Abstract:
Objective To assess the clinical predictive value of a model based on enhanced CT image features to enable the local control of post-radiotherapy primary lesions in patients with esophageal squamous cell carcinoma.
Methods Data from 218 patients with esophageal squamous cell carcinoma, diagnosed by pathology and receiving radical radiotherapy and chemotherapy, were collected between July 2016 and December 2017 at The Fourth Hospital of Hebei Medical University. Patients were randomly assigned into training (n=153) and validation groups (n=65). Imaging features were extracted after delineation of the region of interest (ROI) from enhanced CT images captured before and after radiotherapy. The radiomics features of the training group in combination with the clinical features were used to construct a radiomics nomogram to predict the local control of primary lesions after radiotherapy. Receiver operating characteristic (ROC) curves, C-index curves, calibration curves, and decision curves were used to evaluate the performances of the different models.
Results Six significant radiomic features were extracted from the training group to generate radiological labels for the prediction of local control of post-radiotherapy primary lesions in esophageal cancer. In the training and validation groups, the areas under the ROC curves were 0.758 and 0.728, respectively, and their C-indices were 0.709 and 0.695, respectively. Patients were classified as high- and low-risk based on a radiological label score, whose cut-off was set at -0.22. The 1-, 3-, and 5-year survival rates of the patients classified a slow-risk were higher than those of the patients classified as high-risk (P < 0.05). Additionally, a radiomics nomogram was generated to predict the survival of patients with esophageal cancer without any recurrence of the primary lesion after radiotherapy. The area under the curve of the nomogram was 0.775 in the training group and 0.740 in the validation group, while the corresponding C-indices were 0.722 and 0.707, respectively. A radiomics nomogram model score of 0.55 was used as a cut-off to classify patients into high-and low-risk groups. Patients in the low-risk group had higher 1-, 3-, and 5-year survival rates coupled with the absence of primary lesion recurrence compared to those in the high-risk group (P< 0.05).
Conclusions A radiological labeling method and radiomics nomogram were successfully constructed to predict survival without primary lesion recurrence among patients with esophageal cancer after radiotherapy. Both tools were found to have good clinical predictive value.