AUTHOR=Qiu Qingtao , Duan Jinghao , Deng Hongbin , Han Zhujun , Gu Jiabing , Yue Ning J. , Yin Yong 

TITLE=Development and Validation of a Radiomics Nomogram Model for Predicting Postoperative Recurrence in Patients With Esophageal Squamous Cell Cancer Who Achieved pCR After Neoadjuvant Chemoradiotherapy Followed by Surgery

JOURNAL=Frontiers in Oncology

VOLUME=10

YEAR=2020

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.01398

DOI=10.3389/fonc.2020.01398

ISSN=2234-943X

ABSTRACT=<p><bold>Background and purpose:</bold> 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.</p><p><bold>Materials and methods:</bold> Two hundred six patients with ESCC were enrolled and divided into a training cohort (<italic>n</italic> = 146) and a validation cohort (<italic>n</italic> = 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.</p><p><bold>Results:</bold> 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 (<italic>P</italic> &lt; 0.001) and clinical nomogram (<italic>P</italic> &lt; 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.</p><p><bold>Conclusions:</bold> 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.</p>