Patients
We searched patients who underwent surgery at our hospital between March 2019 and July 2020 consecutively, and 491 patients were identified with GC. The following were inclusion criteria: (1) a pathological confirmation of GC postoperatively and (2) availability of tumor markers and abdominal contrast-enhanced CT within 2 weeks prior to surgery. The following were exclusion criteria: (1) a history of GC treatment before surgery (n=20); (2) insufficient distention of the stomach (n=40); (3) no definite information on PD-L1 (n=12); and (4) poor imaging quality due to respiratory or peristaltic motion (n=16) ; (5) hardly visible on CT images due to the small size of the lesion (n=37); and (6) incomplete information on tumor markers (n=8). The flow chart of patient selection is plotted in Fig. 1. Our Institutional Review Board has approved the current study, following the regulations outlined in the Declaration of Helsinki.
A total of 358 patients (male, 258; female, 100; median age, 60 years; age range, 29-97 years) conformed to the criteria. Patients were divided into primary cohort (n=239) and validation cohort (n=119) at a ratio of 3:1 according to the time of surgery.
CT image acquisition
CT examinations were performed on 64-row scanners (VCT, Discovery HD 750, GE Healthcare, and uCT 780, United Imaging). All patients were requested to fast for at least 6 h and drink 600-1000 mL warm water to distend stomach before examination. All patients were in the supine position, and the scan covered the upper or entire abdomen. The patients were trained to hold their breath during CT scans. Following the unenhanced scan, 1.5 mL/kg iodinated contrast agent (Omnipaque 350 mg I/mL, GE Healthcare) was injected intravenously at a flow rate of 3.0 mL/s using a high-pressure syringe (Medrad Stellant CT Injector System, Medrad Inc.). Imaging was achieved with a post-injection delay of 30-40 s and 70 s after initiation of contrast material injection, corresponding to the arterial and venous phases, respectively. CT scan parameters: tube voltage 100-120 kV, tube current 150-250 mA, slice thickness 5 mm, slice interval 5 mm, field of view 35-50 cm, matrix 512 × 512, rotation time 0.7 s, and pitch 1.0875.
Tumor marker
Six serum tumor markers, including alpha fetoprotein, carcinoembryonic antigen, carbohydrate antigen (CA) 125, CA199, CA724, and CA242, were collected within 2 weeks before surgery.
Image analysis
Axial venous CT images of all patients were downloaded through a picture archiving and communication system and uploaded into Imaging Biomarker Explorer software. A polygonal region of interest (ROI) was manually drawn along the margin of the tumor on maximal transverse slice as illustrated in Fig. 2, carefully avoiding the normal gastric wall tissue and gastric cavity contents. ROI segmentations were performed manually by reader 1 (X.X. with 8 years of experience in abdominal imaging) who was unaware of clinicopathological information of the patients. The general location of the tumors (cardia, body, and antrum) was informed. To evaluate the interobserver reproducibility, 35 cases of CT images were randomly selected for the second ROI segmentation and feature extraction as above by reader 2 (X.X. with 8 years’ experience in abdominal imaging). In total, 744 radiomic features were generated automatically from the ROIs. The detailed explanations and formulas of radiomic features are displayed in supplementary material.
Development and performance of signatures
As depicted in Fig. 2, first, the intraclass correlation coefficient (ICC) was calculated to evaluate the interobserver variability of radiomic features extraction using “irr” package (vers. 0.84). Radiomic features with the ICC values >0.8 were regarded as highly reproducible features and initially selected. Second, the Mann-Whitney U test was used to select significantly different radiomic features between different PD-L1 status groups. The least absolute shrinkage and selection operator (LASSO) was used for the dimension reduction of radiomic features. Then, the optimal variables were put into our in-house software programmed with the R software package (version 3.5.2: http:// www.Rproject.org), and the Support Vector Machine (SVM) and Random Forest (RF) algorithms were applied to generate signatures in the primary cohort. The ratio of the training and testing sets was 4:1. In the training phase, a popular data-preprocessing method in machine learning-Synthetic Minority Oversampling Technique was applied to handle the class imbalance problem. The models were evaluated by repeated stratified (K=5) cross-validation. The models developed was also applied to the validation cohort. In addition, to evaluate the clinical usefulness of the developed model, a decision curve analysis (DCA) was plotted by demonstrating the net benefits graphically for a range of threshold probabilities in the validation cohort.
Detection of PD-L1 Expression Status
The PD-L1 expression status were measured through immunohistochemistry testing for paraffin-embedded tumor tissues in our study. The markers cytokeratin and the lymphocyte common antigen were used to differentiate tumor cells. The positivity for PD-L1 was assessed by one pathologist using SP142 abcam staining. The expression for PD-L1 was scored according to tumor cell / tumor infiltrating lymphocyte proportion, which was defined as the percentage of tumor cells / tumor infiltrating lymphocytes with complete or partial membranous staining at any intensity.
Statistical analysis
The normality distribution of radiomic features was evaluated by the Shapiro-Wilk test. Based on the normality test results, the difference of them was analyzed by the Mann-Whitney U test. Interobserver agreement of radiomic features was estimated with ICC (0.000-0.200: poor; 0.201-0.400: fair; 0.401-0.600: moderate; 0.601-0.800: good; 0.801-1.000: excellent). Receiver operating characteristic (ROC) analysis and the area under the ROC curve (AUC) were performed to evaluate the diagnostic performance of signatures. All those statistical analyses were performed with SPSS (version 22.0 for Microsoft Windows x64, SPSS), MedCalc Statistical Software (version 11.4.2.0 MedCalc Software bvba; http://www.medcalc.org; 2011), and R software package (version 3.5.2: http:// www.Rproject.org). A two-tailed p value <0.05 was considered statistically significant.