS有显著相关性。在训练队列[C指数（95%置信区间[CI]），分别为0.746（0.680 - 0.812）、0.685（0.620 - 0.750）、0.614（0.538 - 0.690）]和验证队列[C指数（95% CI），分别为0.724（0.696 - 0.752）、0.671（0.624 - 0.718）、0.629（0.597 - 0.661）]中，结合放射组学特征与临床风险因素的列线图与放射组学特征（P < 0.001）和临床列线图（P < 0.001）相比均表现出最佳性能。校准曲线和决策曲线分析表明，放射组学列线图优于其他两个模型。已开发出一种结合放射组学特征和临床因素的放射组学列线图模型，其预测nCRT后接受手术且达到pCR的ESCC患者术后复发风险的能力有所提高。
