The protocol for this retrospective study was obtained from the local ethics and institutional review board. Approval and the need for informed consent had been waived. This study included patients with EC who underwent definitive CCRT at AAA between September 2009 and August 2015. The inclusion criteria were: (1) pathological diagnosis of OSCC; (2) primary tumour located in the cervical, upper thoracic, or middle thoracic oesophagus; and (3) contrast-enhanced CT scan findings, which were used in treatment planning before definitive CCRT. The exclusion criteria were: (1) patients who only received radiotherapy or chemotherapy; (2) prior surgery or administration of chest radiotherapy or chemotherapy. As shown in Figure 1, the final study population consisted of 154 patients. All patients received intensity modulated radiation therapy (IMRT) combined with chemotherapy. Of these, 78 patients with OSCC were from a phase II prospective clinical study, using simultaneous modulated accelerated radiotherapy (SMART) combined with chemotherapy . The 154 patients were randomly assigned into a training cohort (n=99) and validation cohort (n=55).
All patients underwent simulated CT scans for treatment planning. Seventy-eight patients underwent SMART, followed by radiation therapy with a prescribed dose of 66 Gy/30F, 5 days/week. Other patients underwent radiation therapy with a prescribed dose of 64/32F, 5 days/week. Most patients (90.9%) received concurrent chemotherapy based on the cisplatin and 5-fluorouracil (PF) regimen. The intensity of concurrent chemotherapy was relatively reduced among patients with advanced age or poor performance status. Data regarding clinical characteristics of patients were collected in both cohorts, including age, sex, clinical stage, and tumour location. Dose-volume information for the primary tumour was collected from the radiotherapy planning system. Further details are shown in Table 1.
Contrast-enhanced CT image obtainment
The CT scans of all patients were acquired (Philips Brilliance CT Big Bore Oncology Configuration, Cleveland, OH, USA; voxel size: 1.0 × 1.0 × 3.0 mm3 for 79 patients and 1.0 × 1.0 × 5.0 mm3 for 72 patients; convolution kernel: Philips Healthcare’s B), using a scanning voltage of 120 kVp with a slice thickness of 3–5 mm after an intravenous injection of 75 ml of 300 mg/mL iodinated contrast agent at a rate of 1.8–2 mL/sec with a pump injector (Medrad Stellant; Bayer, Beijing, China). The CT images were transmitted to the radiation therapy planning system (Eclipse Planning System version 10.0) via the DICOM 3.0 port.
Region of interest (ROI) delineation and IBMs extracted
Pre-treatment contrast-enhanced CT scan images of patients were exported for analysis. The primary tumour was delineated by experienced radiation oncologists on the mediastinal window of the planning CT scan. IBMs were extracted by internal programming software using MATLAB R2016a (Mathworks, Natick, USA) and its toolbox. From the contrast-enhanced CT images of each patient, 96 IBMs were extracted, including the following types: (1) 24 CT intensity IBMs, describing the distribution of voxel parameter values in the volume of interest, such as the min, max and skewness of the primary tumour intensity; (2) 20 geometric IBMs that calculated the size and shape of the volume of interest, such as sphericity, volume, surface and long axis length; and (3) 52 texture IBMs, that described the difference in voxel density distribution of the three-dimensional contoured structure and consisted of four different matrices: grey level co-occurrence (GLCM) , gray level run-length (GLRLM) , neighbourhood grey-tone difference (NGTDM) , and grey level size-zone (GLSZM) matrices . More details on the algorithms for IBM extraction and application have been discussed in previous studies [14, 27].
Pre-selection Method and IBM score building
Because high correlations between most of the IBM variables were expected, in order to reduce the statistical probability of multi-collinearity, three rules were implemented to pre-select IBM variables for further analysis. First, IBM variables were assessed in the univariable analysis; variables with a p-value less than 0.25 were used for the next analysis. Second, from highly correlated pairs of IBMs (i.e. the Pearson correlation coefficient r≥0.8) variables with the higher p-value in the Cox univariable analysis were omitted. Third, we performed the least absolute shrinkage and selection operator (LASSO) for the Cox regression model to select the most useful prognostic IBM variables from the potential predictors. . A multiple-IBM-based score (defined as the IBM score) was calculated for each patient to reflect the risk of mortality or tumour progression and variance inflation factor (VIF) used to evaluate the collinearity among these final IBMs.
IBM score performance and validation
As patients with OSCC were assigned into two cohorts, the performances of the IBM score were evaluated by the concordance indices (C-indices), respectively. The potential correlation of the IBM score with the OS and PFS for both the training and validation cohorts was assessed by using Kaplan–Meier survival curve analyses. Time-dependent receiver operating characteristic (ROC) curves were plotted for both the training and validation cohorts in term of OS and PFS. 95% confidence intervals were used as the confidence level on the ROC curves in this study. The optimal cut-off values of the ROC curves were determined using the Youden Indices (YIs) in the training cohort, patients into high- and low IBM score subgroups, the thresholds of which were stratified by the maximum YIs. Then, the same cut-off values were applied to the validation cohort. Multivariable Cox proportional hazards analysis was used to assess the IBM score as an independent predictor by integrating clinical risk factors. In the training cohort, the nomograms based on the IBM score were developed to assess individual patient-level probability estimates for the median survival time and 1-year, 3-year, and 5-year OS or PFS rates according to each patient’s unique combination of baseline characteristics. To estimate the clinical utility of the IBMs nomograms, decision curve analysis (DCA) was used to quantify the net benefits at different threshold probabilities in both cohorts.
The survival estimates mainly assessed in this study were OS and PFS. OS was defined as the time from the beginning of radiation therapy to death due to any cause or the last day of clinical follow-up, while PFS was defined as the time from the beginning of radiation therapy to first relapse at any site or death from any cause, whichever occurred first, or the last day of clinical follow-up.
The clinical features of the patients in the two cohorts were compared using the independent t-test or chi-squared test, with a statistical significance level of 0.05 for 2-tailed test. All statistical analyses were performed using R version 3.6.0 (The R Foundation for Statistical Computing, Vienna, Austria) and SPSS version 23.0 (IBM Corp, Armonk, NY, USA). The LASSO algorithm was implemented using the glmnet package in the R environment . The ROC and Kaplan-Meier curves were plotted using the pROC and survminer packages, respectively, in the R environment. Nomograms were constructed using the rms and survival packages in the R environment. The DCA curves were created using the rmda package in the R environment.