Introduction
Although the incidence of, and mortality from, gastric cancer (GC) have decreased recently, GC remains the fifth most common cancer and the third leading cause of cancer deaths worldwide (1). Treatment options are limited beacuse many patients exhibit locally advanced cancer at diagnosis (stages T4a, 4b, and N1,2,3). New diagnostic options for locally advanced GC and identification of patient subgroups who can benefit from personalised treatments have become prime research topics (2-4). Multidetector computed tomography (MDCT) is often the primary method used for preoperative GC staging and shows promise in terms of detecting intra-tumour heterogeneity and, thus, variations in differentiation, angiogenesis, and the extracellular matrix (5). Textural analysis (TA) is a new non-invasive technique that can be applied to various images such as those of radiography, ultrasound, MDCT, magnetic resonance imaging, and positron emission tomography; TA assesses spatial changes in tumoural gray level densities (5). TA has attracted particular attention in the context of malignant tumour imaging, and may be used to estimate tumour stage, grade, treatment response, and prognosis (6-9). The diagnostic accuracy of MDCT in terms of T-staging improves from early to advanced stages, but remains unsatisfactory because it is difficult to evaluate all gastric wall layers, particularly the minor curvature (10). However, to the best of our knowledge, no previous report has explored whether TA can detect stage T4 locally advanced GC. Therefore, we retrospectively analysed whether first-order histogram analysis of the preoperative computed tomography “CT” textural features of locally advanced GC patients could be used to predict stage T4 disease.
Methods
Ethics
This single-centre retrospective study adhered to all relevant tenets of the Helsinki Declaration and the Good Clinical Practice Guidelines and was approved by University of Health Sciences Turkey, İstanbul Training and Research Hospital Medical Ethics Committee (approval number: 1750, date: 15.03.2019). All patients were included after their informed consent was obtained.
We retrieved images taken from January 2017 to December 2019 from our archiving system.
Patients
A total of 148 consecutive patients who underwent radical gastrectomy with standard D1+/D2 lymph node dissection to treat locally advanced GC between January 2017 and December 2019 in our hospital were retrospectively analysed. The inclusion criteria were: the availablility of contrast-enhanced CT images taken in our institution within 4 weeks before surgery and histologically confirmed GC. We excluded patients who were very weak because they lacked adequate adipose tissue in the perigastric region in which regions of interest (ROIs) were drawn for TA (n=21), and also those whose images afforded only poor tumour visualisation because of inadequate distension or peristalsis (n=17) We finally included 110 GC patients who underwent CT.
CT image acquisition
CT was performed using a 128-detector scanner (Philips Ingenuity, the Netherlands) or a 64-detector scanner (Aquilion, Canon Medical Systems, Japan). The usual MDCT scan parameters were: 1) 120 kVp; 2) 80-500 mA; 3) slice thickness 2 mm; 4) pitch 0.797-1.5; 5) field-of-view 50x50 cm; 6) rotation time 0.5-0.75 s; 7) window level 400 (200-600); 8) window width 40 (30-60); 9) matrix 512x512; and, 10) reconstruction interval 0.4 mm for 128-detector CT and 0.5 mm for 64-detector CT. A nonionic intravenous contrast agent (2 mL/kg iopromide (maximum: 150 mL); Ultravist 370: Bayer, Berlin, Germany) was delivered via an automatic injector (Optivantage) (Mallinckrodt, 2010); the volume varied by patient weight. Patient drank 1000-1500 mL of water and was injected with a hypotonic agent (20 mg of scopolamine) to distend the stomach before CT scanning.
Image preprocessing
All CT images were labelled, anonymised, and recorded in Digital Imaging and Communication in Medicine format. Pixel gaps were resized and synchronised to a 1x1 mm2 in-plane resolution using free 3D-Slicer software (ver. 4.10.2). To minimise the effects of differences between the two CT devices, image normalisation and grey level discretisation were performed by assigning a gray level range between 1 and 2 kbits/pixel (termed K-values); the MaZda software (see below) analysed only images with K-values of 6. All images were normalised using the ± 3-sigma technique (11,12).
Texture analysis
Preoperative CT images were retrieved from our archive and loaded onto an independent workstation for TA in random order. CT-TA was performed with the aid of MaZda software (ver. 4.6, P.M. Szczypiński, Institute of Electronics, Technical University of Lodz), which is free for research purposes (12). The first line of each region of interest (ROI) was drawn parallel to the gastric wall via the consensus of two radiologists (Aytül Hande Yardımcı and İpek Sel with 12 and 4 years of experience respectively, blinded to histopathological data). When expanding the ROI, the radiologists were careful to avoid large vessels and adjacent organs. In the section exhibiting the greatest portion of the tumour, a line was drawn to the outer tumour boundary. Three-fold dilatation was then performed using MaZda, and the area of perigastric area invasion within the ROI expanded toward that area. All tumour masses were evaluated separately (Figure 1a-c). The software automatically calculated all pixel attenuations within the ROI and generated 275 feature variables including first-, second-, and higher-order statistics for each patient (http://www.Eletel.p.lodz.pl/programy/MaZda). We used only the histogram features (1, 10, 50, 90, and 99% percentiles; minimum, mean, and maximum norm; variance; skewness, and kurtosis). All images were from the portal venous phase. The cases were divided in to two groups by tumour T-stage [T 4 (a, b) and T1-to-3]; calculations were performed separately for each group.
Statistical Analysis
The descriptive statistics include the mean, median, standard deviation, minimum, maximum, and percentage. The distributions of all variables were checked with the aid of the Kolmogorov-Smirnov test. The Independent-samples t-test and the Mann-Whitney U test were used (as appropriate) to compare quantitative data. The chi-squared test was employed to compare qualitative data. Receiver operator curve analysis was performed to assess whether various features were of diagnostic utility. SPSS ver. 26.0 software was used for all statistical analyses.
Surgical resection
All GC resections was performed according to the Japanese Gastric Cancer Society guidelines (13). Radical gastrectomy and standard D1/D2 lymph node dissection were performed by surgeons expert in gastrointestinal system procedures; all cases had pathologically confirmed GC.
Pathological analysis and the reference standard
Specimens were evaluated by reference to edition 8 of the Tumour, Node, Metastasis staging system by two pathologists with 5 and 10 years of experience.
Results
Patient data
A total of 110 patients with histopathologically proven GC were enrolled, of whom 10 had stage T1, 9 T2, 28 T3, and 63 T4 a,b disease. Eighty-four (76.4%) patients were male and 26 (23.6%) female. Histologically, 50 tumours were tubular adenocarcinomas; four papillary adenocarcinomas; three mucinous adenocarcinomas; 13 poorly cohesive carcinomas (including signet ring cell carcinomas and other variants); and 40 mixed adenocarcinomas (tubular, papillary, mucinous, medullary and poorly cohesive GCs).
Histogram analysis
Neither age nor gender differed significantly between the T1-to-T3 and T4 groups (both p˃0.05). Stage T4 tumours were significantly larger in size than stage T1-to-T3 tumours (p˂0.05). In the T4 group, the min, max, and mean norms; and the 1, 10, and 50% percentiles were significantly lower than in the T1-to-T3 group (all p<0.05). Neither the 90 nor 99% percentiles differed between the two groups (both p˃0.05). The T4 variance was significantly greater than that of the T1-to-T3 group (p˂0.05). The T4 skewness was significantly lower than that of the T1-to-T3 group (p˂0.05) but the T4 kurtosis significantly higher (p˂0.05) (Table 1, 2). The mean AUCs and classification accuracies with 95% confidence intervals are listed in Table 3.
Discussion
Locally advanced GC is defined as stage T4 disease in which the tumour perforates the serosa (T4a) or invades adjacent structures (T4b), and often has a poor prognosis because of the presence of peritoneal seeding, liver metastasis, and/or distant lymph node involvement (14). CT-TA has recently been considered a promising tool; CT-TA evaluates gray level distributions and spatial intratumoural heterogeneities (15). Earlier studies suggested that CT-TA might usefully evaluate GC clinical stage, pathological grade, and prognosis (16,17). However, CT-TA has not previously been used to detect serosal invasion (stage-T4) GC. We found significant differences in tumour size; the minimum, maximum, and mean norms; the 1, 10, and 50% percentiles; variance; skewness; and kurtosis between the T4 and T1-to-T3 stages. Skewness derived from portal venous phase images most accurately (AUC: 0.798) distinguished T4 from T1-to-T3 disease. CT attenuation reflects tumour enhancement (18); higher attenuation probably reflects the higher vascularity of more aggressive tumours.
We found that lower skewness and higher kurtosis were significantly associated with T4 status and serosal invasion. Higher skewness and lower kurtosis were significantly associated with the presence of a K-ras mutation in non-small cell lung cancer patients in the study of Weiss et al. (19). Feng et al. (20) showed that volumetric CT textural features, particularly entropy, could potentially serve as biomarkers for risk stratification of small intestinal/gastrointestinal stromal tumours. Here, we performed first-order histogram analysis of single slices; thus, not entire tumours. Previous studies found that CT-TA predicted GC histopathological characteristics (16-20). Liu et al. (17) reported that the invasiveness of tumours of different grades depended principally on the extent of neovascularisation, reflected by attenuation of contrast-enhanced CT.
Study Limitations
Our work had several limitations. First, this was a retrospective single-centre study, with an inevitable patient selection bias. Second, we did not evaluate arterial phase data; this would aid determination of gastric wall invasion depth, especially early in disease progression.
Thirdly, when the perigastric area invasion of the tumor was evaluated after 3-fold dilatation using the MaZda program, both the perigastric and the area towards the tumor fell within the limits of the analysis. Considering different tumor types, this creates a limitation since it is analyzed in the tumor with the perigastric area, and in the next studies, the analysis by removing the tumor margin will give more successful results in evaluating the actual perigastric area invasion.
Finally, in weak patients, the absence of perigastric adipose tissue rendered it difficult to draw ROIs and perform CT-TA; we excluded such patients.
Conclusion
The histogram parameters of CT-TA, especially skewness and kurtosis derived from portal, venous phase CT images, may serve as biomarkers stratifying the risk of serosal invasion (stage-T4) by locally advanced GC. Thus, histogram analysis can be used preoperatively to evaluate serosal invasion.
Ethics Committee Approval: This single-centre retrospective study adhered to all relevant tenets of the Helsinki Declaration and the Good Clinical Practice Guidelines and was approved by University of Health Sciences Turkey, İstanbul Training and Research Hospital Medical Ethics Committee (approval number: 1750, date: 15.03.2019).
Informed Consent: All patients were included after their informed consent was obtained.
Peer-review: Externally peer-reviewed.
Authorship Contributions: Surgical and Medical Practices - V.H.Y.; Concept - A.H.Y., V.H.Y., İ.S., C.T.B.; Design - A.H.Y., Ö.M., C.T.B.; Data Collection or Processing - Ö.M., İ.S.; Analysis or Interpretation - A.H.Y., C.T.B.; Literature Search - Ö.M., V.H.Y., İ.S.; Writing - A.H.Y.
Conflict of Interest: No conflict of interest was declared by the authors.
Financial Disclosure: The authors declared that this study received no financial support.