Gastric cancer by multidetector row CT: preoperative staging

Construction of a predictive model for gastric cancer neuroaggression and clinical validation analysis: A single-center retrospective study

BACKGROUND

This study investigated the construction and clinical validation of a predictive model for neuroaggression in patients with gastric cancer. Gastric cancer is one of the most common malignant tumors in the world, and neuroinvasion is the key factor affecting the prognosis of patients. However, there is a lack of systematic analysis on the construction and clinical application of its prediction model. This study adopted a single-center retrospective study method, collected a large amount of clinical data, and applied statistics and machine learning technology to build and verify an effective prediction model for neuroaggression, with a view to providing scientific basis for clinical treatment decisions and improving the treatment effect and survival rate of patients with gastric cancer.

AIM

To investigate the value of a model based on clinical data, spectral computed tomography (CT) parameters and image omics characteristics for the preoperative prediction of nerve invasion in patients with gastric cancer.

METHODS

A retrospective analysis was performed on 80 gastric cancer patients who underwent preoperative energy spectrum CT at our hospital between January 2022 and August 2023, these patients were divided into a positive group and a negative group according to their pathological results. Clinicopathological data were collected, the energy spectrum parameters of primary gastric cancer lesions were measured, and single factor analysis was performed. A total of 214 image omics features were extracted from two-phase mixed energy images, and the features were screened by single factor analysis and a support vector machine. The variables with statistically significant differences were included in logistic regression analysis to construct a prediction model, and the performance of the model was evaluated using the subject working characteristic curve.

RESULTS

There were statistically significant differences in sex, carbohydrate antigen 199 expression, tumor thickness, Lauren classification and Borrmann classification between the two groups (all P < 0.05). Among the energy spectrum parameters, there were statistically significant differences in the single energy values (CT60-CT110 keV) at the arterial stage between the two groups (all P < 0.05) and statistically significant differences in CT values, iodide group values, standardized iodide group values and single energy values except CT80 keV at the portal vein stage between the two groups (all P < 0.05). The support vector machine model with the largest area under the curve was selected by image omics analysis, and its area under the curve, sensitivity, specificity, accuracy, P value and parameters were 0.843, 0.923, 0.714, 0.925, < 0.001, and c:g 2.64:10.56, respectively. Finally, based on the logistic regression algorithm, a clinical model, an energy spectrum CT model, an imaging model, a clinical + energy spectrum model, a clinical + imaging model, an energy spectrum + imaging model, and a clinical + energy spectrum + imaging model were established, among which the clinical + energy spectrum + imaging model had the best efficacy in diagnosing gastric cancer nerve invasion. The area under the curve, optimal threshold, Youden index, sensitivity and specificity were 0.927 (95%CI: 0.850-1.000), 0.879, 0.778, 0.778, and 1.000, respectively.

CONCLUSION

The combined model based on clinical features, spectral CT parameters and imaging data has good value for the preoperative prediction of gastric cancer neuroinvasion.

Energy spectrum computed tomography multi-parameter imaging in preoperative assessment of vascular and neuroinvasive status in gastric cancer

BACKGROUND

Vascular and nerve infiltration are important indicators for the progression and prognosis of gastric cancer (GC), but traditional imaging methods have some limitations in preoperative evaluation. In recent years, energy spectrum computed tomography (CT) multiparameter imaging technology has been gradually applied in clinical practice because of its advantages in tissue contrast and lesion detail display.

AIM

To explore and analyze the value of multiparameter energy spectrum CT imaging in the preoperative assessment of vascular invasion (LVI) and nerve invasion (PNI) in GC patients.

METHODS

Data from 62 patients with GC confirmed by pathology and accompanied by energy spectrum CT scanning at our hospital between September 2022 and September 2023, including 46 males and 16 females aged 36-71 (57.5 ± 9.1) years, were retrospectively collected. The patients were divided into a positive group (42 patients) and a negative group (20 patients) according to the presence of LVI/PNI. The CT values (CT40 keV, CT70 keV), iodine concentration (IC), and normalized IC (NIC) of lesions in the upper energy spectrum CT images of the arterial phase, venous phase, and delayed phase 40 and 70 keV were measured, and the slopes of the energy spectrum curves [K (40-70)] from 40 to 70 keV were calculated. Arterial phase combined parameter, venous phase combined parameters (VP-ALLs), and delayed phase association parameters were calculated for patients with late-stage disease. The differences in the energy spectrum parameters between the positive and negative groups were compared, receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC), sensitivity, specificity, and optimal threshold were calculated to measure the diagnostic efficiency of each parameter.

RESULTS

In the delayed phase, the CT40 keV, CT70 keV, K (40-70), IC, NIC, and CT70 keV and the NIC in the upper arterial and venous phases of energy spectrum CT were greater in the LVI/PNI-positive group than in the LVI-negative group. The representative parameters for the arterial phase NIC were 0.14 ± 0.04 in the positive group and 0.12 ± 0.04 in the negative group. The venous phase NIC was 0.5 (0.5, 0.6) in the positive group and 0.4 (0.4, 0.5) in the negative group. Last, for the delayed phase NIC, it was 0.6 ± 0.1 in the positive group and 0.5 ± 0.1 in the negative group (all P values are less than 0.05). ROC curve analysis demonstrated that the diagnostic efficacy of each parameter during the venous stage was superior to that during the arterial and delayed stages. Furthermore, the diagnostic efficacy of the combined parameter throughout all three stages was superior to that of any single parameter. The AUC, sensitivity, and specificity of the optimal parameter, VP-ALL, were 0.931 (95% confidence interval: 0.872-0.990), 80.95%, and 95.00%, respectively.

CONCLUSION

When assessing the condition of LVI and PNI (perineural invasion) in patients with GC prior to surgery, the ability to diagnose these conditions using venous stage parameters was superior to that using arterial stage and delayed stage parameters. Furthermore, the diagnostic accuracy of using a combination of parameters was better than that of using individual parameters alone.

Predicting tumor invasion depth in gastric cancer: developing and validating multivariate models incorporating preoperative IVIM-DWI parameters and MRI morphological characteristics

INTRODUCTION

Accurate assessment of the depth of tumor invasion in gastric cancer (GC) is vital for the selection of suitable patients for neoadjuvant chemotherapy (NAC). Current problem is that preoperative differentiation between T1-2 and T3-4 stage cases in GC is always highly challenging for radiologists.

METHODS

A total of 129 GC patients were divided into training (91 cases) and validation (38 cases) cohorts. Pathology from surgical specimens categorized patients into T1-2 and T3-4 stages. IVIM-DWI and MRI morphological characteristics were evaluated, and a multimodal nomogram was developed. The MRI morphological model, IVIM-DWI model, and combined model were constructed using logistic regression. Their effectiveness was assessed using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).

RESULTS

The combined nomogram, integrating preoperative IVIM-DWI parameters (D value) and MRI morphological characteristics (maximum tumor thickness, extra-serosal invasion), achieved the highest area under the curve (AUC) values of 0.901 and 0.883 in the training and validation cohorts, respectively. No significant difference was observed between the AUCs of the IVIM-DWI and MRI morphological models in either cohort (training: 0.796 vs. 0.835, p = 0.593; validation: 0.794 vs. 0.766, p = 0.79).

CONCLUSION

The multimodal nomogram, combining IVIM-DWI parameters and MRI morphological characteristics, emerges as a promising tool for assessing tumor invasion depth in GC, potentially guiding the selection of suitable candidates for neoadjuvant chemotherapy (NAC) treatment.

Double contrast-enhanced ultrasonography improves diagnostic accuracy of T staging compared with multi-detector computed tomography in gastric cancer patients

BACKGROUND

Gastric cancer (GC) is the most common malignant tumor and ranks third for cancer-related deaths among the worldwide. The disease poses a serious public health problem in China, ranking fifth for incidence and third for mortality. Knowledge of the invasive depth of the tumor is vital to treatment decisions.

AIM

To evaluate the diagnostic performance of double contrast-enhanced ultrasonography (DCEUS) for preoperative T staging in patients with GC by comparing with multi-detector computed tomography (MDCT).

METHODS

This single prospective study enrolled patients with GC confirmed by preoperative gastroscopy from July 2021 to March 2023. Patients underwent DCEUS, including ultrasonography (US) and intravenous contrast-enhanced ultrasonography (CEUS), and MDCT examinations for the assessment of preoperative T staging. Features of GC were identified on DCEUS and criteria developed to evaluate T staging according to the 8^th edition of AJCC cancer staging manual. The diagnostic performance of DCEUS was evaluated by comparing it with that of MDCT and surgical-pathological findings were considered as the gold standard.

RESULTS

A total of 229 patients with GC (80 T1, 33 T2, 59 T3 and 57 T4) were included. Overall accuracies were 86.9% for DCEUS and 61.1% for MDCT (P < 0.001). DCEUS was superior to MDCT for T1 (92.5% vs 70.0%, P < 0.001), T2 (72.7% vs 51.5%, P = 0.041), T3 (86.4% vs 45.8%, P < 0.001) and T4 (87.7% vs 70.2%, P = 0.022) staging of GC.

CONCLUSION

DCEUS improved the diagnostic accuracy of preoperative T staging in patients with GC compared with MDCT, and constitutes a promising imaging modality for preoperative evaluation of GC to aid individualized treatment decision-making.

Advanced Gastric Cancer: CT Radiomics Prediction of Lymph Modes Metastasis After Neoadjuvant Chemotherapy

This study aims to create and assess machine learning models for predicting lymph node metastases following neoadjuvant treatment in advanced gastric cancer (AGC) using baseline and restaging computed tomography (CT). We evaluated CT images and pathological data from 158 patients with resected stomach cancer from two institutions in this retrospective analysis. Patients were eligible for inclusion if they had histologically proven gastric cancer. They had received neoadjuvant chemotherapy, with at least 15 lymph nodes removed. All patients received baseline and preoperative abdominal CT and had complete clinicopathological reports. They were divided into two cohorts: (a) the primary cohort (n = 125) for model creation and (b) the testing cohort (n = 33) for evaluating models' capacity to predict the existence of lymph node metastases. The diagnostic ability of the radiomics-model for lymph node metastasis was compared to traditional CT morphological diagnosis by radiologist. The radiomics model based on the baseline and preoperative CT images produced encouraging results in the training group (AUC 0.846) and testing cohort (AUC 0.843). In the training cohort, the sensitivity and specificity were 81.3% and 77.8%, respectively, whereas in the testing cohort, they were 84% and 75%. The diagnostic sensitivity and specificity of the radiologist were 70% and 42.2% (using baseline CT) and 46.3% and 62.2% (using preoperative CT). In particular, the specificity of radiomics model was higher than that of conventional CT in diagnosing N0 cases (no lymph node metastasis). The CT-based radiomics model could assess lymph node metastasis more accurately than traditional CT imaging in AGC patients following neoadjuvant chemotherapy.

Gastric Outlet Obstruction Secondary to Gastric Adenocarcinoma Diagnosed on Ultrasonography

Gastric outlet obstruction often manifests as a result of mural, luminal, or extrinsic compression. Due to capacity of the stomach to distend 2-4 L after food intake, gastric outlet obstruction secondary to a malignant cause goes often undetected clinically until a high-grade obstruction develops. Gastric adenocarcinoma seldom manifests as gastric outlet obstruction secondary to a partially obstructing mass or a stricture that develops due to peptic ulceration. Fatal sequelae and serious complications of gastric outlet obstruction may result when early detection and appropriate intervention such as gastric decompression and surgical resection are delayed. This report describes a rare case of gastric adenocarcinoma causing gastric outlet obstruction diagnosed on ultrasonography in a 40-year-old female.

Predictive nomogram for lymph node metastasis and survival in gastric cancer using contrast-enhanced computed tomography-based radiomics: a retrospective study

Background

Lymph node involvement significantly impacts the survival of gastric cancer patients and is a crucial factor in determining the appropriate treatment. This study aimed to evaluate the potential of enhanced computed tomography (CT)-based radiomics in predicting lymph node metastasis (LNM) and survival in patients with gastric cancer before surgery.

Methods

Retrospective analysis of clinical data from 192 patients diagnosed with gastric carcinoma was conducted. The patients were randomly divided into a training cohort (n = 128) and a validation cohort (n = 64). Radiomic features of CT images were extracted using the Pyradiomics software platform, and distinctive features were further selected using a Lasso Cox regression model. Features significantly associated with LNM were identified through univariate and multivariate analyses and combined with radiomic scores to create a nomogram model for predicting lymph node involvement before surgery. The predictive performance of radiomics features, CT-reported lymph node status, and the nomogram model for LNM were compared in the training and validation cohorts by plotting receiver operating characteristic (ROC) curves. High-risk and low-risk groups were identified in both cohorts based on the cut-off value of 0.582 within the radiomics evaluation scheme, and survival rates were compared.

Results

Seven radiomic features were identified and selected, and patients were stratified into high-risk and low-risk groups using a 0.582 cut-off radiomics score. Univariate and multivariate analyses revealed that radiomics features, diabetes mellitus, Nutrition Risk Screening (NRS) 2002 score, and CT-reported lymph node status were significant predictors of LNM in patients with gastric cancer. A predictive nomogram model was developed by combining these predictors with the radiomics score, which accurately predicted LNM in gastric cancer patients before surgery and outperformed other models in terms of accuracy and sensitivity. The AUC values for the training and validation cohorts were 0.82 and 0.722, respectively. The high-risk and low-risk groups in both the training and validation cohorts showed significant differences in survival rates.

Conclusion

The radiomics nomogram, based on contrast-enhanced computed tomography (CECT ), is a promising non-invasive tool for preoperatively predicting LNM in gastric cancer patients and postoperative survival.

Diagnostic performance of CT scan-based radiomics for prediction of lymph node metastasis in gastric cancer: a systematic review and meta-analysis

Objective

The purpose of this study was to evaluate the diagnostic performance of computed tomography (CT) scan-based radiomics in prediction of lymph node metastasis (LNM) in gastric cancer (GC) patients.

Methods

PubMed, Embase, Web of Science, and Cochrane Library databases were searched for original studies published until 10 November 2022, and the studies satisfying the inclusion criteria were included. Characteristics of included studies and radiomics approach and data for constructing 2 × 2 tables were extracted. The radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) were utilized for the quality assessment of included studies. Overall sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated to assess diagnostic accuracy. The subgroup analysis and Spearman's correlation coefficient was done for exploration of heterogeneity sources.

Results

Fifteen studies with 7,010 GC patients were included. We conducted analyses on both radiomics signature and combined (based on signature and clinical features) models. The pooled sensitivity, specificity, DOR, and AUC of radiomics models compared to combined models were 0.75 (95% CI, 0.67-0.82) versus 0.81 (95% CI, 0.75-0.86), 0.80 (95% CI, 0.73-0.86) versus 0.85 (95% CI, 0.79-0.89), 13 (95% CI, 7-23) versus 23 (95% CI, 13-42), and 0.85 (95% CI, 0.81-0.86) versus 0.90 (95% CI, 0.87-0.92), respectively. The meta-analysis indicated a significant heterogeneity among studies. The subgroup analysis revealed that arterial phase CT scan, tumoral and nodal regions of interest (ROIs), automatic segmentation, and two-dimensional (2D) ROI could improve diagnostic accuracy compared to venous phase CT scan, tumoral-only ROI, manual segmentation, and 3D ROI, respectively. Overall, the quality of studies was quite acceptable based on both QUADAS-2 and RQS tools.

Conclusion

CT scan-based radiomics approach has a promising potential for the prediction of LNM in GC patients preoperatively as a non-invasive diagnostic tool. Methodological heterogeneity is the main limitation of the included studies.

Systematic review registration

https://www.crd.york.ac.uk/Prospero/display_record.php?RecordID=287676, identifier CRD42022287676.

A Nomogram Based on Nutrition-Related Indicators and Computed Tomography Imaging Features for Predicting Preoperative Lymph Node Metastasis in Curatively Resected Esophagogastric Junction Adenocarcinoma

BACKGROUNDS

Preoperative noninvasive tools to predict pretreatment lymph node metastasis (PLNM) status accurately for esophagogastric junction adenocarcinoma (EJA) are few. Thus, the authors aimed to construct a nomogram for predicting PLNM in curatively resected EJA.

METHODS

This study enrolled 638 EJA patients who received curative surgery resection and divided them randomly (7:3) into training and validation groups. For nomogram construction, 26 candidate parameters involving 21 preoperative clinical laboratory blood nutrition-related indicators, computed tomography (CT)-reported tumor size, CT-reported PLNM, gender, age, and body mass index were screened.

RESULTS

In the training group, Lasso regression included nine nutrition-related blood indicators in the PLNM-prediction nomogram. The PLNM prediction nomogram yielded an area under the receiver operating characteristic (ROC) curve of 0.741 (95 % confidence interval [CI], 0.697-0.781), which was better than that of the CT-reported PLNM (0.635; 95% CI 0.588-0.680; p < 0.0001). Application of the nomogram in the validation cohort still gave good discrimination (0.725 [95% CI 0.658-0.785] vs 0.634 [95% CI 0.563-0.700]; p = 0.0042). Good calibration and a net benefit were observed in both groups.

CONCLUSIONS

This study presented a nomogram incorporating preoperative nutrition-related blood indicators and CT imaging features that might be used as a convenient tool to facilitate the preoperative individualized prediction of PLNM for patients with curatively resected EJA.

Imaging of Gastrointestinal Tract Ailments

Gastrointestinal (GI) disorders comprise a diverse range of conditions that can significantly reduce the quality of life and can even be life-threatening in serious cases. The development of accurate and rapid detection approaches is of essential importance for early diagnosis and timely management of GI diseases. This review mainly focuses on the imaging of several representative gastrointestinal ailments, such as inflammatory bowel disease, tumors, appendicitis, Meckel's diverticulum, and others. Various imaging modalities commonly used for the gastrointestinal tract, including magnetic resonance imaging (MRI), positron emission tomography (PET) and single photon emission computed tomography (SPECT), and photoacoustic tomography (PAT) and multimodal imaging with mode overlap are summarized. These achievements in single and multimodal imaging provide useful guidance for improved diagnosis, staging, and treatment of the corresponding gastrointestinal diseases. The review evaluates the strengths and weaknesses of different imaging techniques and summarizes the development of imaging techniques used for diagnosing gastrointestinal ailments.

A new radiomics approach combining the tumor and peri-tumor regions to predict lymph node metastasis and prognosis in gastric cancer

Objective

The development of non-invasive methods for evaluating lymph node metastasis (LNM) preoperatively in gastric cancer (GC) is necessary. In this study, we developed a new radiomics model combining features from the tumor and peri-tumor regions for predicting LNM and prognoses.

Methods

This was a retrospective observational study. In this study, two cohorts of patients with GC treated in Zhongshan Hospital Fudan University (Shanghai, China) were included. In total, 193 patients were assigned to the internal training/validation cohort; another 98 patients were assigned to the independent testing cohort. The radiomics features were extracted from venous phase computerized tomography (CT) images. The radiomics model was constructed and the output was defined as the radiomics score (RS). The performance of the RS and CT-defined N status (ctN) for predicting LNM was compared using the area under the curve (AUC). The 5-year overall survival and progression-free survival were compared between different subgroups using Kaplan-Meier curves.

Results

In both cohorts, the RS was significantly higher in the LNM-positive group than that in the LNM-negative group (all P < 0.001). The radiomics model combining features from the tumor and peri-tumor regions achieved the highest AUC in predicting LNM (AUC, 0.779 and 0.724, respectively), which performed better than the radiomics model based only on the tumor region and ctN (AUC, 0.717, 0.622 and 0.710, 0.603, respectively). The differences in 5-year overall survival and progression-free survival between high-risk and low-risk groups were significant (both P < 0.001).

Conclusions

The radiomics model combining features from the tumor and peri-tumor regions could effectively predict the LNM in GC. Risk stratification based on the RS was capable of distinguishing patients with poor prognoses.

The value of CT-based radiomics nomogram in differential diagnosis of different histological types of gastric cancer

To establish and verify a nomogram based on computed tomography (CT) radiomics analysis to predict the histological types of gastric cancer preoperatively for patients with surgical indications. A sum of 171 patients with gastric cancer were included into this retrospective study. The least absolute shrinkage and selection operator (LASSO) was used for feature selection while the multivariate Logistic regression method was used for radiomics model and nomogram building. The area under curve (AUC) was used for performance evaluation in this study. The radiomics model got AUCs of 0.755 (95% CI 0.650-0.859), 0.71 (95% CI 0.543-0.875) and 0.712 (95% CI 0.500-0.923) for histological prediction in the training, the internal and external verification cohorts. The radiomics nomogram based on radiomics features and Carbohydrate antigen 125 (CA125) showed good discriminant performance in the training cohort (AUC: 0.777; 95% CI 0.679-0.875), the internal (AUC: 0.726; 95% CI 0.5591-0.8933) and external verification cohort (AUC: 0.720; 95% CI 0.5036-0.9358). The calibration curve of the radiomics nomogram also showed good results. The decision curve analysis (DCA) shows that the radiomics nomogram is clinically practical. The radiomics nomogram established and verified in this study showed good performance for the preoperative histological prediction of gastric cancer, which might contribute to the formulation of a better clinical treatment plan.

Added Value of the Sliding Sign on Right Down Decubitus CT for Determining Adjacent Organ Invasion in Patients with Advanced Gastric Cancer

Purpose

To investigate the added value of right down decubitus (RDD) CT when determining adjacent organ invasion in cases of advanced gastric cancer (AGC).

Materials and Methods

A total of 728 patients with pathologically confirmed T4a (pT4a), surgically confirmed T4b (sT4b), or pathologically confirmed T4b (pT4b) AGCs who underwent dedicated stomach-protocol CT, including imaging of the left posterior oblique (LPO) and RDD positions, were included in this study. Two radiologists scored the T stage of AGCs using a 5-point scale on LPO CT with and without RDD CT at 2-week intervals and recorded the presence of "sliding sign" in the tumors and adjacent organs and compared its incidence of appearance.

Results

A total of 564 patients (77.4%) were diagnosed with pT4a, whereas 65 (8.9%) and 99 (13.6%) patients were diagnosed with pT4b and sT4b, respectively. When RDD CT was performed additionally, both reviewers deemed that the area under the curve (AUC) for differentiating T4b from T4a increased (p < 0.001). According to both reviewers, the AUC for differentiating T4b with pancreatic invasion from T4a increased in the subgroup analysis (p < 0.050). Interobserver agreement improved from fair to moderate (weighted kappa value, 0.296-0.444).

Conclusion

RDD CT provides additional value compared to LPO CT images alone for determining adjacent organ invasion in patients with AGC due to their increased AUC values and improved interobserver agreement.

Computed Tomography-Based Deep Learning Nomogram Can Accurately Predict Lymph Node Metastasis in Gastric Cancer

BACKGROUND

Computed tomography is the most commonly used imaging modality for preoperative assessment of lymph node status, but the reported accuracy is unsatisfactory.

AIMS

To evaluate and verify the predictive performance of computed tomography deep learning on the presurgical evaluation of lymph node metastasis in patients with gastric cancer.

METHODS

347 patients were retrospectively selected (training cohort: 242, test cohort: 105). The enhanced computed tomography arterial phase images of gastric cancer were used for lesion segmentation, radiomics and deep learning feature extraction. Three methods were used for feature selection. Support vector machine (SVM) or random forest (RF) was used to build models. The classification performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC). We also established a nomogram that included clinical predictors.

RESULTS

The model based on ResNet50-RF showed favorable classification performance and was verified in the test cohort (AUC = 0.9803). The nomogram based on deep learning feature scores and the lymph node status reported by computed tomography showed excellent discrimination. AUC of 0.9978 was achieved in the training cohort and verified in the test cohort (AUC = 0.9914). Decision analysis curve showed the value of nomogram in clinical application.

CONCLUSION

The computed tomography-based deep learning nomogram can accurately and effectively evaluate lymph node metastasis in patients with gastric cancer before surgery.

Gastric hydrodistension CT versus CT without gastric distension in preoperative TN staging of gastric carcinoma: analysis of single-center cancer registry

Accurate staging of gastric cancer is essential for the selection and optimization of therapy. Hydrodistension of the stomach is recommended to improve the accuracy of preoperative staging with contrast-enhanced multidetector computed tomography (MDCT). This study compares the performance of contrast-enhanced gastric water distension versus a nondistension MDCT protocol for T and N staging and serosal invasion in comparison to surgical histopathology. After propensity score matching, 86 patients in each group were included for analysis. The overall accuracy of distension versus nondistension group in T staging was 45% (95% CI 35-56) and 55% (95% CI 44-65), respectively (p = 0.29). There was no difference in the sensitivity and specificity in individual T staging and assessment of serosal invasion (all p > 0.41). Individual stage concordance with pathology was not significantly different (all p > 0.41). The overall accuracy of N staging was the same for distension and nondistension groups (51% [95% CI 40-62]). The majority of N0 staging (78-81%) were correctly staged, whereas N3 staging cases (63-68%) were predominantly understaged. In summary, there was no significant difference in the diagnostic performance of individual TN staging and assessment of serosal invasion using MDCT with or without gastric water distension.

Correlation between pneumo-computed tomography and pathology findings for subepithelial gastric lesions

OBJECTIVE

This article aims to show the usefulness of the pneumo-computed tomography gastric distention technique in the detection and morphological characterization of subepithelial gastric lesions. We correlate the pneumo-computed tomography and pathology findings in lesions studied at our institution and review the relevant literature.

CONCLUSION

Pneumo-computed tomography, combined with multiplanar reconstructions, three-dimensional reconstructions, and virtual endoscopy, is useful for delineating the morphological details of subepithelial gastric lesions, thanks to the additional gastric distention. This technique better delimits and characterizes the upper and lower margins of the lesions. Pneumo-computed tomography can be considered a useful noninvasive imaging techniques for characterizing these lesions.

The current and possible future role of 3D modelling within oesophagogastric surgery: a scoping review

BACKGROUND

3D reconstruction technology could revolutionise medicine. Within surgery, 3D reconstruction has a growing role in operative planning and procedures, surgical education and training as well as patient engagement. Whilst virtual and 3D printed models are already used in many surgical specialities, oesophagogastric surgery has been slow in their adoption. Therefore, the authors undertook a scoping review to clarify the current and future roles of 3D modelling in oesophagogastric surgery, highlighting gaps in the literature and implications for future research.

METHODS

A scoping review protocol was developed using a comprehensive search strategy based on internationally accepted guidelines and tailored for key databases (MEDLINE, Embase, Elsevier Scopus and ISI Web of Science). This is available through the Open Science Framework (osf.io/ta789) and was published in a peer-reviewed journal. Included studies underwent screening and full text review before inclusion. A thematic analysis was performed using pre-determined overarching themes: (i) surgical training and education, (ii) patient education and engagement, and (iii) operative planning and surgical practice. Where applicable, subthemes were generated.

RESULTS

A total of 56 papers were included. Most research was low-grade with 88% (n = 49) of publications at or below level III evidence. No randomised control trials or systematic reviews were found. Most literature (86%, n = 48) explored 3D reconstruction within operative planning. These were divided into subthemes of pre-operative (77%, n = 43) and intra-operative guidance (9%, n = 5). Few papers reported on surgical training and education (14%, n = 8), and were evenly subcategorised into virtual reality simulation (7%, n = 4) and anatomical teaching (7%, n = 4). No studies utilising 3D modelling for patient engagement and education were found.

CONCLUSION

The use of 3D reconstruction is in its infancy in oesophagogastric surgery. The quality of evidence is low and key themes, such as patient engagement and education, remain unexplored. Without high quality research evaluating the application and benefits of 3D modelling, oesophagogastric surgery may be left behind.

Multi-view learning for lymph node metastasis prediction using tumor and nodal radiomics in gastric cancer

Purpose. This study aims to develop and validate a multi-view learning method by the combination of primary tumor radiomics and lymph node (LN) radiomics for the preoperative prediction of LN status in gastric cancer (GC). Methods. A total of 170 contrast-enhanced abdominal CT images from GC patients were enrolled in this retrospective study. After data preprocessing, two-step feature selection approach including Pearson correlation analysis and supervised feature selection method based on test-time budget (FSBudget) was performed to remove redundance of tumor and LN radiomics features respectively. Two types of discriminative features were then learned by an unsupervised multi-view partial least squares (UMvPLS) for a latent common space on which a logistic regression classifier is trained. Five repeated random hold-out experiments were employed. Results. On 20-dimensional latent common space, area under receiver operating characteristic curve (AUC), precision, accuracy, recall and F1-score are 0.9531 ± 0.0183, 0.9260 ± 0.0184, 0.9136 ± 0.0174, 0.9468 ± 0.0106 and 0.9362 ± 0.0125 for the training cohort respectively, and 0.8984 ± 0.0536, 0.8671 ± 0.0489, 0.8500 ± 0.0599, 0.9118 ± 0.0550 and 0.8882 ± 0.0440 for the validation cohort respectively (reported as mean ± standard deviation). It shows a better discrimination capability than single-view methods, our previous method, and eight baseline methods. When the dimension was reduced to 2, the model not only has effective prediction performance, but also is convenient for data visualization. Conclusions. Our proposed method by integrating radiomics features of primary tumor and LN can be helpful in predicting lymph node metastasis in patients of GC. It shows multi-view learning has great potential for guiding the prognosis and treatment decision-making in GC.

Assessment of gastric wall structure using ultra-high-resolution computed tomography

PURPOSE

To evaluate the image quality of ultra-high-resolution CT (U-HRCT) in the comparison among four different reconstruction methods, focusing on the gastric wall structure, and to compare the conspicuity of a three-layered structure of the gastric wall between conventional HRCT (C-HRCT) and U-HRCT.

METHOD

Our retrospective study included 48 patients who underwent contrast-enhanced U-HRCT. Quantitative analyses were performed to compare image noise of U-HRCT between deep-learning reconstruction (DLR) and other three methods (filtered back projection: FBP, hybrid iterative reconstruction: Hybrid-IR, and Model-based iterative reconstruction: MBIR). The mean overall image quality scores were also compared between the DLR and other three methods. In addition, the mean conspicuity scores for the three-layered structure of the gastric wall at five regions were compared between C-HRCT and U-HRCT.

RESULTS

The mean noise of U-HRCT with DLR was significantly lower than that with the other three methods (P < 0.001). The mean overall image quality scores with DLR images were significantly higher than those with the other three methods (P < 0.001). Regarding the comparison between C-HRCT and U-HRCT, the mean conspicuity scores for the three-layered structure of the gastric wall on U-HRCT were significantly better than those on C-HRCT in the fornix (5 [5-5] vs. 3.5 [3-4], P < 0.001), body (4 [3.25-5] vs. 4 [3-4], P = 0.039), angle (5 [4-5] vs. 3 [2-4], P < 0.001), and antral posterior (4 [3.25-5] vs. 2 [2-4], P < 0.001), except for antral anterior (4 [3-5] vs. 3 [3-4], P = 0.230) CONCLUSION: U-HRCT using DLR improved the image noise and overall image quality of the gastric wall as well as the conspicuity of the three-layered structure, suggesting its utility for the evaluation of the anatomical details of the gastric wall structure.

Radiomics signature based on computed tomography images for the preoperative prediction of lymph node metastasis at individual stations in gastric cancer: A multicenter study

BACKGROUND

Specific diagnosis and treatment of gastric cancer (GC) require accurate preoperative predictions of lymph node metastasis (LNM) at individual stations, such as estimating the extent of lymph node dissection. This study aimed to develop a radiomics signature based on preoperative computed tomography (CT) images, for predicting the LNM status at each individual station.

METHODS

We enrolled 1506 GC patients retrospectively from two centers as training (531) and external (975) validation cohorts, and recruited 112 patients prospectively from a single center as prospective validation cohort. Radiomics features were extracted from preoperative CT images and integrated with clinical characteristics to construct nomograms for LNM prediction at individual lymph node stations. Performance of the nomograms was assessed through calibration, discrimination and clinical usefulness.

RESULTS

In training, external and prospective validation cohorts, radiomics signature was significantly associated with LNM status. Moreover, radiomics signature was an independent predictor of LNM status in the multivariable logistic regression analysis. The radiomics nomograms revealed good prediction performances, with AUCs of 0.716-0.871 in the training cohort, 0.678-0.768 in the external validation cohort and 0.700-0.841 in the prospective validation cohort for 12 nodal stations. The nomograms demonstrated a significant agreement between the actual probability and predictive probability in calibration curves. Decision curve analysis showed that nomograms had better net benefit than clinicopathologic characteristics.

CONCLUSION

Radiomics nomograms for individual lymph node stations presented good prediction accuracy, which could provide important information for individual diagnosis and treatment of gastric cancer.

Clinical Research of Combined Application of DCEUS and Dynamic Contrast-Enhanced MSCT in Preoperative cT Staging of Gastric Cancer

Purpose

To investigate the clinical value of double contrast-enhanced ultrasound (DCEUS) combined with dynamic contrast-enhanced multislice CT (MSCT) in preoperative T staging of gastric cancer (GC).

Methods

206 patients with GC confirmed by preoperative gastroscopy from February 2019 to February 2021 were collected, all patients were examined by DCEUS and dynamic contrast-enhanced MSCT before operation, and the invasion depth (T staging) of GC was evaluated. The diagnosis results of DCEUS, dynamic contrast-enhanced MSCT, and combined diagnosis of DCEUS and MSCT methods (D&M method) were compared with the pathological staging results (gold standard).

Results

The correct diagnosis rate of MSCT was 27.27% in T1 staging, 55.56% in T2 staging, 42.11% in T3 staging, 59.29% in T4 staging, and 55.34% in summation. The correct diagnosis rate of DCEUS was 90.91% in T1 staging, 88.89% in T2 staging, 78.95% in T3 staging, 82.86% in T4 staging, and 83.98% in summation. The correct diagnosis rate of the D&M method was 100.00% in T1 staging, 94.44% in T2 staging, 89.47% in T3 staging, 93.57% in T4 staging, and 93.69% in summation. The D&M method had higher correct diagnosis rate than MSCT or DCEUS alone, the correct diagnosis rate of the D&M method in T1, T2, T3, and T4 staging was significantly higher than that of MSCT (P < 0.05). The correct diagnosis rate of the D&M method in T1, T3, and T4 was significantly higher than that of DCEUS (P < 0.05). The Youden index of preoperative T1, T2, T3, and T4 staging of GC by the D&M method was 99.49%, 94.44%, 84.13%, and 90.54%, respectively, and the Kappa values of these were 0.954, 0.966, 0.707, and 0.881, respectively.

Conclusions

Dynamic contrast-enhanced MSCT combined with DCEUS in the diagnosis of preoperative cT staging of GC has more validity, reliability, and revenue than the using of MSCT or DCEUS alone, which is an image evaluation method worthy of clinical promotion.

Nomograms for Predicting the Lymph Node Metastasis in Early Gastric Cancer by Gender: A Retrospective Multicentric Study

Background

Lymph node metastasis (LNM) has a significant impact on the prognosis of patients with early gastric cancer (EGC). Our aim was to identify the independent risk factors for LNM and construct nomograms for male and female EGC patients, respectively.

Methods

Clinicopathological data of 1,742 EGC patients who underwent radical gastrectomy and lymphadenectomy in the First Affiliated Hospital, Second Affiliated Hospital, and Fourth Affiliated Hospital of Anhui Medical University between November 2011 and April 2021 were collected and analyzed retrospectively. Male and female patients from the First Affiliated Hospital of Anhui Medical University were assigned to training sets and then from the Second and Fourth Affiliated Hospitals of Anhui Medical University were enrolled in validation sets. Based on independent risk factors for LNM in male and female EGC patients from the training sets, the nomograms were established respectively, which was also verified by internal validation from the training sets and external validation from the validation sets.

Results

Tumor size (odd ratio (OR): 1.386, p = 0.030), depth of invasion (OR: 0.306, p = 0.001), Lauren type (OR: 2.816, p = 0.000), lymphovascular invasion (LVI) (OR: 0.160, p = 0.000), and menopause (OR: 0.296, p = 0.009) were independent risk factors for female EGC patients. For male EGC patients, tumor size (OR: 1.298, p = 0.007), depth of invasion (OR: 0.257, p = 0.000), tumor location (OR: 0.659, p = 0.002), WHO type (OR: 1.419, p = 0.001), Lauren type (OR: 3.099, p = 0.000), and LVI (OR: 0.131, p = 0.000) were independent risk factors. Moreover, nomograms were established to predict the risk of LNM for female and male EGC patients, respectively. The area under the ROC curve of nomograms for female and male training sets were 87.7% (95% confidence interval (CI): 0.8397–0.914) and 94.8% (95% CI: 0.9273–0.9695), respectively. For the validation set, they were 92.4% (95% CI: 0.7979–1) and 93.4% (95% CI: 0.8928–0.9755), respectively. Additionally, the calibration curves showed good agreements between the bias-corrected prediction and the ideal reference line for both training sets and validation sets in female and male EGC patients.

Conclusions

Nomograms based on risk factors for LNM in male and female EGC patients may provide new insights into the selection of appropriate treatment methods.

Role of CT in the prediction of pathological complete response in gastric cancer after neoadjuvant chemotherapy

OBJECTIVE

To explore which computed tomography (CT) features can predict pathological complete response (pCR) (ypT0N0) after neoadjuvant chemotherapy (NAC) in patients with gastric adenocarcinoma (GC).

MATERIALS AND METHODS

This study reviewed an institutional database of patients who underwent resection of GC after NAC and identified patients with pCR from January 2010 to December 2013. The correlations between pre-chemotherapy and post-chemotherapy CT features and pCR were analyzed.

RESULTS

Eleven of 199 patients with GC who achieved ypT0N0 status after NAC were classified as the pCR group in this study. After matching pCR (n = 11) and non-pCR patients (n = 44) in the ratio of 1:4, a total of 55 cases were analyzed. The binary logistic regression analysis showed that the post-chemotherapy short diameter of the largest lymph node and tumor thickness ratio reduction were independent predictors of pCR, with an area under the curve (AUC) of 0.94 on the receiver operating characteristic (ROC) curve analysis.

CONCLUSION

Two CT features, including the short diameter of the largest lymph node post-chemotherapy and tumor thickness ratio reduction, are good predictors of pCR after NAC in patients with GC.

A CT-based Radiomics Model for Prediction of Lymph Node Metastasis in Early Stage Gastric Cancer

RATIONALE AND OBJECTIVES

To develop and validate a CT-based radiomics model for preoperative prediction of lymph node metastasis (LNM) in early stage gastric cancer (EGC).

MATERIALS AND METHODS

Four hundred and sixty-three consecutive EGC patients were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase CT scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator method. The predictive performance of radiomics signature was tested in the training and testing cohorts. Multivariate logistic regression analysis was conducted to build a radiomics-based model combined radiomics signature and lymph node status according to CT. Performance of the model was determined by its discrimination, calibration, and clinical usefulness.

RESULTS

The radiomics signature comprised six robust features showed significant association with LNM in both cohorts. A radiomics model that incorporated radiomics signature and CT-reported lymph node status showed good calibration and discrimination in the training cohort (AUC = 0.91) and testing cohort (AUC = 0.89). Decision curve analysis confirmed the clinical utility of this model.

CONCLUSION

The CT-based radiomics model showed favorable accuracy for prediction of LNM in EGC and may help to improve clinical decision-making.

A genomic-clinicopathologic Nomogram for the preoperative prediction of lymph node metastasis in gastric cancer

BACKGROUND

Preoperative evaluation of lymph node (LN) state is of pivotal significance for informing therapeutic decisions in gastric cancer (GC) patients. However, there are no non-invasive methods that can be used to preoperatively identify such status. We aimed at developing a genomic biosignature based model to predict the possibility of LN metastasis in GC patients.

METHODS

We used the RNA profile retrieving strategy and performed RNA expression profiling in a large GC cohort (GSE62254, n = 300) from Gene Expression Ominus (GEO). In the exploratory stage, 300 GC patients from GSE62254 were involved and the differentially expressed RNAs (DERs) for LN-status were determined using the R software. GC samples in GSE62254 were randomly allocated into a learning set (n = 210) and a verification set (n = 90). By using the Least absolute shrinkage and selection operator (LASSO) regression approach, a set of 23-RNA signatures were established and the signature based nomogram was subsequently built for distinguishing LN condition. The diagnostic efficiency, as well as the clinical performance of this model were assessed using the decision curve analysis (DCA). Metascape was used for bioinformatic analysis of the DERs.

RESULTS

Based on the genomic signature, we established a nomogram that robustly distinguished LN status in the learning (AUC = 0.916, 95% CI 0.833-0.999) and verification sets (AUC = 0.775, 95% CI 0.647-0.903). DCA demonstrated the clinical value of this nomogram. Functional enrichment analysis of the DERs was performed using bioinformatics methods which revealed that these DERs were involved in several lymphangiogenesis-correlated cascades.

CONCLUSIONS

In this study, we present a genomic signature based nomogram that integrates the 23-RNA biosignature based scores and Lauren classification. This model can be utilized to estimate the probability of LN metastasis with good performance in GC. The functional analysis of the DERs reveals the prospective biogenesis of LN metastasis in GC.

Prediction of Survival Outcomes Based on Preoperative Clinical Parameters in Gastric Cancer

BACKGROUND

Few current preoperative risk assessment tools provide essential, optimized treatment for gastric cancer. The purpose of this study was to develop and validate a nomogram that uses preoperative data to predict survival and risk assessments.

METHODS

A survival prediction model was constructed using data from a developmental cohort of 1251 patients with stage I to III gastric cancer who underwent curative resection between January 2005 and December 2008 at Ajou University Hospital, Korea. The model was internally validated for discrimination and calibrated using bootstrap resampling. To externally validate the model, data from a validation cohort of 2012 patients with stage I to III gastric cancer who underwent surgery at multiple centers in Korea between January 2001 and June 2006 were analyzed. Analyses included the model's discrimination index (C-index), calibration plots, and decision curve that predict overall survival.

RESULTS

Eight independent predictors, including age, sex, clinical tumor size, macroscopic features, body mass index, histology, clinical stages, and tumor location, were considered for developing the nomogram. The discrimination index was 0.816 (adjusted C-index) in the developmental cohort and 0.781 (adjusted C-index) in the external validation cohort. Additionally, in both the developmental and validation datasets, age and tumor size were significantly correlated with each other and were independent indicators for survival (P < 0.05).

CONCLUSIONS

We developed a new nomogram by using the most common and significant preoperative parameters that can help to identify high-risk patients before treatment and help clinicians to make appropriate decisions for patients with stage I to III gastric cancer.

Utility of the gastric window in computed tomography for differentiation of early gastric cancer (T1 stage) from muscularis involvement (T2 stage)

OBJECTIVE

To analyze the diagnostic value of using the gastric window in computed tomography for differentiation of early gastric cancer (T1 stage) from muscularis involvement (T2 stage).

MATERIALS AND METHODS

All patients with pathologically confirmed T1 stage and T2 stage gastric cancer and who underwent endoscopic resection or gastrectomy at our institution from January 2011 to November 2018 were examined. Each patient received an enhanced CT scan of the abdomen before the operation. T staging of tumors based on the CT scans was performed independently by two radiologists using the gastric window (width 150-200 HU, level 80-100 HU) and the abdominal window (width 350-400 HU, level 50 HU).

RESULTS

Use of the gastric window to diagnose stage T1 EGC led to an accuracy of 88.9% for observer1 and 91.5% for observer2; use of the abdominal window led to an accuracy of 53.6% for observer1 and 51.6% (38/106) for observer2. Use of the gastric window to diagnose stage T2 led to an accuracy of 85.6% for observer1 and 82.4% for observer2; use of the abdominal window led to an accuracy of 52.3% for both observer1 and observer2. For observer1, use of the gastric window had a diagnostic accuracy of 69.2% for stage T1a and 62.5% for stage T1b; for observer2, the diagnostic accuracy was 65.1% for stage T1a and 67.0% for stage T1b. A Kappa test indicated moderate and substantial inter-observer agreement for T staging with gastric window (κ = 0.598, P < 0.001) and abdominal window (κ = 0.745, P < 0.001).

CONCLUSION

Use of the gastric window in computed tomography provided more accurate staging for T1 and T2 stages of gastric cancer than the conventional abdominal window.

Computed Tomography Features of Gastric Cancer Patients With DNA Mismatch Repair Deficiency

Objective

To explore the computed tomography (CT) features of gastric cancer (GC) patients with DNA mismatch repair deficiency (dMMR).

Materials and Methods

This study reviewed the clinical and CT features of GC patients with dMMR, confirmed by the postoperative results, between September 2017 and December 2019. The expression pattern of MMR major proteins (MLH1, MSH2, MSH6, and PMS2) in immunohistochemistry was used to confirm the MMR status in GC tissues. The correlation between pre-treatment CT features and MMR status was statistically analyzed.

Results

A total of 28 patients with GC were diagnosed as dMMR in our study, and 49 patients were MMR-proficient (pMMR). The tumor locations were significantly different between the dMMR and pMMR groups (p = 0.006). The CT tumor thickness, CT long and short diameters of the largest lymph node, and the number of lymph nodes on CT of the dMMR group were significantly different from the pMMR group.

Conclusion

The dMMR GC exhibited a lower stomach location, smaller tumor thickness and lymph node diameter, and fewer lymph nodes on CT imaging.

Accuracy of 18F-FDG PET/CT and CECT for primary staging and diagnosis of recurrent gastric cancer: A meta-analysis

Contrast-enhanced computed tomography (CECT) is commonly used for staging and diagnosing recurrent gastric cancer. Recently, 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET)/CT gained popularity as a diagnostic tool owing to advantages including dual functional and anatomical imaging, which may facilitate early diagnosis. The diagnostic performance of 18F-FDG PET/CT and CECT has been assessed in several studies but with variable results. Therefore, the present meta-analysis aimed to evaluate the accuracy of 18F-FDG PET/CT and CECT for primary TNM staging and the diagnosis of recurrent gastric cancers. A systematic search of the PubMed Central, Medline, Scopus, Cochrane and Embase databases from inception until January 2020 was performed. The Quality Assessment of Diagnostic Accuracy Study-2 tool was used to determine the quality of the selected studies. Pooled estimates of sensitivity and specificity were calculated. A total of 58 studies comprising 9,997 patients were included. Most studies had a low risk of bias. The sensitivity and specificity for nodal staging of gastric cancer were 49% (95% CI, 37-61%) and 92% (95% CI, 86-96%) for 18F-FDG PET/CT, respectively, and 67% (95% CI, 57-76%) and 86% (95% CI, 81-89%) for CECT, respectively. For metastasis staging, the sensitivity and specificity were 56% (95% CI, 40-71%) and 97% (95% CI, 87-99%) for 18F-FDG PET/CT, respectively, and 59% (95% CI, 41-75%) and 96% (95% CI, 83-99%) for CECT, respectively. For diagnosing cancer recurrence, the pooled sensitivity and specificity were 81% (95% CI, 72-88%) and 83% (95% CI, 74-89%) for 18F-FDG PET/CT, respectively, and 59% (95% CI, 41-75%) and 96% (95% CI, 83-99%) for CECT, respectively. Both 18F-FDG PET/CT and CECT were deemed highly useful for diagnosing recurrent gastric cancer due to their high sensitivities and specificities. However, these techniques cannot be used to exclude or confirm the presence of lymph node metastases or recurrent gastric cancer tumors, but can be used for the confirmation of distal metastasis.

Prediction of the Depth of Tumor Invasion in Gastric Cancer: Potential Role of CT Radiomics

RATIONALE AND OBJECTIVES

The aim of this study was to investigate the value of computed tomography (CT) radiomics for the differentiation between T2 and T3/4 stage lesions in gastric cancer.

MATERIALS AND METHODS

A total of 244 consecutive patients with pathologically proven gastric cancer were retrospectively included and split into a training cohort (171 patients) and a test cohort (73 patients). Preoperative arterial phase and portal phase contrast enhanced CT images were retrieved for tumor segmentation and feature extraction by using a dedicated postprocessing software. The random forest method was used to build the classifier models.

RESULTS

The performance of single phase radiomics models were favorable in the differentiation between T2 and T3/4 stage tumors. Arterial phase-based radiomics model exhibited areas under the curve of 0.899 (95% CI: 0.812-0.955) in the training cohort and 0.825 (95% CI: 0.718-0.904) in the test cohort. Portal phase-based radiomics model showed areas under the curve of 0.843 (95% CI: 0.746-0.914) and 0.818 (95% CI: 0.711-0.899) in the training and test cohort, respectively.

CONCLUSION

CT radiomics approach has a potential role in differentiation between T2 and T3/4 stage tumors in gastric cancer.

A radiomics-based model for prediction of lymph node metastasis in gastric cancer

PURPOSE

To develop and validate a radiomics-based model for preoperative prediction of lymph node metastasis (LNM) in gastric cancer (GC).

METHOD

A total of 768 GC patients were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase computed tomography (CT) scans. A radiomics signature was built with highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method in the training cohort (n = 486). The signature was further validated in internal validation (n = 240) and external testing cohorts (n = 42). Multivariate logistic regression analysis was conducted to build a model that combined radiomics signature, serum biomarkers, and lymph node status according to CT. Performance of the model was determined by its discrimination, calibration, and clinical usefulness. The predictive value of the model was also evaluated in early stage GC (EGC) subgroup.

RESULTS

The radiomics signature comprised 7 robust features showed favorable prediction efficacy in all cohorts. A radiomics-based model that incorporated radiomics signature, serum CA72-4, and CT-reported lymph node status had good calibration and discrimination in training cohort [AUC, 0.92; 95% confidence interval (CI), 0.89-0.95] and validation cohort (AUC 0.86; 95% CI, 0.81-0.91). The model also showed a favorable predictive performance for EGC patients with an AUC of 0.85 (95% CI, 0.76-0.94). Decision curve analysis confirmed the clinical utility of this model.

CONCLUSIONS

The radiomics-based model showed favorable accuracy for prediction of LNM in GC. The model may also serve as a noninvasive tool for preoperative evaluation of LNM in EGC.

The role of MRI in the diagnosis and treatment of gastric cancer

Gastric cancer is the fifth most common malignancies and the third leading cause of cancer-related death worldwide, with more than 40% of new cases occurring in China. With the advancement of treatment methods, the application of adjuvant therapy and targeted drugs, the prognosis of patients with gastric cancer has been significantly improved. In recent years, more and more studies have reported that magnetic resonance imaging (MRI) showed great value in the clinical application among patients with gastric cancer, including preoperative staging, treatment response evaluation, predicting prognosis and histopathological features, treatment guidance, and molecular imaging. The remarkable research progress of MRI in gastric cancer will provide new evaluation and treatment approaches for clinical diagnosis and treatment. This article aims to review the current status of the application and research progress of MRI in patients with gastric cancer.

Feasibility of using two-dimensional axial computed tomography in pretreatment decision making for patients with early gastric cancer

Computed tomography (CT) is widely used in the pretreatment period of early gastric cancer (EGC). Only few studies have reported low accuracy of CT imaging for T and N staging in patients with EGC. However, owing to the limited number of studies, the value of CT imaging for EGC staging is not well known. Thus, we conducted a retrospective cross-sectional study regarding the associations among submucosal invasion, lymph node metastasis, and CT findings.The medical records of patients with EGC who had surgery or endoscopic resection were reviewed in a single center from January 2011 to December 2016. We evaluated the histological type, invasion depth, and lymph node (LN) metastasis on the basis of two-dimensional CT findings.We enrolled 1544 patients. Submucosal (SM) invasion was related to tumor size, histological type, and wall thickening or enhancement on CT images. Deep SM invasion (>500 μm) was also related to tumor size, poorly differentiated type, and abnormal CT findings (wall thickening, enhancement, and central depression). Among the patients with LN reactive positivity (0.5-1 cm), those who were female and had a tumor invasion of >1000 μm showed a higher prevalence of LN metastasis. The false-negative LN group had a higher prevalence of large tumors (>3 cm), poor differentiation, and SM invasion than the true-negative group.Wall thickening, enhancement, and central depression on CT images might be related to SM invasion. Patients with any positive CT findings needs more attention when performing ESD.

Performance of a machine learning-based decision model to help clinicians decide the extent of lymphadenectomy (D1 vs. D2) in gastric cancer before surgical resection

BACKGROUND

Controversy still exists on the optimal surgical resection for potentially curable gastric cancer (GC). Use of radiologic evaluation and machine learning algorithms might predict extent of lymphadenectomy to limit unnecessary surgical treatment. We purposed to design a machine learning-based clinical decision-support model for predicting extent of lymphadenectomy (D1 vs. D2) in local advanced GC.

METHODS

Clinicoradiologic features available from routine clinical assignments in 557 patients with GCs were retrospectively interpreted by an expert panel blinded to all histopathologic information. All patients underwent surgery using standard D2 resection. Decision models were developed with a logistic regression (LR), support vector machine (SVM) and auto-encoder (AE) algorithm in 371 training and tested in 186 test data, respectively. The primary end point was to measure diagnostic performance of decision model and a Japanese gastric cancer treatment guideline version 4th (JPN 4th) criteria for discriminate D1 (pT1 + pN0) versus D2 (≥ pT1 + ≥ pN1) lymphadenectomy.

RESULTS

The decision model with AE analysis produced highest area under ROC curve (train: 0.965, 95% confidence interval (CI) 0.948-0.978; test: 0.946, 95% CI 0.925-0.978), followed by SVM (train: 0.925, 95% CI 0.902-0.944; test: 0.942, 95% CI 0.922-0.973) and LR (train: 0.886, 95% CI 0.858-0.910; test: 0.891, 95% CI 0.891-0.952). By this improvement, overtreatment was reduced from 21.7% (121/557) by treat-all pattern, to 15.1% (84/557) by JPN 4th criteria, and to 0.7-0.9% (4-5/557) by the new approach.

CONCLUSIONS

The decision model with machine learning analysis demonstrates high accuracy for identifying patients who are candidates for D1 versus D2 resection. Its approximate 14-20% improvements in overtreatment compared to treat-all pattern and JPN 4th criteria potentially increase the number of patients with local advanced GCs who can safely avoid unnecessary lymphadenectomy.

CT radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer

PURPOSE

To investigate the role of computed tomography (CT) radiomics for the preoperative prediction of lymph node (LN) metastasis in gastric cancer.

MATERIALS AND METHODS

This retrospective study included 247 consecutive patients (training cohort, 197 patients; test cohort, 50 patients) with surgically proven gastric cancer. Dedicated radiomics prototype software was used to segment lesions on preoperative arterial phase (AP) CT images and extract features. A radiomics model was constructed to predict the LN metastasis by using a random forest (RF) algorithm. Finally, a nomogram was built incorporating the radiomics scores and selected clinical predictors. Receiver operating characteristic (ROC) curves were used to validate the capability of the radiomics model and nomogram on both the training and test cohorts.

RESULTS

The radiomics model showed a favorable discriminatory ability in the training cohort with an area under the curve (AUC) of 0.844 (95% CI, 0.759 to 0.909), which was confirmed in the test cohort with an AUC of 0.837 (95% CI, 0.705 to 0.926). The nomogram consisted of radiomics scores and the CT-reported LN status showed excellent discrimination in the training and test cohorts with AUCs of 0.886 (95% CI, 0.808 to 0.941) and 0.881 (95% CI, 0.759 to 0.956), respectively.

CONCLUSIONS

The CT-based radiomics nomogram holds promise for use as a noninvasive tool in the individual prediction of LN metastasis in gastric cancer.

KEY POINTS

• CT radiomics showed a favorable performance for the prediction of LN metastasis in gastric cancer. • Radiomics model outperformed the routine CT in predicting LN metastasis in gastric cancer. • The radiomics nomogram holds potential in the individualized prediction of LN metastasis in gastric cancer.

Preoperative N stage evaluation in advanced gastric cancer patients using multidetector CT: can the sum of the diameters of metastatic LNs be used for N stage evaluation?

AIM

To compare the diagnostic performance of total counts of metastatic lymph nodes (LN-sum) and conventional multidetector (MD) computed tomography (CT) staging in the nodal evaluation of advanced gastric cancer (AGC) patients.

MATERIALS AND METHODS

In total, 127 consecutive patients who underwent preoperative MDCT and gastrectomy for AGC were identified. Metastatic LNs on MDCT were defined as LNs with a short axis ≥8 mm, marked or heterogeneous enhancement, and morphological features (central necrosis, round shape, clustering). The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of the N-stage using LN-sum and conventional MDCT staging were generated and compared. In addition, metastatic LN counts between the MDCT and the histopathological examinations and correlation between LN-sum and histopathological nodal status were analysed.

RESULTS

The total counts of metastatic LNs on MDCT was significantly smaller than those detected in histopathological assessments (p<0.0001). LN-sum showed significant correlation with the pathological N stage and the number of metastatic LNs (rho=0.69, 0.73, p<0.0001). The areas under the receiver operating characteristic curve were 0.896, and 0.835, for N stage ≥N2 and N3, with cut-off values of 12.5 and 23.5 mm, respectively. LN-sum provided better diagnostic performance than conventional MDCT staging for discriminating N0-2 versus N3; sensitivity, accuracy, PPV and NPV of LN-sum were significantly higher (80.4 versus 52.2%, 81.1 versus 68.5%, 71.2 versus 57.1%, and 88 versus 74.1%).

CONCLUSION

LN-sum may be sufficiently useful in assessing the N3 stage of AGC and may help to plan appropriate therapy for AGC patients.

Contribution of DECT in detecting serosal invasion of gastric cancer

Background/aim

This study aimed to investigate the relationship between the iodine concentration (IC) of perigastric fat tissue as assessed by dual-energy computed tomography (DECT) and serosal invasion of gastric cancer.

Materials and methods

A total of 41 patients underwent preoperative staging evaluation for gastric cancer using DECT between July 2015 and March 2018. Patients were divided into 2 groups based on pathology results: serosal invasion (stage T4a) and intact serosa (stages T1–T3). Cutoff values, the diagnostic efficacy of IC in the perigastric fat tissue, and the perigastric fat tissue/tumor (P/T) ratio were determined.

Results

Among the 41 patients, 22 had stage T4a gastric cancer and 19 patients had gastric cancer with a stage lower than T4a. The mean IC of perigastric fat tissue and the P/T ratio were significantly higher in patients with serosal invasion than in those with intact serosa (P < 0.001). During the arterial phase, the area under the curve (AUC) was 0.915 and 0.854 for the IC of perigastric fat tissue and the P/T ratio, respectively. During the venous phase, the AUC was 0.890 and 0.876 for the IC of perigastric fat tissue and the P/T ratio, respectively.

Conclusion

The IC in the perigastric fat tissue seems to be a reliable indicator for serosal invasion of gastric cancer.

T and N Staging of Gastric Cancer Using Dual-Source Computed Tomography

Aim

This study is aimed at comparing gastric cancer T and N staging between virtual monochromatic energy images and fusion images generated by dual-source computed tomography (DSCT) dual-energy mode data acquisition prospectively while measuring the iodine concentration of gastric cancer and lymph nodes at different T and N stages from iodine map retrospectively.

Methods

A total of 71 patients (50 males and 21 females; mean age: 59 ± 11 years) confirmed with gastric cancer by endoscopic biopsy with no neoadjuvant chemotherapy were enrolled for the CT examination before surgeries. The preoperative T and N staging results were compared between groups with pathological results as the gold standard. The iodine concentrations of the gastric lesions and LNs were measured on the iodine-based material decomposition images. All iodine concentration values were normalized against those in the abdominal aorta and defined as normalized iodine concentration (nIC) values. The short axis length of LNs and nIC values were statistically analyzed.

Results

Group A was better than group B for T3 and T4 staging. No statistically significant difference in the overall accuracies for N staging was found between groups. For the late arterial and delayed phases, T3 and T4 nIC values of the extraserosal adipose tissue showed statistically significant differences. The nIC values between N0 and Nm (N1-N3) showed statistically significant differences in the portal phase only.

Conclusions

T3 and T4 nIC values of the extraserosal adipose tissue showed statistically significant differences. Hence, dual-source CT may be helpful in the differential diagnosis between T3 and T4.

Radiomics study for differentiating gastric cancer from gastric stromal tumor based on contrast-enhanced CT images

PURPOSE

To test the feasibility of differentiate gastric cancer from gastric stromal tumor using a radiomics study based on contrast-enhanced CT images.

MATERIALS AND METHODS

The contrast-enhanced CT image data of 60 patients with gastric cancer and 40 patients with gastric stromal tumor confirmed by postoperative pathology were retrospectively analyzed. First, CT images were read by two senior radiologists to acquire subjective CT signs model, including perigastric fatty infiltration, perigastric enlarged lymph nodes, the enhancement and growth modes of gastric tumors. Second, the manual segmentation of gastric tumors from the CT images was performed by the two radiologists to extract radiomics features via ITK-SNAP software, and to construct radiomics signature model. Finally, a diagnostic model integrated with subjective CT signs and radiomics signatures was constructed. The diagnostic efficacy of three models in differentiating gastric cancer from gastric stromal tumor was compared by using receiver operating characteristic curves (ROC).

RESULTS

There are statistically significant differences between the gastric cancer and gastric stromal tumor in the perigastric enlarged lymph nodes, growth mode and radiomics signature (p < 0.05). The area under ROC curve (AUC), sensitivity and accuracy of subjective CT signs model were the lowest among the three models. While the combined model yields the highest AUC value (0.903), specificity (93.33%) and accuracy (86.00%) among the three models (p = 0.03).

CONCLUSION

The diagnostic model integrating subjective CT signs and radiomics signature can improve the diagnostic accuracy of gastric tumors.

Computed tomography with a stomach protocol and virtual gastroscopy in the staging of gastric cancer: an initial experience

Objective

To evaluate the accuracy of multidetector computed tomography with a stomach protocol in staging of gastric cancer.

Materials and Methods

We evaluated 14 patients who underwent computed tomography in a 16-channel scanner for preoperative staging of gastric adenocarcinoma between September 2015 and December 2016. All images were analyzed by the same radiologist, who had extensive experience in abdominal cancer imaging. The sensitivity, specificity, and accuracy of the method were calculated by comparing it with the pathology result. All patients underwent partial or total gastrectomy.

Results

The mean age was 61.5 years, and 53.8% of the patients were male. The gastric lesions were classified as T1/T2 in 35.7% of the cases, as T3 in 28.5%, and as T4 in 35.7%. Eleven patients (68.7%) had suspicious (N positive) lymph nodes. The accuracy of the T1/T2, T3, T4, and lymph node staging tests was 85%, 78%, 90%, and 78%, respectively. The respective sensitivity and specificity values were 71% and 100% for T1/T2, 66% and 81% for T3, 100% and 90% for T4, and 88% and 60% for lymph nodes.

Conclusion

Multidetector computed tomography with a stomach protocol, used in conjunction with virtual gastroscopy, shows good accuracy in the tumor and lymph node staging of gastric adenocarcinoma.

CT volumetry for gastric adenocarcinoma: association with lymphovascular invasion and T-stages

Purpose

To determine whether gross tumor volume of resectable gastric adenocarcinoma on multidetector computed tomography could predict presence of lymphovascular invasion and T-stages.

Results

Gross tumor volume increased with the lymphovascular invasion (r = 0.426, P < 0.0001) and T stage (r = 0.656, P < 0.0001). Univariate analysis showed gross tumor volume could predict lymphovascular invasion (P < 0.0001). Multivariate analyses indicated gross tumor volume as an independent risk factor of lymphovascular invasion (P = 0.026, odds ratio = 2.284). The Mann-Whitney U test showed gross tumor volume could distinguish T2 from T3, T1 from T2–T4a, T1–T2 from T3–T4a and T1–T3 from T4a (P = 0.000). In the development cohort, gross tumor volume could predict lymphovascular invasion (cutoff, 15.92 cm³; AUC, 0.760), and distinguish T2 from T3 (cutoff, 10.09 cm³; AUC, 0.828), T1 from T2-T4a (cutoff, 8.20 cm³; AUC, 0.860), T1-T2 from T3-T4a (cutoff, 15.88 cm³; AUC, 0.883), and T1-T3 from T4a (cutoff, 21.53 cm³; AUC, 0.834). In validation cohort, gross tumor volume could predict presence of lymphovascular invasion (AUC, 0.742), and distinguish T2 from T3 (AUC, 0.861), T1 from T2-T4a (AUC, 0.859), T1–T2 from T3–T4a (AUC, 0.875), and T1–T3 from T4a (AUC, 0.773).

Materials and Methods

360 consecutive patients with gastric adenocarcinoma were retrospectively identified. Gross tumor volume was evaluated on multidetector computed tomography images. Statistical analysis was performed to determine whether gross tumor volume could predict presence of lymphovascular invasion and T-stages. Cutoffs of gross tumor volume were first investigated in 212 patients and then validated in an independent 148 patients using area under the receiver operating characteristic curve (AUC) for predicting lymphovascular invasion and T-stages.

Conclusions

Gross tumor volume of resectable gastric adenocarcinoma at multidetector computed tomography demonstrated capability in predicting lymphovascular invasion and distinguishing T-stages.

Comparison between CT Net enhancement and PET/CT SUV for N staging of gastric cancer: A case series

BACKGROUND

The therapeutic approach of gastric cancer strictly depends on TNM staging mainly provided by CT and PET/CT. However, the lymph node size criterion as detected by MDCT causes a poor differential diagnosis between reactive and metastatic enlarged lymph nodes with low specificity values. Our study aims to compare 320-row CT Net enhancement and fluorine-18 fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography (F-FDG PET/CT) SUV for N staging of gastric cancer.

MATERIALS AND METHODS

45 patients with histologically proven gastric cancer underwent CT and F-FDG PET/CT. Two radiologists in consensus evaluated all images and calculated the CT Net enhancement and F-FDG PET/CT SUV for N staging, having the histological findings as the reference standard. CT and F-FDG PET/CT sensitivity, specificity, diagnostic accuracy, positive and negative predictive values (PPV and NPV) were evaluated and compared by using the Mc Nemar test.

RESULTS

The histological examination revealed nodal metastases in 29/45 cases (64%). CT Net enhancement obtained sensitivity, specificity, accuracy, PPV and NPV of 90%, 81%, 87%, 90% and 81%, respectively. F-FDG PET/CT SUV obtained sensitivity, specificity, accuracy, PPV and NPV of 66%, 88%, 73%, 90% and 58%, respectively. No statistically significant difference between the two imaging modalities was found (p = 0.1).

CONCLUSION

CT Net enhancement represents an accurate tool for N staging of gastric cancer and could be considered as the CT corresponding quantitative parameter of F-FDG PET/CT SUV. It could be applied in the clinical practice for differentiating reactive lymph nodes from metastatic ones improving accuracy and specificity of CT.

Value and impact factors of multidetector computed tomography in diagnosis of preoperative lymph node metastasis in gastric cancer: A PRISMA-compliant systematic review and meta-analysis

BACKGROUND

Multidetector computed tomography (MDCT) exhibited wide ranges of sensitivities and specificities for lymph node assessment of gastric cancer (GC) in several individual studies. This present meta-analysis was carried out to evaluate the value of MDCT in diagnosis of preoperative lymph node metastasis (LNM) and to explore the impact factors that might explain the heterogeneity of its diagnostic accuracy in GC.

METHODS

A comprehensive search was conducted to collect all the relevant studies about the value of MDCT in assessing LNM of GC within the PubMed, Cochrane library and Embase databases up to Feb 2, 2016. Two investigators independently screened the studies, extracted data, and evaluated the quality of included studies. The sensitivity, specificity, and area under ROC curve (AUC) were pooled to estimate the overall accuracy of MDCT. Meta-regression and subgroup analysis were carried out to identify the possible factors influencing the heterogeneity of the accuracy.

RESULTS

A total of 27 studies with 6519 subjects were finally included. Overall, the pooled sensitivity, specificity, and AUC were 0.67 (95% CI: 0.56-0.77), 0.86 (95% CI: 0.81-0.90), and 0.86 (95% CI: 0.83-0.89), respectively. Meta-regression revealed that MDCT section thickness, proportion of serosal invasion, and publication year were the main significant impact factors in sensitivity, and MDCT section thickness, multiplanar reformation (MPR), and reference standard were the main significant impact factors in specificity. After the included studies were divided into 2 groups (Group A: studies with proportion of serosa-invasive GC subjects ≥50%; Group B: studies with proportion of serosa-invasive GC subjects <50%), the pooled sensitivity in Group A was significantly higher than in Group B (0.84 [95% CI: 0.75-0.90] vs 0.55 [95% CI: 0.41-0.68], P < .01). For early gastric cancer (EGC), the pooled sensitivity, specificity, and AUC were 0.34 (95% CI: 0.15-0.61), 0.91 (95% CI: 0.84-0.95), and 0.83 (95% CI: 0.80-0.86), respectively.

CONCLUSION

To summarize, MDCT tends to be adequate to assess preoperative LNM in serosa-invasive GC, but insufficient for non-serosa-invasive GC (particularly for EGC) owing to its low sensitivity. Proportion of serosa-invasive GC subjects, MDCT section thickness, MPR, and reference standard are the main factors influencing its diagnostic accuracy.

Consideration of clinicopathologic features improves patient stratification for multimodal treatment of gastric cancer

Preoperative staging of gastric cancer with computed tomography alone exhibits poor diagnostic accuracy, which may lead to improper treatment decisions. We developed novel patient stratification criteria to select appropriate treatments for gastric cancer patients based on preoperative staging and clinicopathologic features. A total of 5352 consecutive patients who underwent gastrectomy for gastric cancer were evaluated. Preoperative stages were determined according to depth of invasion and nodal involvement on computed tomography. Logistic regression analysis was used to identify clinicopathological factors associated with the likelihood of proper patient stratification. The diagnostic accuracies of computed tomography scans for depth of invasion and nodal involvement were 67.1% and 74.1%, respectively. Among clinicopathologic factors, differentiated tumor histology, tumors smaller than 5 cm, and gross appearance of early gastric cancer on endoscopy were shown to be related to a more advanced stage of disease on preoperative computed tomography imaging than actual pathological stage. Additional consideration of undifferentiated histology, tumors larger than 5 cm, and grossly advanced gastric cancer on endoscopy increased the probability of selecting appropriate treatment from 75.5% to 94.4%. The addition of histology, tumor size, and endoscopic findings to preoperative staging improves patient stratification for more appropriate treatment of gastric cancer.

CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma

PURPOSE

To evaluate the value of CT-based radiomics signature for differentiating Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL).

MATERIALS AND METHODS

40 patients with Borrmann type IV GC and 30 patients with PGL were retrospectively recruited. 485 radiomics features were extracted and selected from the portal venous CT images to build a radiomics signature. Subjective CT findings, including gastric wall peristalsis, perigastric fat infiltration, lymphadenopathy below the renal hila and enhancement pattern, were assessed to construct a subjective findings model. The radiomics signature, subjective CT findings, age and gender were integrated into a combined model by multivariate analysis. The diagnostic performance of these three models was assessed with receiver operating characteristics curves (ROC) and were compared using DeLong test.

RESULTS

The subjective findings model, the radiomics signature and the combined model showed a diagnostic accuracy of 81.43% (AUC [area under the curve], 0.806; 95% CI [confidence interval]: 0.696-0.917; sensitivity, 63.33%; specificity, 95.00%), 84.29% (AUC, 0.886 [95% CI: 0.809-0.963]; sensitivity, 86.67%; specificity, 82.50%), 87.14% (AUC, 0.903 [95%CI: 0.831-0.975]; sensitivity, 70.00%; specificity, 100%), respectively. There were no significant differences in AUC among these three models (P=0.051-0.422).

CONCLUSION

Radiomics analysis has the potential to accurately differentiate Borrmann type IV GC from PGL.

MDCT imaging of the stomach: advances and applications

The stomach may be involved by a myriad of pathologies ranging from benign aetiologies like inflammation to malignant aetiologies like carcinoma or lymphoma. Multidetector CT (MDCT) of the stomach is the first-line imaging for patients with suspected gastric pathologies. Conventionally, CT imaging had the advantage of simultaneous detection of the mural and extramural disease extent, but advances in MDCT have allowed mucosal assessment by virtual endoscopy (VE). Also, better three-dimensional (3D) post-processing techniques have enabled more robust and accurate pre-operative planning in patients undergoing gastrectomy and even predict the response to surgery for patients undergoing laparoscopic sleeve gastrectomy for weight loss. The ability of CT to obtain stomach volume (for bariatric surgery patients) and 3D VE images depends on various patient and protocol factors that are important for a radiologist to understand. We review the appropriate CT imaging protocol in the patients with suspected gastric pathologies and highlight the imaging pearls of various gastric pathologies on CT and VE.

Impact of the Cross-Sectional Location of Multidetector Computed Tomography Scans on Prediction of Serosal Exposure in Patients with Advanced Gastric Cancer

BACKGROUND

The extent of serosal exposure varies depending on the cross-section of the stomach that is viewed, affected by the visceral peritoneum of the omentum. Although multidetector computed tomography (MDCT) is the most useful method to predict serosal exposure, the MDCT criteria for such exposure by cross-sectional location remain to be established.

METHODS

The MDCT of gastric cancer patients who underwent surgery, and for whom pathological reports were available, were reviewed by radiologists. The MDCT criteria for invasion depth were divided into five grades: (1) smooth margin; (2) undulating margin; (3) streaky margin within vessels; (4) nodular margin within perigastric vessels; and (5) streaky or nodular margin over the perigastric vessels. The five grades were compared in terms of pathological tumor depth by curvature and wall group.

RESULTS

A total of 125 patients of stage ≥ T2 were enrolled. The five MDCT grades correlated with tumor depth (P < 0.001). Exposed serosal lesions of grade 3 (P = 0.031) and 5 (P = 0.030) constituted significantly the largest proportion of wall and curvature cancers, respectively. The accuracy of MDCT in terms of T staging using the five grades was calculated by cross-sectional location. The highest accuracies were associated with curvature- and wall-located tumors (55.1 and 64.3%, respectively) when serosal exposure was graded 5 and 3, respectively. The highest overall accuracy for T staging was 59.2% when the various MDCT criteria were applied by reference to the cross-sectional location.

CONCLUSIONS

The MDCT criteria for serosal exposure vary by the cross-sectional location of the gastric cancer.