Patients with pathologically confirmed ESCC and a Karnofsky Performance status ≥70 who were receiving three-dimensional conformal or intensity-modulated radiotherapy were included in this study. Patients were excluded if they met the following criteria: 1) distant metastasis; 2) low-dose palliative radiotherapy; 3) preoperative or postoperative adjuvant radiotherapy; 4) incomplete clinicopathological information; 5) esophageal fistula and esophageal stent implantation; 6) image artifacts or tumour volumes are too small to be recognised on CT images, resulting in poor visualisation quality; 7) previous malignant tumour history.
218 patients who received three-dimensional conformal or intensity-modulated radiotherapy at the Fourth Hospital of Hebei Medical University from July 2016 to December 2017 were collected. The median age was 67 years (37-84 years). All patients received electronic gastroscopy, esophageal barium meal contrast, chest enhanced CT scan and abdominal ultrasound or CT examination before treatment, according to the eighth edition of the American Joint Committee on Cancer staging criteria (13).
Gross tumour volume included the primary esophageal tumour and regional lymph nodes. The criteria for determining esophageal lesions on CT images were esophageal wall thickness >5 mm or non-airless oesophagus diameter >10 mm, localised or whole esophageal wall thickening, and/or local lumen stenosis. The clinical target volume (CTV) was obtained by expanding the GTV to a margin to 2.0-3.0 cm at the long axis and 0.5 cm at the lateral axis. The planning target volume (PTV) was reached by CTV plus a margin of 0.5 cm. The prescription dose for the whole group was 50.0-66.0 Gy, the median dose was 60.0 Gy, and a single dose was 1.8-2.2 Gy.
90 patients received 1-2 cycles of concurrent chemotherapy, with the main regimens of FP (cisplatin, 12.5 mg/m2×5 days or 25 mg/m2×3 days; 5-fluorouracil, 450 to 500 mg/m2×5 days) or TP (paclitaxel, 135 mg/m2, d1,8 days; cisplatin, 25 mg/m2, d2, 3, 4 days, 28 days as a cycle, then 1, 5 weeks of administration).
CT image acquisition
CT images were collected before and within one month after radiotherapy. All patients underwent standard chest contrast-enhanced CT scanning with a CT scanner (SOMATOM Definition Flash CT, SOMATOM Sensation Open CT, Forchheim, Germany). Scan parameters were as follows: tube voltage, 120 kV; tube current, 110 mA; scanning matrix, 512 x 512; conventional scanning layer thickness, 5.0 mm; reconstruction layer thickness, 1.0 mm; mediastinal window width, 350 HU; window position, 40 HU; lung window width, 1200 HU; and window position, -600 HU. In this study, enhanced CT images were used for tumour delineation and feature extraction.
CT image segmentation
Arterial-phase CT images of 218 patients which were retrieved from PACS were imported into the 3D Slicer software (version 4.8.1, http://www.slicer.org), The tumours were manually segmented slice by slice using the software. and an attending physician with more than 5 years of clinical experience independently outlined the region of interest (ROI) of the esophageal primary tumours. Intraluminal air and contrast agent, fatty tissues,tumour necrosis surrounding the lesion, and blood vessels near the gross tumour were removed from the ROI, defined as an area with attenuation values below -50 HU and over 300 HU. The attending physician sketched all tumour ROIs, and the associate chief physician randomly selected 40 cases of sketched tumour ROIs for a consistency test.
Radiomic feature extraction and selection
Radiomic features of the segmented 3D images were extracted using the Python programme package Pyradiomics 1.2.0.(Amsterdam Netherlands). A total of seven categories of imaging features were collected in this study. This included 18 first-order, 14 shape-based histogram, 24 grey level co-occurrence matrix, 16 grey level size zone matrix, 16 grey level run length matrix, 14 grayscale dependence matrix, and 5 neighbourhood grey-tone difference matrix features (14).
The intergroup correlation coefficient (ICC) was used to analyse the consistency of the radiomic features extracted from the ROI of the tumours in the training group. The features with good reproducibility (ICC>0.75) were selected. First, Spearman correlation analysis was performed for any two feature columns. R>0.9 indicated that the two features were highly correlated, and the features with large correlation coefficients with LRFS were retained. Second, the most useful predictive features were selected using the least absolute shrinkage and selection operator (LASSO) Cox regression model, which was applied to reduce high-dimensional data. Ten-fold cross-validation was used in the parameter tuning phase of the LASSO algorithm to extract the effective and predictive features.
Construction of the radiological label and radiomics nomogram
After the imaging features were screened by the least absolute shrinkage and selection operator (LASSO) regression, Logistic regression was used to calculate the regression coefficient (β). The weighted linear formula was as follows:
For a better prediction effect, multivariate logistic model was to build a radiomics nomogram. The nomogram was constructed by combining the radiomic signature with the conventional clinical parameters to determine the optimal predictive performance.
Validation of the radiological label and radiomics nomogram
The efficacy of the radiological label in predicting CR was determined using the receiver operating characteristic curve, area under the curve (AUC) was calculated, and the sensitivity, specificity, accuracy, positive predictive value and negative predictive value were calculated. The consistency of the two evaluation methods was defined according to the value of kappa coefficient. Decision curve analysis was then used to determine the clinical value of the constructed prediction model. In addition, we used the calibration curve to assess the predictive accuracy and the agreement between the actual and predicted CR.
According to the criteria for evaluating the efficacy of radiotherapy for primary lesions of esophageal cancer proposed by our previous studies (15): ① complete remission: esophagography indicates complete remission (CR), that is, the tumor disappears completely, the edge of esophageal slice is smooth, barium passes smoothly, but the esophageal wall can be slightly stiff, the lumen has no stenosis or slight stenosis, and the mucosa basically returns to normal or thickened, Chest CT showed that the maximum wall thickness of esophagus after treatment was ≤ 1.2cm; ② Partial remission (PR): esophagography indicates partial remission, that is, most of the lesions disappear without obvious distortion or angulation, no extraluminal ulcer, barium passes smoothly, but the edge is not smooth, with small filling defect and / or small niche, or although the edge is smooth, but the lumen is significantly narrow. Chest CT shows that the maximum wall thickness of the esophagus is ≤ 1.2cm, esophagography indicates CR, The edge of esophageal film is smooth, barium passes smoothly, but the tube wall can be slightly stiff, the lumen is not narrow or slightly narrow, and the mucosa basically returns to normal or thickened. Chest CT shows that the maximum tube wall thickness of esophagus is > 1.2cm; ③ No remission (NR): esophagography indicates no remission, that is, at the end of radiotherapy, there are residual lesions or no obvious improvement of lesions, but there are still obvious filling defects, niches or stenosis aggravation. Chest CT shows that the maximum tube wall thickness of esophagus is in any situation. The follow-up efficacy evaluation of this study refers to this standard.
Statistical analysis was performed using R software (version 3.4.4) and SPSS version 25.0 (IBM). Comparisons of patient characteristics were performed using X2 test or two-sample t-test. Univariate analysis used X2 test to compare the CR of different groups of esophageal cancer patients. Logistic regression model was used to screen the independent influencing factors of CR. P<0.05 was statistically significant.