The Institutional Review Board of our hospital approved this retrospective study and the requirement for informed consent was waived.
The inclusion criteria for the primary and validation cohorts were as follows: (a) patients who underwent surgery with curative intent for T1-2 GC and with pathological results; (b) LN dissection performed; (c) excisional LN with detailed grouping and pathological diagnosis; (d) standard contrast-enhanced CT performed less than 10 days before surgical resection. The exclusion criteria were:(a) hypotensive drug taboo (such as glaucoma, prostatic hypertrophy, etc.); (b) preoperative therapy (radiotherapy, chemotherapy, or chemoradiotherapy); (c) concurrent with other tumors or diseases; (d) patients with variation of the left gastric artery; (e) invisible lesions on CT images.
A total of 159 patients between March 2012 and November 2017 were enrolled in this study (113 males, 46 females; average age, 61.78 ± 10.47 years). All the patients were randomly divided into two independent cohorts: a primary cohort, containing 80 patients (53 males, 27 females; average age, 61.78 ± 11.11 years), and a validation cohort, containing 79 patients (60 males, 19 females; average age, 61.78 ± 9.77 years).
Clinical data, including gender, age, carcinoembryonic antigen (CEA: 0–5 ng/ml), carbohydrate antigen 19 − 9 (CA19-9: 0-40u/ml), cancer antigen 125 (CA125: 0-35u/ml), pathologic grade (see detailed description in Supplementary Table S1), CT-reported LN metastasis status from radiologist, and tumor infiltration depth, were obtained by reviewing the medical records.
CT data acquisition
All patients fasted for at least 4 hours, and 20 mg anisodamine (654-2) was administered intramuscularly to reduce gastrointestinal peristalsis 10 minutes prior to CT examination. 800–1000 mL warm water was drank to distend the stomach. CT was performed using a 256-Slice (Brilliance iCT, ROYAL PHILIPS, Eindhoven, Netherlands) or a 64-slice (SOMATON sensation64, SIEMENS Healthcare, Muenchhen, Germany) multi-slice spiral CT. Patients underwent both unenhanced and two-phase enhanced CT examinations (arterial phase: 35 s after injection; venous phase: 70 s after injection). The CT scans, covering the entire stomach region, were acquired during a breath-hold with the patient supine in all of the phases. During the enhanced CT scan, patients were infused with 1.5 mL/kg of the non-ionic contrast material (iohexol, Yangzi River Pharmaceutical Group, Jiangsu, China; iIodine concentration: 300 mg/mL) with a pump injector (Ulrich CT Plus 150, Ulrich Medical, Ulm, Germany) at a rate of 3.0 mL/s into the antecubital vein. The imaging parameters were as follows: 120 kV; 220–250 mAs; rotation time: 0.5 s; detector collimation: 128 × 0.625 mm or 32 × 0.6 mm; field of view: 400 × 400 mm; matrix: 512 × 512; reconstruction slice thickness: 5 mm for axial plane, and 3 mm for coronal and sagittal plane.
The pipeline of this study includes five steps: lesion detection, region of interest (ROI) segmentation, radiomic feature extraction, radiomic signature building, and nomogram construction and evaluation (Fig. 1).
Detection of Lesion on CT Images
All CT images were reviewed by a radiologist with more than 10 years of experience in GC diagnosis. Localization of GC lesions: The 159 patients selected in this study all had the results of gastroscopy and CT examination. Combined with gastroscopy and CT images (axial, coronal and sagittal images), the lesions could be located. The diagnostic criteria of CT-reported LN metastasis-positive were shown as follows: short-axis diameter of LN ≥ 5 mm, the ratio of short diameter to long diameter of LN ≥ 0.7, and the plain CT value of LN ≥ 25 HU or venous phase CT value of LN ≥ 75 HU; or multiple LNs were fused together even if above conditions were not satisfied.
ROI Segmentation on CT Images
Two 2-dimensional ROIs were manually segmented by a radiologist with more than 10 years of experience in GC diagnosis. The first ROI (ROI-1) was delineated on the tumor in the slice with the maximum tumor lesion. The second ROI (ROI-2) was delineated on the region of No.3 station LNs around the lesser curvature of stomach. ROI segmentation was performed using ITK-SNAP software (version 2.2.0; www.itksnap.org) on the venous phase CT images with axial view (see Supplementary A1 for detail).
Extraction of Radiomic Features
Two feature groups were extracted from two ROIs, with each group containing 273 features [27, 28]. These features were divided into 4 categories: (a) shape and size features, (b) gray intensity features, (c) texture features, and (d) wavelet features. The feature extraction was implemented using MATLAB (version 2014a; Mathworks, Natick, MA, USA). Radiomic features of all patients were standardized by the z-score method, based on the parameters calculated from the primary cohort. More information about the radiomic feature extraction is shown in Supplementary A2.
Radiomic Signature Construction
Radiomic feature selection and signature building were performed in the primary cohort for ROI-1 and ROI-2, respectively. More details are described as follows. In order to avoid model over-fitting and improve performance, feature selection was performed to match the sample size (Supplementary A3).
First, the minimum redundancy maximum relevance algorithm (mRMR) ranked each feature based on its relevance to LN metastasis status, and the ranking process was able to consider the redundancy of these features at the same time . Since the number of predictors should be kept within 1/10 − 1/3 of the size of the group that contains the smallest cases in the primary cohort (LN metastasis-positive group, n = 22) , the number of potential features was limited to 7 or less in this study.
Second, five-fold cross-validation was performed multiple times on the primary cohort to find the optimal number of features with the best performance based on ranked features. Then a radiomic signature (RS1) reflecting phenotype of ROI-1 and a radiomic signature (RS2) reflecting phenotype of ROI-2, were built as independent predictors of LN metastasis using selected features, respectively. For each radiomic signature, the signature score was calculated to reflect the risk of LN metastasis. The predictive performance of the radiomic signatures were quantitatively tested using the area under the receiver operator characteristic (ROC) curve in both the primary and validation cohorts.
Construction and Evaluation of Nomogram
Univariate analysis and multivariate analysis were used to screen out significant clinical risk factors. For univariate analysis, continuous variables were assessed using independent t-test or Mann-Whitney U test for differences between different groups, and categorical variables were assessed by Chi-squared test. As for multivariate analysis, we performed multivariate logistic regression to screen out key factors. Furthermore, multivariate logistic regression was used to merge two radiomic signatures and clinical risk factors into a nomogram. Meanwhile, we performed variable selection according to the p-values of the logistic regression. After that, the calibration curves and Hosmer-Lemeshow test were used to assess the goodness-of-fit of the nomogram, and the AUC was used to quantify its predictive performance. For assessing overfitting, DeLong test was adapted to compare AUCs between primary and validation cohorts. Moreover, we used net reclassification index (NRI) to compare the performance between nomogram and clinical risk factors, and quantify the improvement in predictive performance.
Furthermore, a stratified analysis was used to evaluate the influence of clinical factors to the nomogram. In addition, we performed a subgroup analysis to evaluate the additional value of the nomogram in the CT-reported LN metastasis-negative (CT-LNM0) subgroup. Since the number of metastasis in No.4 station LNs (left greater curvature) ranked only behind No.3 station LNs (Supplementary Table S2), we further validated our nomogram on No.4 station LNs.
Finally, to estimate the clinical utility of the nomogram, decision curve analysis (DCA) was performed by calculating the net benefits using a range of threshold probabilities.
All statistical analysis was performed using R software (version 3.3.4; http://www.Rproject.org). A two-sided P value < 0.05 was used to indicate statistical significance.