For patients with ESCC who cannot be operated or refused surgery, CCRT was the main treatment. Previous studies have shown that patients who achieved CR after CCRT had a better prognosis than those didn’t achieve CR[7]. A predictive model of CR status would help us to classify patients' radio-sensitivity, and formulate more personalized treatment plans before treatment. In the present study, we developed and validated a nomogram model combined clinical staging and radiomics signature Rad-score for predicting CR status of ESCC patients treated with CCRT. The AUCs of the nomogram were 0.844 and 0.807 in the training set and the validation set, respectively, indicating a high predictive ability.
Radiomics is an emerging image analysis method, which can convert CT, MRI and PET-CT images into high-throughput radiomics feature data[12]. Investigators can extract radiomics features data from regions of interest by using the python software, including intensity, texture, shape, wavelet features, and so on. Then a radiomics signature was developed by linear or nonlinear machine learning methods to achieve a comprehensive quantitative description of the tumor for diagnosis, efficacy prediction and survival prognosis analysis[18]. Due to the advantages of radiomics feature signature over traditional imaging techniques, the application of radiomics to predict treatment response and prognosis has been launched widely. For patients with esophageal cancer who undergo concurrent chemoradiation, the treatment response assessment and prognosis prediction rely on medical image evaluation. However, there were many uncertainties, which had attracted attention of many investigators.
To date, several studies have reported the application of radiomics features in predicting the treatment response and prognosis for patients with esophageal cancer[10, 13, 14, 19–22]. Hou et al[22] extracted 214 radiomics features from the pretreatment enhanced CT images of 49 patients with esophageal cancer. A model based on 5 radiomics features was developed with an AUC of 0.686–0.727 and the classification accuracy is 0.891 and 0.972, respectively. Li et al. analyzed the changes of CT radiomics features during the radiotherapy of esophageal cancer patients, and found that the tumor volume and CT value varied with the irradiation dose. Therefore, the CT based radiomics features can be used to early predict the response of chemoradiotherapy in patients with esophageal cancer. Jin et al[14] combined CT radiomics features and dosimetric parameters to establish a model for predicting the response of esophageal cancer to chemoradiotherapy. However, the sample size included in previous studies was small. In our present study, 226 ESCC patients treated with CCRT were included for investigation. As a result, 7 CT radiomics features and clinical staging were selected to develop a nomogram model for predicting CR status of patients after CCRT. This model can provide an economic and non-invasive method for clinicians to predict the treatment response of ESCC patients treated with CCRT.
As we all know, tumor staging is the most important prognostic predictor for patients with malignant tumors, which is the basis for clinicians to make a treatment strategy. Previous study have found that patients with AJCC stage II were more likely to achieve CR after chemo-radiotherapy[23]. Other studies have shown that patients with more advanced T staging before treatment have a lower probability of achieving CR after CCRT[24]. In the T staging of esophageal cancer AJCC staging, the staging criteria are determined based on the depth of esophageal tumor infiltrating the esophagus wall and the relationship with the surrounding tissues and organs, which only represent the depth of the invasion of the esophageal cancer lesion in the horizontal axis direction but not the infiltration of the esophageal cancer lesion in the direction of the longitudinal axis of the esophagus[25]. Therefore, the primary tumor cannot be comprehensively evaluated due to deficiency of some prognostic information. In addition, the clinical staging of esophageal cancer relies on imaging examination, which was inevitably inconsistent with pathological staging. Radiomics analysis can extract the three-dimensional image information of tumors and provide information that comprehensively represents the tumor, consequently improving the accuracy of clinical staging. Our results showed that there was a significant correlation between the pretreatment clinical stage and treatment response of CR, with P < 0.001 in the training set and P = 0.016 in the validation set. Multivariate analysis showed that pretreatment clinical staging and radiomics signature Rad-score were independent predictors for CR status. Then a nomogram was developed and validated with AUCs of 0.844 in training set and 0.807 in validation set, respectively. Furthermore, we compared the performance of nomogram model and clinical staging and found that the prediction ability of the nomogram model was significantly better than clinical staging both in the training set and the validation set. These results suggested that the nomogram model we developed was superior to clinical staging in predicting chemoradiotherapy response.
Compared with previous studies, this study has its own advantages. First, the CT images collected in this study were the radiotherapy simulation positioning CT image, which were from the same CT machine with the unified scanning parameters to avoid the impact of different machines and different scanning parameters on radiomics features. Second, all esophageal cancer patients included in this study were pathologically diagnosed as esophageal squamous cell carcinoma and the sample size was the largest of its kind so far. Third, all patients received concurrent chemo-radiotherapy with a definitive radiation dose of 50-72Gy.
Of course, this study also has several limitations. First of all, this study is a retrospective and single center investigation. The conclusion of the study still needs to be verified externally with larger sample size of patients. If possible, a prospective investigation will be more illustrative. Secondly, this study included only a few clinical features. Third, the treatment response evaluation was performed using CT images and barium esophagogram but not pathologically evaluation.