How useful is preoperative imaging for tumor, node, metastasis (TNM) staging of gastric cancer? A meta-analysis

The value of deep learning and radiomics models in predicting preoperative serosal invasion in gastric cancer: a dual-center study

PURPOSE

To establish and validate a model based on deep learning (DL), integrating radiomic features with relevant clinical features to generate nomogram, for predicting preoperative serosal invasion in gastric cancer (GC).

METHODS

This retrospective study included 335 patients from dual centers. T staging (T1-3 or T4) was used to assess serosal invasion. Radiomic features were extracted from primary GC lesions in the venous phase CT, and DL features from 8 transfer learning models were combined to create the Hand-crafted Radiomics and Deep Learning Radiomics (HCR-DLR) model. The Clinical (CL) model was built using clinical features, and both were combined into the Clinical and Radiomics Combined (CRC) model. In total, 15 predictive models were developed using 5 machine learning algorithms. The best-performing models were visualized as nomograms.

RESULTS

The total of 14 radiomic features, 13 DL features, and 2 clinical features were considered valuable through dimensionality reduction and selection. Among the constructed models: CRC model (AUC, training cohort: 0.9212; internal test cohort: 0.8743; external test cohort: 0.8853) than HCR-DLR model (AUC, training cohort: 0.8607; internal test cohort: 0.8543; external test cohort: 0.8824) and CL model (AUC, training cohort: 0.7632; internal test cohort: 0.7219; external test cohort: 0.7294) showed better performance. A nomogram based on the logistic CL model was drawn to facilitate the usage and showed its excellent predictive performance.

CONCLUSION

The predictive performance of the CRC Model, which integrates clinical features, radiomic features, and DL features, exhibits robust predictive capability and can serve as a simple, non-invasive, and practical tool for predicting the serosal invasion status of GC.

Influence of visceral adipose tissue on the accuracy of tumor T-staging of gastric cancer in preoperative CT

OBJECTIVES

To evaluate the impact of the visceral adipose tissue (VAT) area and density on the accuracy of tumor T-staging of gastric cancer in preoperative computed tomography (CT).

METHODS

This study included 136 patients with gastric cancer in our research center from January 2021 to June 2022. The patients were divided into two groups based on their postoperative pathological results: accurate-staging (matched T-staging evaluated by preoperative CT and postoperative pathology) and inaccurate-staging (unmatched T-staging evaluated by preoperative CT and postoperative pathology) groups. Preoperative CT was performed to assess the VAT area and density, and logistic regression was employed to evaluate the effect of VAT on the accuracy of preoperative-CT-evaluated T-staging of patients with gastric cancer.

RESULTS

The accurate-staging group had a significantly higher VAT area (134.64 ± 70.55 cm2 vs 95.44 ± 66.18 cm2, P = 0.003) and significantly lower VAT density (-95.05 ± 12.28 Hounsfield Units [HU] vs - 89.68 ± 13.26 HU, P = 0.027) than the inaccurate-staging group. A low VAT area (P = 0.002) and tumor located in the upper stomach (P = 0.019) were significantly associated with and were independent risk factors for the error of CT-evaluated T-staging. Compared to a VAT area ≥ 81.04 cm2, which was used as a reference, the odds ratio (OR) of a VAT area < 81.04 cm2 for the probability of T-staging mis-assessment was 4.455 (95% confidence interval [CI]: 1.728-11.485).

CONCLUSIONS

A low VAT area in patients with gastric cancer had adverse effects on preoperative CT-evaluated T-staging.

Multi-cohort study in gastric cancer to develop CT-based radiomic models to predict pathological response to neoadjuvant immunotherapy

BACKGROUND

Neoadjuvant immunotherapy has been shown to improve survival in patients with gastric cancer. This study sought to develop and validate a radiomics-based machine learning (ML) model for patients with locally advanced gastric cancer (LAGC), specifically to predict whether patients will achieve a major pathological response (MPR) following neoadjuvant immunotherapy. With its predictive capabilities, this tool shows promise for enhancing clinical decision-making processes in the future.

METHODS

This study utilized a multicenter cohort design, retrospectively gathering clinical data and computed tomography (CT) images from 268 patients diagnosed with advanced gastric cancer who underwent neoadjuvant immunotherapy between January 2019 and December 2023 from two medical centers. Radiomic features were extracted from CT images, and a multi-step feature selection procedure was applied to identify the top 20 representative features. Nine ML algorithms were implemented to build prediction models, with the optimal algorithm selected for the final prediction model. The hyperparameters of the chosen model were fine-tuned using Bayesian optimization and grid search. The performance of the model was evaluated using several metrics, including the area under the curve (AUC), accuracy, and Cohen's kappa coefficient.

RESULTS

Three cohorts were included in this study: the development cohort (DC, n = 86), the internal validation cohort (IVC, n = 59), and the external validation cohort (EVC, n = 52). Nine ML models were developed using DC cases. Among these, an optimized Bayesian-LightGBM model, demonstrated robust predictive performance for MPR following neoadjuvant immunotherapy in LAGC patients across all cohorts. Specifically, within DC, the LightGBM model attained an AUC of 0.828, an overall accuracy of 0.791, a Cohen's kappa coefficient of 0.552, a sensitivity of 0.742, a specificity of 0.818, a positive predictive value (PPV) of 0.586, a negative predictive value (NPV) of 0.867, a Matthews correlation coefficient (MCC) of 0.473, and a balanced accuracy of 0.780. Comparable performance metrics were validated in both the IVC and the EVC, with AUC values of 0.777 and 0.714, and overall accuracies of 0.729 and 0.654, respectively. These results suggested good fitness and generalization of the Bayesian-LightGBM model. Shapley Additive Explanations (SHAP) analysis identified significant radiomic features contributing to the model's predictive capability. The SHAP values of the features wavelet.LLH_gldm_SmallDependenceLowGrayLevelEmphasis, wavelet.HHL_glrlm_RunVariance, and wavelet.LLH_glszm_LargeAreaHighGrayLevelEmphasis were ranked among the top three, highlighting their significant contribution to the model's predictive performance. In contrast to existing radiomic models that exclusively focus on neoadjuvant chemotherapy, our model integrates both neoadjuvant immunotherapy and chemotherapy, thereby offering more precise predictive capabilities.

CONCLUSION

The radiomics-based ML model demonstrated significant efficacy in predicting the pathological response to neoadjuvant immunotherapy in LAGC patients, thereby providing a foundation for personalized treatment strategies.

Comprehensive evaluation of tumor response better evaluates the efficacy of neoadjuvant chemotherapy and predicts the prognosis in gastric cancer - a post hoc analysis of a single-center randomized controlled trial

BACKGROUND

Perioperative chemotherapy combined with D2 radical gastrectomy has been proven to be the standard treatment for local advanced gastric cancer. However, tumor regression grading (TRG) is the only neoadjuvant chemotherapy (NACT) response evaluation criterion recommended by the NCCN guideline for gastric cancer (GC). Given TRG's limitations, we aim to explore a better comprehensive response evaluation method in this study.

METHODS

Clinical information of 96 GC patients who received NACT was collected prospectively. Clinicopathological variables predictive of the response to NACT were identified by comparing the pre- and post-NACT examination results. The correlations between the response mode and long-term survival rate were assessed.

RESULTS

Univariate Cox regression analysis showed that CT-based evaluation of the primary lesion thickness (CT-thickness) and tumor markers (TMs) were significantly associated with prognosis. The comprehensive evaluation method, including CT-thickness, TRG, and TMs, was constructed and proved to have a higher Harrell's C index. Significant differences in overall survival (OS) and recurrence-free survival (RFS) were observed between responders and non-responders distinguished by the comprehensive evaluation method.

CONCLUSIONS

The combination of CT-thickness, TRG, and TMs could be used to construct a pragmatic NACT efficacy evaluation method with both high sensitivity and specificity, which could facilitate clinical decision-making, NACT-related clinical research conduction, and efficacy predictive biomarker exploration.

Node Reporting and Data System Combined With Computed Tomography Radiomics Can Improve the Prediction of Nonenlarged Lymph Node Metastasis in Gastric Cancer

OBJECTIVES

To investigate the diagnostic performance of Node Reporting and Data System (Node-RADS) combined with computed tomography (CT) radiomics for assessing nonenlargement regional lymph nodes in gastric cancer (GC).

METHODS

Preoperative CT images were retrospectively collected from 376 pathologically confirmed of gastric adenocarcinoma from January 2019 to December 2023, with 605 lymph nodes included for analysis. They were divided into training (n = 362) and validation (n = 243) sets. Radiomics features were extracted from venous-phase, and the radiomics score was obtained. Clinical information, CT parameters, and Node-RADS classification were collected. A combined model was built using machine-learning approach and tested in validation set using receiver operating characteristic curve analysis. Further validation was conducted in different subgroups of lymph node short-axis diameter (SD) range.

RESULTS

Node-RADS score, SD, maximum diameter of thickness of tumor, and radiomics were identified as the most predictive factors. The results demonstrated that the integrated model combining SD, maximum diameter of thickness of tumor, Node-RADS, and radiomics outperformed the model excluding radiomics, yielding an area under the receiver operating characteristic curve of 0.82 compared with 0.79, with a statistically significant difference ( P  < 0.001). Subgroup analysis based on different SDs of lymph nodes also revealed enhanced diagnostic accuracy when incorporating the radiomics score for the 4- to 7.9-mm subgroups, all P  < 0.05. However, for the 8- to 9.9-mm subgroup, the combination of the radiomics did not significantly improve the prediction, with an area under the receiver operating characteristic curve of 0.85 versus 0.85, P  = 0.877.

CONCLUSION

The integration of radiomics scores with Node-RADS assessments significantly enhances the accuracy of lymph node metastasis evaluation for GC. This combined model is particularly effective for lymph nodes with smaller standard deviations, yielding a marked improvement in diagnostic precision.

CLINICAL RELEVANCE STATEMENT

The findings of this study indicate that a composite model, which incorporates Node-RADS, radiomics features, and conventional parameters, may serve as an effective method for the assessment of nonenlarged lymph nodes in GC.

Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers

OBJECTIVE

The objective of this study is to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning model. This approach is intended to address the limitations of manual segmentation, which is known to be susceptible to inter-observer variability. Subsequently, a prediction model of gastric cancer (GC) serosal invasion was constructed in conjunction with radiomics and deep learning features, and a nomogram was generated to explore the clinical guiding significance.

METHODS

This study enrolled 311 patients from two centers with pathologically confirmed of GC. we employed a deep learning model, U-Mamba, to obtain fully automatic segmentation of the spleen CT images. Subsequently, radiomics features and deep learning features were extracted from the entire spleen CT images, and significant features were identified through dimensionality reduction. The clinical features, radiomic features, and deep learning features were organized and integrated, and five machine learning methods were employed to develop 15 predictive models. Ultimately, the model exhibiting superior performance was presented in the form of a nomogram.

RESULTS

A total of 18 radiomics features, 30 deep learning features, and 1 clinical features were deemed valuable. The DLRA model demonstrated superior discriminative capacity relative to other models. A nomogram was constructed based on the logistic clinical model to facilitate the usage and verification of the clinical model.

CONCLUSION

Radiomics and deep learning features derived from automated spleen segmentation to construct a nomogram demonstrate efficacy in predicting serosal invasion in GC. Concurrently, fully automated segmentation provides a novel and reproducible approach for radiomics research.

Development and External Validation of a CT-Based Radiomics Nomogram to Predict Perineural Invasion and Survival in Gastric Cancer: A Multi-institutional Study

RATIONALE AND OBJECTIVES

To develop and validate a radiomics nomogram utilizing CT data for predicting perineural invasion (PNI) and survival in gastric cancer (GC) patients.

MATERIALS AND METHODS

A retrospective analysis of 408 GC patients from two institutions: 288 patients from Institution I were divided 7:3 into a training set (n = 203) and a testing set (n = 85); 120 patients from Institution II served as an external validation set. Radiomics features were extracted and screened from CT images. Independent radiomics, clinical, and combined models were constructed to predict PNI. Model discrimination, calibration, clinical utility, and prognostic significance were evaluated using area under the curve (AUC), calibration curves, decision curves analysis, and Kaplan-Meier curves, respectively.

RESULTS

15 radiomics features and three clinical factors were included in the final analysis. The AUCs of the radiomics model in the training, testing, and external validation sets were 0.843 (95% CI: 0.788-0.897), 0.831 (95% CI: 0.741-0.920), and 0.802 (95% CI: 0.722-0.882), respectively. A nomogram was developed by integrating significant clinical factors with radiomics features. The AUCs of the nomogram in the training, testing, and external validation sets were 0.872 (95% CI: 0.823-0.921), 0.862 (95% CI: 0.780-0.944), and 0.837 (95% CI: 0.767-0.908), respectively. Survival analysis revealed that the nomogram could effectively stratify patients for recurrence-free survival (Hazard Ratio: 4.329; 95% CI: 3.159-5.934; P < 0.001).

CONCLUSION

The radiomics-derived nomogram presented a promising tool for predicting PNI in GC and held significant prognostic implications.

IMPORTANT FINDINGS

The nomogram functioned as a non-invasive biomarker for determining the PNI status. The predictive performance of the nomogram surpassed that of the clinical model (P < 0.05). Furthermore, patients in the high-risk group stratified by the nomogram had a significantly shorter RFS (P < 0.05).

Clinical application of oral contrast-enhanced ultrasound in evaluating the preoperative T staging of gastric cancer

BACKGROUND

Oral contrast-enhanced ultrasound (OCEUS) is widely used in the noninvasive diagnosis and screening of gastric cancer (GC) in China.

AIM

To investigate the clinical application of OCEUS in evaluating the preoperative T staging of gastric cancer.

METHODS

OCEUS was performed before the operation, and standard ultrasound images were retained. The depth of infiltration of GC (T-stage) was evaluated according to the American Joint Committee on Cancer 8th edition of the tumor-node-metastasis staging criteria. Finally, with postoperative pathological staging as the gold standard reference, the sensitivity, specificity, negative predictive value, positive predictive value, and diagnostic value of OCEUS T staging were evaluated.

RESULTS

OCEUS achieved diagnostic accuracy rates of 76.6% (T1a), 69.6% (T1b), 62.7% (T2), 60.8% (T3), 88.0% (T4a), and 88.7% (T4b), with an average of 75.5%. Ultrasonic T staging sensitivity exceeded 62%, aside from T1b at 40.3%, while specificity was over 91%, except for T3 with 83.5%. The Youden index was above 60%, with T1b and T2 being exceptions. OCEUS T staging corresponded closely with pathology in T4b (kappa > 0.75) and moderately in T1a, T1b, T2, T3, and T4a (kappa 0.40-0.75), registering a concordance rate exceeding 84%.

CONCLUSION

OCEUS was effective, reliable, and accurate in diagnosing the preoperative T staging of GC. As a noninvasive diagnostic technique, OCEUS merits clinical popularization.

A quantitative model using multi-parameters in dual-energy CT to preoperatively predict serosal invasion in locally advanced gastric cancer

OBJECTIVES

To develop and validate a quantitative model for predicting serosal invasion based on multi-parameters in preoperative dual-energy CT (DECT).

MATERIALS AND METHODS

A total of 342 LAGC patients who underwent gastrectomy and DECT from six centers were divided into one training cohort (TC), and two validation cohorts (VCs). Dual-phase enhanced DECT-derived iodine concentration (IC), water concentration, and monochromatic attenuation of lesions, along with clinical information, were measured and collected. The independent predictors among these characteristics for serosal invasion were screened with Spearman correlation analysis and logistic regression (LR) analysis. A quantitative model was developed based on LR classifier with fivefold cross-validation for predicting the serosal invasion in LAGC. We comprehensively tested the model and investigated its value in survival analysis.

RESULTS

A quantitative model was established using IC, 70 keV, 100 keV monochromatic attenuations in the venous phase, and CT-reported T4a, which were independent predictors of serosal invasion. The proposed model had the area-under-the-curve (AUC) values of 0.889 for TC and 0.860 and 0.837 for VCs. Subgroup analysis showed that the model could well discriminate T3 from T4a groups, and T2 from T4a groups in all cohorts (all p < 0.001). Besides, disease-free survival (DFS) (TC, p = 0.015; and VC1, p = 0.043) could be stratified using this quantitative model.

CONCLUSION

The proposed quantitative model using multi-parameters in DECT accurately predicts serosal invasion for LAGC and showed a significant correlation with the DFS of patients.

CRITICAL RELEVANCE STATEMENT

This quantitative model from dual-energy CT is a useful tool for predicting the serosal invasion of locally advanced gastric cancer.

KEY POINTS

Serosal invasion is a poor prognostic factor in locally advanced gastric cancer that may be predicted by DECT. DECT quantitative model for predicting serosal invasion was significantly and positively correlated with pathologic T stages. This quantitative model was associated with patient postoperative disease-free survival.

Value of orthogonal axial MR images in preoperative T staging of gastric cancer

PURPOSE

To assess the value of orthogonal axial images (OAI) of MRI in gastric cancer T staging.

METHODS

This retrospective study enrolled 133 patients (median age, 63 [range, 24-85] years) with gastric adenocarcinoma who underwent both CT and MRI followed by surgery. MRI lacking or incorporating OAI and CT images were evaluated, respectively. Diagnostic performance (accuracy, sensitivity, and specificity) for each T stage, overall diagnostic accuracy and rates of over- and understaging were quantified employing pathological T stage as a reference standard. The McNemar's test was performed to compare the overall accuracy.

RESULTS

Among patients with pT1-pT4 disease, MRI with OAI (accuracy: 88.7-94.7%, sensitivity: 66.7-93.0%, specificity: 91.5-100.0%) exhibited superior diagnostic performance compared to MRI without OAI (accuracy: 81.2-88.7%, sensitivity: 46.2-83.1%, specificity: 85.5-99.1%) and CT (accuracy: 88.0-92.5%, sensitivity: 53.3-90.1%, specificity: 88.7-98.1%). The overall accuracy of MRI with OAI was significantly higher (83.5%) than that of MRI without OAI (67.7%) (p < .001). However, there was no significant difference in the overall accuracy of MRI with OAI and CT (78.9%) (p = .35). The over- and understaging rates of MRI with OAI (12.0, 4.5%) were lower than those of MRI without OAI (21.8, 10.5%) and CT (12.8, 8.3%).

CONCLUSION

OAI play a pivotal role in the T staging of gastric cancer. MRI incorporating OAI demonstrated commendable performance for gastric cancer T-staging, with a slight tendency toward its superiority over CT.

Perioperative chemotherapy with nivolumab for HER2-negative locally advanced gastric cancer: a case series

BACKGROUND

Nivolumab with chemotherapy has been transformative for metastatic gastric cancer (GC). The potential of this regimen for local tumor control could be utilized for perioperative chemotherapy in locally advanced GC with bulky tumors or lymph node metastasis involving other organs.

CASE PRESENTATION

Five patients with HER2-negative advanced GC were treated with nivolumab and oxaliplatin-based chemotherapy. All patients presented with clinical stage III or IVA GC with tumors in contact with either the pancreas or liver. Following chemotherapy, all tumors demonstrated shrinkage, allowing successful radical gastrectomies including four minimally invasive approach without postoperative complications. Four patients avoided combined resection of other organs.

CONCLUSIONS

Perioperative chemotherapy with nivolumab was effective for local disease control in this case series. This regimen could be a promising treatment approach for locally advanced GC; however, its survival benefits should be evaluated in clinical trials.

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.

Interpretable deep learning model to predict lymph node metastasis in early gastric cancer using whole slide images

In early gastric cancer (EGC), the presence of lymph node metastasis (LNM) is a crucial factor for determining the treatment options. Endoscopic resection is used for treatment of EGC with minimal risk of LNM. However, owing to the lack of definitive criteria for identifying patients who require additional surgery, some patients undergo unnecessary additional surgery. Considering that histopathologic patterns are significant factor for predicting lymph node metastasis in gastric cancer, we aimed to develop a machine learning algorithm which can predict LNM status using hematoxylin and eosin (H&E)-stained images. The images were obtained from several institutions. Our pipeline comprised two sequential approaches including a feature extractor and a risk classifier. For the feature extractor, a segmentation network (DeepLabV3+) was trained on 243 WSIs across three datasets to differentiate each histological subtype. The risk classifier was trained with XGBoost using 70 morphological features inferred from the trained feature extractor. The trained segmentation network, the feature extractor, achieved high performance, with pixel accuracies of 0.9348 and 0.8939 for the internal and external datasets in patch level, respectively. The risk classifier achieved an overall AUC of 0.75 in predicting LNM status. Remarkably, one of the datasets also showed a promising result with an AUC of 0.92. This is the first multi-institution study to develop machine learning algorithm for predicting LNM status in patients with EGC using H&E-stained histopathology images. Our findings have the potential to improve the selection of patients who require surgery among those with EGC showing high-risk histological features.

MRI versus Dual-Energy CT in Local-Regional Staging of Gastric Cancer

Background Preoperative local-regional tumor staging of gastric cancer (GC) is critical for appropriate treatment planning. The comparative accuracy of multiparametric MRI (mpMRI) versus dual-energy CT (DECT) for staging of GC is not known. Purpose To compare the diagnostic accuracy of personalized mpMRI with that of DECT for local-regional T and N staging in patients with GC receiving curative surgical intervention. Materials and Methods Patients with GC who underwent gastric mpMRI and DECT before gastrectomy with lymphadenectomy were eligible for this single-center prospective noninferiority study between November 2021 and September 2022. mpMRI comprised T2-weighted imaging, multiorientational zoomed diffusion-weighted imaging, and extradimensional volumetric interpolated breath-hold examination dynamic contrast-enhanced imaging. Dual-phase DECT images were reconstructed at 40 keV and standard 120 kVp-like images. Using gastrectomy specimens as the reference standard, the diagnostic accuracy of mpMRI and DECT for T and N staging was compared by six radiologists in a pairwise blinded manner. Interreader agreement was assessed using the weighted κ and Kendall W statistics. The McNemar test was used for head-to-head accuracy comparisons between DECT and mpMRI. Results This study included 202 participants (mean age, 62 years ± 11 [SD]; 145 male). The interreader agreement of the six readers for T and N staging of GC was excellent for both mpMRI (κ = 0.89 and 0.85, respectively) and DECT (κ = 0.86 and 0.84, respectively). Regardless of reader experience, higher accuracy was achieved with mpMRI than with DECT for both T (61%-77% vs 50%-64%; all P < .05) and N (54%-68% vs 51%-58%; P = .497-.005) staging, specifically T1 (83% vs 65%) and T4a (78% vs 68%) tumors and N1 (41% vs 24%) and N3 (64% vs 45%) nodules (all P < .05). Conclusion Personalized mpMRI was superior in T staging and noninferior or superior in N staging compared with DECT for patients with GC. Clinical trial registration no. NCT05508126 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Méndez and Martín-Garre in this issue.

Current trends in the management of Gastro-oEsophageal cancers: Updates to the ESSO core curriculum (ESSO-ETC-UGI-WG initiative)

Gastro-oEsophageal Cancers (GECs) are severe diseases whose management is rapidly evolving. The European Society of Surgical Oncology (ESSO) is committed to the generation and spread of knowledge, and promotes the multidisciplinary management of cancer patients through its core curriculum. The present work discusses the approach to GECs, including the management of oligometastatic oesophagogastric cancers (OMEC), the diagnosis and management of peritoneal metastases from gastric cancer (GC), the management of Siewert Type II tumors, the importance of mesogastric excision, the role of robotic surgery, textbook outcomes, organ preserving options, the use of molecular markers and immune check-point inhibitors in the management of patients with GECs, as well as the improvement of current clinical practice guidelines for the management of patients with GECs. The aim of the present review is to provide a concise overview of the state-of-the-art on the management of patients with GECs and, at the same time, to share the latest advancements in the field and to foster the debate between surgical oncologists treating GECs worldwide. We are sure that our work will, at the same time, give an update to the advanced surgical oncologists and help the training surgical oncologists to settle down the foundations for their future practice.

Impact of 18FFDG-PET/CT and Laparoscopy in Staging of Locally Advanced Gastric Cancer: A Cost Analysis in the Prospective Multicenter PLASTIC-Study

BACKGROUND

Unnecessary D2-gastrectomy and associated costs can be prevented after detecting non-curable gastric cancer, but impact of staging on treatment costs is unclear. This study determined the cost impact of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18FFDG-PET/CT) and staging laparoscopy (SL) in gastric cancer staging.

MATERIALS AND METHODS

In this cost analysis, four staging strategies were modeled in a decision tree: (1) 18FFDG-PET/CT first, then SL, (2) SL only, (3) 18FFDG-PET/CT only, and (4) neither SL nor 18FFDG-PET/CT. Costs were assessed on the basis of the prospective PLASTIC-study, which evaluated adding 18FFDG-PET/CT and SL to staging advanced gastric cancer (cT3-4 and/or cN+) in 18 Dutch hospitals. The Dutch Healthcare Authority provided 18FFDG-PET/CT unit costs. SL unit costs were calculated bottom-up. Gastrectomy-associated costs were collected with hospital claim data until 30 days postoperatively. Uncertainty was assessed in a probabilistic sensitivity analysis (1000 iterations).

RESULTS

18FFDG-PET/CT costs were €1104 including biopsy/cytology. Bottom-up calculations totaled €1537 per SL. D2-gastrectomy costs were €19,308. Total costs per patient were €18,137 for strategy 1, €17,079 for strategy 2, and €19,805 for strategy 3. If all patients undergo gastrectomy, total costs were €18,959 per patient (strategy 4). Performing SL only reduced costs by €1880 per patient. Adding 18FFDG-PET/CT to SL increased costs by €1058 per patient; IQR €870-1253 in the sensitivity analysis.

CONCLUSIONS

For advanced gastric cancer, performing SL resulted in substantial cost savings by reducing unnecessary gastrectomies. In contrast, routine 18FFDG-PET/CT increased costs without substantially reducing unnecessary gastrectomies, and is not recommended due to limited impact with major costs.

TRIAL REGISTRATION

NCT03208621. This trial was registered prospectively on 30-06-2017.

Diagnostic performance of Node Reporting and Data System (Node-RADS) for regional lymph node staging of gastric cancer by CT

OBJECTIVES

Diagnostic performance of imaging for regional lymph node assessment in gastric cancer is still limited, and there is a lack of consensus on radiological evaluation. At the same time, there is an increasing demand for structured reporting using Reporting and Data Systems (RADS) to standardize oncological imaging. We aimed at investigating the diagnostic performance of Node-RADS compared to the use of various individual criteria for assessing regional lymph nodes in gastric cancer using histopathology as reference.

METHODS

In this retrospective single-center study, consecutive 91 patients (median age, 66 years, range 33-91 years, 54 men) with CT scans and histologically proven gastric adenocarcinoma were assessed using Node-RADS assigning scores from 1 to 5 for the likelihood of regional lymph node metastases. Additionally, different Node-RADS criteria as well as subcategories of altered border contour (lobulated, spiculated, indistinct) were assessed individually. Sensitivity, specificity, and Youden's index were calculated for Node-RADS scores, and all criteria investigated. Interreader agreement was calculated using Cohen's kappa.

RESULTS

Among all criteria, best performance was found for Node-RADS scores ≥ 3 and ≥ 4 with a sensitivity/specificity/Youden's index of 56.8%/90.7%/0.48 and 48.6%/98.1%/0.47, respectively, both with substantial interreader agreement (κ = 0.73 and 0.67, p < 0.01). Among individual criteria, the best performance was found for short-axis diameter of 10 mm with sensitivity/specificity/Youden's index of 56.8%/87.0%/0.44 (κ = 0.65, p < 0.01).

CONCLUSION

This study shows that structured reporting of combined size and configuration criteria of regional lymph nodes in gastric cancer slightly improves overall diagnostic performance compared to individual criteria including short-axis diameter alone. The results show an increase in specificity and unchanged sensitivity.

CLINICAL RELEVANCE STATEMENT

The results of this study suggest that Node-RADS may be a suitable tool for structured reporting of regional lymph nodes in gastric cancer.

KEY POINTS

• Assessment of lymph nodes in gastric cancer is still limited, and there is a lack of consensus on radiological evaluation. • Node-RADS in gastric cancer improves overall diagnostic performance compared to individual criteria including short-axis diameter. • Node-RADS may be a suitable tool for structured reporting of regional lymph nodes in gastric cancer.

Chinese national clinical practice guidelines on the prevention, diagnosis, and treatment of early gastric cancer

BACKGROUND

Gastric cancer is one of the most common malignant tumors in the digestive system in China. Few comprehensive practice guidelines for early gastric cancer in China are currently available. Therefore, we created the Chinese national clinical practice guideline for the prevention, diagnosis, and treatment of early gastric cancer.

METHODS

This clinical practice guideline (CPG) was developed in accordance with the World Health Organization's recommended process and with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) in assessing evidence quality. We used the Evidence to Decision framework to formulate clinical recommendations to minimize bias and increase transparency in the CPG development process. We used the Reporting Items for practice Guidelines in HealThcare (RIGHT) statement and the Appraisal of Guidelines for Research and Evaluation II (AGREE II) as reporting and conduct guidelines to ensure completeness and transparency of the CPG.

RESULTS

This CPG contains 40 recommendations regarding the prevention, screening, diagnosis, treatment, and follow-up of early gastric cancer based on available clinical studies and guidelines. We provide recommendations for the timing of Helicobacter pylori eradication, screening populations for early gastric cancer, indications for endoscopic resection and surgical gastrectomy, follow-up interval after treatment, and other recommendations.

CONCLUSIONS

This CPG can lead to optimum care for patients and populations by providing up-to-date medical information. We intend this CPG for widespread adoption to increase the standard of prevention, screening, diagnosis, treatment, and follow-up of early gastric cancer; thereby, contributing to improving national health care and patient quality of life.

Tumor-positive peritoneal cytology in patients with gastric cancer is associated with poor outcome: A nationwide study

BACKGROUND

The clinical significance of tumor-positive peritoneal cytology (CYT+) in gastric cancer (GC) patients is unclear. This nationwide cohort study aimed to i) assess the frequency of cytological analysis at staging laparoscopy; ii) determine the prevalence of CYT+GC; and iii) compare overall survival (OS) in CYT+ patients versus those with (PM+) and those without (PM-) macroscopic peritoneal disease.

METHODS

All patients diagnosed with cT1-4, cN0-2 and M0 or synchronous PM GC between 2016-2021 were identified in the Netherlands Cancer Registry database and linked to the nationwide pathology database.

RESULTS

A total of 4397 patients was included, of which 40 % underwent cytological assessment following staging laparoscopy (863/1745). The prevalence of CYT+ was 8 %. A total of 69 patients had CYT+(1.6 %), 789 (17.9 %) had PM+ and 3539 (80.5 %) had PM- disease. Hazard ratio for OS in CYT+ versus PM+ was 0.86 (95 %CI 0.64-1.17, p-value=0.338), and in PM- versus PM+0.43 (95 %CI 0.38-0.49, p-value<0.001). No survival difference was found between systemic chemotherapy versus surgical resection in CYT+ patients.

DISCUSSION

In this nationwide study, OS for gastric cancer patients with CYT+ was equally unfavorable as for those with PM+ and significantly worse as compared to those with PM-. The optimal treatment strategy has yet to be established.

Pre- and Post-treatment Double-Sequential-Point Dynamic Radiomic Model in the Response Prediction of Gastric Cancer to Neoadjuvant Chemotherapy: 3-Year Survival Analysis

BACKGROUND

Prognosis prediction of patients with gastric cancer after neoadjuvant chemotherapy is suboptimal. This study aims to develop and validate a dynamic radiomic model for prognosis prediction of patients with gastric cancer on the basis of baseline and posttreatment features.

PATIENTS AND METHODS

This single-center cohort study included patients with gastric adenocarcinoma treated with neoadjuvant chemotherapy from June 2009 to July 2015 in the Gastrointestinal Cancer Center of Peking University Cancer Hospital. Their clinicopathological data, pre-treatment and post-treatment computed tomography (CT) images, and pathological reports were retrieved and analyzed. Four prediction models were developed and validated using tenfold cross-validation, with death within 3 years as the outcome. Model discrimination was compared by the area under the curve (AUC). The final radiomic model was evaluated for calibration and clinical utility using Hosmer-Lemeshow tests and decision curve analysis.

RESULTS

The study included 205 patients with gastric adenocarcinoma [166 (81%) male; mean age 59.9 (SD 10.3) years], with 71 (34.6%) deaths occurring within 3 years. The radiomic model alone demonstrated better discrimination than the pathological T stage (ypT) stage model alone (cross-validated AUC 0.598 versus 0.516, P = 0.009). The final radiomic model, which incorporated both radiomic and clinicopathological characteristics, had a significantly higher cross-validated AUC (0.769) than the ypT stage model (0.516), the radiomics alone model (0.598), and the ypT plus other clinicopathological characteristics model (0.738; all P < 0.05). Decision curve analysis confirmed the clinical utility of the final radiomic model.

CONCLUSIONS

The developed radiomic model had good accuracy and could be used as a decision aid tool in clinical practice to differentiate prognosis of patients with gastric cancer.

The Role of Methionine Restriction in Gastric Cancer: A Summary of Mechanisms and a Discussion on Tumor Heterogeneity

Gastric cancer is ranked as the fifth most prevalent cancer globally and has long been a topic of passionate discussion among numerous individuals. However, the incidence of gastric cancer in society has not decreased, but instead has shown a gradual increase in recent years. For more than a decade, the treatment effect of gastric cancer has not been significantly improved. This is attributed to the heterogeneity of cancer, which makes popular targeted therapies ineffective. Methionine is an essential amino acid, and many studies have shown that it is involved in the development of gastric cancer. Our study aimed to review the literature on methionine and gastric cancer, describing its mechanism of action to show that tumor heterogeneity in gastric cancer does not hinder the effectiveness of methionine-restricted therapies. This research also aimed to provide insight into the inhibition of gastric cancer through metabolic reprogramming with methionine-restricted therapies, thereby demonstrating their potential as adjuvant treatments for gastric cancer.

Fibroblast Activation Protein-Targeted PET/CT with 18F-Fibroblast Activation Protein Inhibitor-74 for Evaluation of Gastrointestinal Cancer: Comparison with 18F-FDG PET/CT

Fibroblast activation protein is overexpressed in the stroma of several cancer types. 18F-fibroblast activation protein inhibitor (FAPI)-74 is a PET tracer with high selectivity for fibroblast activation protein and has shown high accumulation in human tumors in clinical studies. However, the use of 18F-FAPI-74 for PET imaging of gastrointestinal cancer has not been systematically investigated. Herein, we investigated the diagnostic accuracy of 18F-FAPI-74 (18F-LNC1005) PET/CT in gastric, liver, and pancreatic cancers and compared the results with those of 18F-FDG PET/CT. Methods: This prospective study analyzed patients with confirmed gastric, liver, or pancreatic malignancies who underwent concurrent 18F-FDG and 18F-FAPI-74 PET/CT between June 2022 and December 2022. PET/CT findings were confirmed by histopathology or radiographic follow-up. 18F-FDG and 18F-FAPI-74 uptake and tumor-to-background ratios were compared using the Wilcoxon signed-rank test. The McNemar test was used to compare the diagnostic accuracy of the 2 scans. Results: Our cohort consisted of 112 patients: 49 with gastric cancer, 39 with liver cancer, and 24 with pancreatic cancer. Among them, 69 patients underwent PET/CT for initial staging and 43 for recurrence detection. Regarding lesion-based diagnostic accuracy, 18F-FAPI-74 PET/CT showed higher sensitivity than did 18F-FDG in the detection of primary tumors (gastric cancer, 88% [22/25] vs. 60% [15/25], P = 0.016; liver cancer, 100% [22/22] vs. 82% [18/22], P = 0.125; pancreatic cancer, 100% [22/22] vs. 86% [19/22], P = 0.250), local recurrence (92% [23/25] vs. 56% [14/25]; P = 0.021), involved lymph nodes (71% [41/58] vs. 40% [23/58]; P < 0.001), and bone and visceral metastases (98% [350/358] vs. 47% [168/358]; P < 0.001). Compared with 18F-FDG, 18F-FAPI-74 PET/CT upstaged 17 patients' TNM staging among all treatment-naïve patients (17/69, 25%) and changed the clinical management of 4 patients (4/43, 9%) in whom recurrence or metastases were detected. Conclusion: 18F-FAPI-74 PET/CT is superior to 18F-FDG PET/CT in detecting primary tumors, local recurrence, lymph node involvement, and bone and visceral metastases in gastric, pancreatic, and liver cancers, with higher uptake in most primary and metastatic lesions.

Coexistence of early gastric cancer and benign submucosal lesions mimic invasive cancer: a retrospective multicenter experience

OBJECTIVE

To present a study to identify the characteristics of coexisting early gastric cancer (EGC) and benign submucosal lesions, with the aim of reducing the adverse consequences of overdiagnosis and overtreatment.

METHODS

In this retrospective study, we searched the endoscopic databases of three tertiary centers. We screened of patients suspected of early gastric cancer submucosal infiltration by conventional endoscopy and ultimately selected for endoscopic submucosal dissection treatment after endoscopic ultrasonography and magnifying endoscopy with narrow-band imaging examination. Patients with coexisting EGC and benign submucosal lesions in histological sections were included. Clinical data and endoscopic images were reviewed. To evaluate the precision of endoscopists' diagnoses for this type of lesion, eight endoscopists with different experiences were recruited to judge the infiltration depth of these lesions and analyze the accuracy rate.

RESULTS

We screened 520 patients and retrospectively identified 18 EGC patients with an invasive cancer-like morphology. The most common lesion site was the cardia (12/18, 66.67%). The coexisting submucosal lesions could be divided into solid (5/18, 27.78%) and cystic (13/18, 72.22%). The most common type of submucosal lesion was gastritis cystica profunda (12/18, 66.67%), whereas leiomyoma was the predominant submucosal solid lesion (3/18, 16.67%). Ten (55.56%) patients < underwent endoscopic ultrasonography; submucosal lesions were definitively diagnosed in 6 patients (60.00%). The accuracy of judgement of the infiltration depth was significantly lower in cases of coexistence of EGC with benign submucosal lesions (EGC-SML) than in EGC (38.50% versus 65.60%, P = 0.0167). The rate of over-diagnosis was significantly higher within the EGC-SML group compared to the EGC group (59.17% versus 10.83%, P < 0.0001).

CONCLUSIONS

We should be aware of the coexistence of EGC and benign submucosal lesions, the most common of which is early cardiac-differentiated cancer with gastritis cystica profunda.

Extracellular volume fraction determined by equilibrium contrast-enhanced computed tomography: correlation with histopathological findings in gastric cancer

PURPOSE

To assess the relationship between histopathological features of gastric cancer and the extracellular volume fraction (ECV) measured by preoperative equilibrium contrast-enhanced computed tomography (CECT).

MATERIALS AND METHODS

The study group consisted of 66 patients with surgically resected gastric adenocarcinoma who underwent preoperative multiphasic CECT. Tumor ECVs were calculated using region-of-interest measurements within the gastric cancer and aorta of each case on unenhanced and equilibrium-phase images. The relationship between the mean ECV values and clinicopathological parameters was examined by univariate analysis. Parameters showing a significant difference in the former test were further tested by linear regression and receiver operating characteristic (ROC) curve analyses.

RESULTS

In the univariate analysis, the values of venous invasion (p = 0.0487) and tumor infiltration (INF) pattern (p < 0.0001) were significantly correlated with the tumor ECV. INF was significantly correlated (β = 0.57, p < 0.0001) in the linear regression analysis. The tumor ECV showed better diagnostic accuracy for predicting INF (INFa/b vs INFc), and the area under the ROC curve value was 0.89.

CONCLUSION

Tumor ECV determined by equilibrium CECT is significantly correlated with the pathological INF of gastric cancer.

The Role of Staging Laparoscopy for Gastric Cancer Patients: Current Evidence and Future Perspectives

A significant proportion of patients diagnosed with gastric cancer is discovered with peritoneal metastases at laparotomy. Despite the continuous improvement in the performance of radiological imaging, the preoperative recognition of such an advanced disease is still challenging during the diagnostic work-up, since the sensitivity of CT scans to peritoneal carcinomatosis is not always adequate. Staging laparoscopy offers the chance to significantly increase the rate of promptly diagnosed peritoneal metastases, thus reducing the number of unnecessary laparotomies and modifying the initial treatment strategy of gastric cancer. The aim of this review was to provide a comprehensive summary of the current literature regarding the role of staging laparoscopy in the management of gastric cancer. Indications, techniques, accuracy, advantages, and limitations of staging laparoscopy and peritoneal cytology were discussed. Furthermore, a focus on current evidence regarding the application of artificial intelligence and image-guided surgery in staging laparoscopy was included in order to provide a picture of the future perspectives of this technique and its integration with modern tools in the preoperative management of gastric cancer.

Prognostic Value of [18F]FDG PET Radiomics to Detect Peritoneal and Distant Metastases in Locally Advanced Gastric Cancer-A Side Study of the Prospective Multicentre PLASTIC Study

AIM

To improve identification of peritoneal and distant metastases in locally advanced gastric cancer using [¹⁸F]FDG-PET radiomics.

METHODS

[¹⁸F]FDG-PET scans of 206 patients acquired in 16 different Dutch hospitals in the prospective multicentre PLASTIC-study were analysed. Tumours were delineated and 105 radiomic features were extracted. Three classification models were developed to identify peritoneal and distant metastases (incidence: 21%): a model with clinical variables, a model with radiomic features, and a clinicoradiomic model, combining clinical variables and radiomic features. A least absolute shrinkage and selection operator (LASSO) regression classifier was trained and evaluated in a 100-times repeated random split, stratified for the presence of peritoneal and distant metastases. To exclude features with high mutual correlations, redundancy filtering of the Pearson correlation matrix was performed (r = 0.9). Model performances were expressed by the area under the receiver operating characteristic curve (AUC). In addition, subgroup analyses based on Lauren classification were performed.

RESULTS

None of the models could identify metastases with low AUCs of 0.59, 0.51, and 0.56, for the clinical, radiomic, and clinicoradiomic model, respectively. Subgroup analysis of intestinal and mixed-type tumours resulted in low AUCs of 0.67 and 0.60 for the clinical and radiomic models, and a moderate AUC of 0.71 in the clinicoradiomic model. Subgroup analysis of diffuse-type tumours did not improve the classification performance.

CONCLUSION

Overall, [¹⁸F]FDG-PET-based radiomics did not contribute to the preoperative identification of peritoneal and distant metastases in patients with locally advanced gastric carcinoma. In intestinal and mixed-type tumours, the classification performance of the clinical model slightly improved with the addition of radiomic features, but this slight improvement does not outweigh the laborious radiomic analysis.

Multi-modal analysis for accurate prediction of preoperative stage and indications of optimal treatment in gastric cancer

BACKGROUND

Accurate preoperative clinical staging of gastric cancer helps determine therapeutic strategies. However, no multi-category grading models for gastric cancer have been established. This study aimed to develop multi-modal (CT/EHRs) artificial intelligence (AI) models for predicting tumor stages and optimal treatment indication based on preoperative CT images and electronic health records (EHRs) in patients with gastric cancer.

METHODS

This retrospective study enrolled 602 patients with a pathological diagnosis of gastric cancer from Nanfang hospital retrospectively and divided them into training (n = 452) and validation sets (n = 150). A total of 1326 features were extracted of which 1316 radiomic features were extracted from the 3D CT images and 10 clinical parameters were obtained from electronic health records (EHRs). Four multi-layer perceptrons (MLPs) whose input was the combination of radiomic features and clinical parameters were automatically learned with the neural architecture search (NAS) strategy.

RESULTS

Two two-layer MLPs identified by NAS approach were employed to predict the stage of the tumor showed greater discrimination with the average ACC value of 0.646 for five T stages, 0.838 for four N stages than traditional methods with ACC of 0.543 (P value = 0.034) and 0.468 (P value = 0.021), respectively. Furthermore, our models reported high prediction accuracy for the indication of endoscopic resection and the preoperative neoadjuvant chemotherapy with the AUC value of 0.771 and 0.661, respectively.

CONCLUSIONS

Our multi-modal (CT/EHRs) artificial intelligence models generated with the NAS approach have high accuracy for tumor stage prediction and optimal treatment regimen and timing, which could facilitate radiologists and gastroenterologists to improve diagnosis and treatment efficiency.

Imaging in Gastric Cancer: Current Practice and Future Perspectives

Gastric cancer represents one of the most common oncological causes of death worldwide. In order to treat patients in the best possible way, the staging of gastric cancer should be accurate. In this regard, endoscopy ultrasound (EUS) has been considered the reference standard for tumor (T) and nodal (N) statuses in recent decades. However, thanks to technological improvements, computed tomography (CT) has gained an important role, not only in the assessment of distant metastases (M status) but also in T and N staging. In addition, magnetic resonance imaging (MRI) can contribute to the detection and staging of primary gastric tumors thanks to its excellent soft tissue contrast and multiple imaging sequences without radiation-related risks. In addition, MRI can help with the detection of liver metastases, especially small lesions. Finally, positron emission tomography (PET) is still considered a useful diagnostic tool for the staging of gastric cancer patients, with a focus on nodal metastases and peritoneal carcinomatosis. In addition, it may play a role in the treatment of gastric cancer in the coming years thanks to the introduction of new labeling peptides. This review aims to summarize the most common advantages and pitfalls of EUS, CT, MRI and PET in the TNM staging of gastric cancer patients.

Clinical application of AI-based PET images in oncological patients

Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.

Аre Multivisceral Resections for Gastric Cancer Acceptable: Experience from a High Volume Center and Extended Literature Review?

Introduction  Multivisceral resections (MVRs) in gastric cancer are potentially curable in selected patients in whom clear resection margins are possible. However, there are still uncertain data on their feasibility and safety considering short- and long-term results. The study compares survival, morbidity, mortality, and other secondary outcomes between standard and MVRs for gastric cancer. Materials and Methods  A monocentric retrospective study in patients with gastric adenocarcinoma, covering 2004 to 2020. Of the 336 operable cases, 101 patients underwent MVRs. The remaining 235 underwent standard gastric resections (SGRs), of which 173 patients were in stage T3/T4. To compare survival, a control group of 101 patients with palliative procedures was used-bypass anastomosis or exploration. Results  MVR had a lower survival rate than the SGR but significantly higher than the palliative procedures. The predominant gender in MVR was male (72.3%), with a mean age of 61 years. The perioperative mortality was 3.96% ( n  = 4), and the overall median survival was 28.1 months. The most frequently resected organs were the spleen (67.3%), followed by the pancreas (32.7%) and the liver (20.8%). In 56.4% of the cases two organs were resected, in 28.7% three organs, and in 13.9% four organs. The main complication was bleeding (9.9%). The major postoperative complications in the MVR were 14.85%, and in the SGR 6.4% ( p  < 0.05). Better long-term results were observed in patients who underwent R0 resections compared with R1. Conclusion  Multiorgan resections are characterized by poorer survival and a higher complication rate than gastrectomies. On the other hand, they have better long-term outcomes than palliative procedures. However, MVRs are admissible when performed by an experienced surgical team in high-volume centers.

Endoscopic Ultrasound Predicts Risk of Occult Intra-Abdominal Metastases in Localized Gastric Cancer: A Validation Study

Background

In gastric cancer (GC) patients without imaging evidence of distant metastasis, diagnostic staging laparoscopy (DSL) is recommended to detect radiographically occult peritoneal metastasis (M1). DSL carries a risk for morbidity and its cost-effectiveness is unclear. Use of endoscopic ultrasound (EUS) to improve patient selection for DSL has been proposed but not validated. We aimed to validate an EUS-based risk classification system predicting risk for M1 disease.

Methods

We retrospectively identified all GC patients without positron emission tomography (PET)/computed tomography (CT) evidence of distant metastasis who underwent staging EUS followed by DSL between 2010 and 2020. T1-2, N0 disease was EUS "low-risk"; T3-4 and/or N+ disease was "high-risk".

Results

A total of 68 patients met inclusion criteria. DSL identified radiographically occult M1 disease in 17 patients (25%). Most patients had EUS T3 tumors (n = 59, 87%) and 48 (71%) patients were node-positive (N+). Five (7%) patients were classified EUS "low-risk" and 63 (93%) were classified "high-risk". Of 63 "high-risk" patients, 17 (27%) had M1 disease. The ability of "low-risk" EUS to predict M0 disease at laparoscopy was 100% and DSL would have been avoided in five patients (7%). This stratification algorithm showed a sensitivity of 100% (95% confidence interval (CI): 80.5-100%) and a specificity of 9.8% (95% CI: 3.3-21.4%).

Conclusions

Use of an EUS-based risk classification system in GC patients without imaging evidence of metastasis helps identify a subset of patients at low-risk for laparoscopic M1 disease who may avoid DSL and proceed directly to neoadjuvant chemotherapy or resection with curative intent. Larger, prospective studies are needed to validate these findings.

Artificial intelligence applicated in gastric cancer: A bibliometric and visual analysis via CiteSpace

Objective

This study aimed to analyze and visualize the current research focus, research frontiers, evolutionary processes, and trends of artificial intelligence (AI) in the field of gastric cancer using a bibliometric analysis.

Methods

The Web of Science Core Collection database was selected as the data source for this study to retrieve and obtain articles and reviews related to AI in gastric cancer. All the information extracted from the articles was imported to CiteSpace to conduct the bibliometric and knowledge map analysis, allowing us to clearly visualize the research hotspots and trends in this field.

Results

A total of 183 articles published between 2017 and 2022 were included, contributed by 201 authors from 33 countries/regions. Among them, China (47.54%), Japan (21.86%), and the USA (13.11%) have made outstanding contributions in this field, accounting fsor 82.51% of the total publications. The primary research institutions were Wuhan University, Tokyo University, and Tada Tomohiro Inst Gastroenterol and Proctol. Tada (n = 12) and Hirasawa (n = 90) were ranked first in the top 10 authors and co-cited authors, respectively. Gastrointestinal Endoscopy (21 publications; IF 2022, 9.189; Q1) was the most published journal, while Gastric Cancer (133 citations; IF 2022, 8.171; Q1) was the most co-cited journal. Nevertheless, the cooperation between different countries and institutions should be further strengthened. The most common keywords were AI, gastric cancer, and convolutional neural network. The "deep-learning algorithm" started to burst in 2020 and continues till now, which indicated that this research topic has attracted continuous attention in recent years and would be the trend of research on AI application in GC.

Conclusions

Research related to AI in gastric cancer is increasing exponentially. Current research hotspots focus on the application of AI in gastric cancer, represented by convolutional neural networks and deep learning, in diagnosis and differential diagnosis and staging. Considering the great potential and clinical application prospects, the related area of AI applications in gastric cancer will remain a research hotspot in the future.

Prostate-Specific Membrane Antigen Targeted Pet/CT Imaging in Patients with Colon, Gastric and Pancreatic Cancer

Current imaging modalities frequently misjudge disease stage in colorectal, gastric and pancreatic cancer. As treatment decisions are dependent on disease stage, incorrect staging has serious consequences. Previous preclinical research and case reports indicate that prostate-specific membrane antigen (PSMA)-targeted PET/CT imaging might provide a solution to some of these challenges. This prospective clinical study aims to assess the feasibility of [¹⁸F]DCFPyL PET/CT imaging to target and visualize primary colon, gastric and pancreatic cancer. In this prospective clinical trial, patients with colon, gastric and pancreatic cancer were included and underwent both [¹⁸F]DCFPyL and [¹⁸F]FDG PET/CT scans prior to surgical resection or (for gastric cancer) neoadjuvant therapy. Semiquantitative analysis of immunohistochemical PSMA staining was performed on the surgical resection specimens, and the results were correlated to imaging parameters. The results of this study demonstrate detection of the primary tumor by [¹⁸F]DCFPyL PET/CT in 7 out of 10 patients with colon, gastric and pancreatic cancer, with a mean tumor-to-blood pool ratio (TBR) of 3.3 and mean SUV_max of 3.6. However, due to the high surrounding uptake, visual distinction of these tumors was difficult, and the SUV_max and TBR on [¹⁸F]FDG PET/CT were significantly higher than on [¹⁸F]DCFPyL PET/CT. In addition, no correlation between PSMA expression in the resection specimen and SUV_max on [¹⁸F]DCFPyL PET/CT was found. In conclusion, the detection of several gastrointestinal cancers using [¹⁸F]DCFPyL PET/CT is feasible. However, low tumor expression and high uptake physiologically in organs/background hamper the clear distinction of the tumor. As a result, [¹⁸F]FDG PET/CT was superior in detecting colon, gastric and pancreatic cancers.

A quantitative model based on gross tumor volume of gastric adenocarcinoma corresponding to N-stage measured at multidetector computed tomography for preoperative determination of resectability: A case control study

PURPOSE

To develop and validate a quantitative model based on gross tumor volume (GTV) of gastric adenocarcinoma (GA) corresponding to N-stage measured at multidetector computed tomography (CT) for preoperative determination of resectability.

MATERIALS AND METHODS

493 consecutive patients with confirmed GA undergoing contrast-enhanced CT two weeks before treatments were randomly enrolled into the training cohort (TC, n = 271), internal validation cohort (IVC, n = 107) and external validation cohort (EVC, n = 115). GTV was measured on CT by multiplying sums of all tumor areas by section thickness. In TC, univariate and multivariate analyses were performed to select factors associated with resectability. Receiver operating characteristic (ROC) analysis was to determine if N-stage based GTV could identify resectability. In IVC and EVC, unweighted Cohen's Kappa tests were to evaluate performances of the ROC models.

RESULTS

According to univariate analysis, age, cT stage, cN stage and GTV were related to resectability in TC (all P-values < 0.05), and multivariate analysis suggested that cN stage and GTV were independent risk factors with odds ratios of 1.594 (95% confidence interval [CI]: 1.105-2.301) and 1.055 (95%CI: 1.035-1.076), respectively. ROC analysis in TC revealed the cutoffs of 21.81, 21.70 and 36.93 cm3 to differentiate between resectable and unresectable cancers in stages cN0-3, cN2 and cN3 with areas under the curves of more than 0.8, respectively, which was validated in IVC and EVC with average Cohen k-values of more than 0.72.

CONCLUSIONS

GTV and cN stage can be independent risk factors of unresectable GA, and N-stage based GTV can help determine resectability.

Application of nanotechnology in the early diagnosis and comprehensive treatment of gastrointestinal cancer

Gastrointestinal cancer (GIC) is a common malignant tumour of the digestive system that seriously threatens human health. Due to the unique organ structure of the gastrointestinal tract, endoscopic and MRI diagnoses of GIC in the clinic share the problem of low sensitivity. The ineffectiveness of drugs and high recurrence rates in surgical and drug therapies are the main factors that impact the curative effect in GIC patients. Therefore, there is an urgent need to improve diagnostic accuracies and treatment efficiencies. Nanotechnology is widely used in the diagnosis and treatment of GIC by virtue of its unique size advantages and extensive modifiability. In the diagnosis and treatment of clinical GIC, surface-enhanced Raman scattering (SERS) nanoparticles, electrochemical nanobiosensors and magnetic nanoparticles, intraoperative imaging nanoparticles, drug delivery systems and other multifunctional nanoparticles have successfully improved the diagnosis and treatment of GIC. It is important to further improve the coordinated development of nanotechnology and GIC diagnosis and treatment. Herein, starting from the clinical diagnosis and treatment of GIC, this review summarizes which nanotechnologies have been applied in clinical diagnosis and treatment of GIC in recent years, and which cannot be applied in clinical practice. We also point out which challenges must be overcome by nanotechnology in the development of the clinical diagnosis and treatment of GIC and discuss how to quickly and safely combine the latest nanotechnology developed in the laboratory with clinical applications. Finally, we hope that this review can provide valuable reference information for researchers who are conducting cross-research on GIC and nanotechnology.

A computed tomography-based preoperative risk scoring system to distinguish lymphoepithelioma-like gastric carcinoma from non-lymphoepithelioma-like gastric carcinoma

Purpose

The aim of this study was to develop a preoperative risk scoring model for distinguishing lymphoepithelioma-like gastric carcinoma (LELGC) from non-LELGC based on contrast-enhanced computed tomography (CT) images.

Methods

Clinicopathological features and CT findings of patients with LELGC and non-LELGC in our hospital from January 2016 to July 2022 were retrospectively analyzed and compared. A preoperative risk stratification model and a risk scoring system were developed using logistic regression.

Results

Twenty patients with LELGC and 40 patients with non-LELGC were included in the training cohort. Significant differences were observed in Epstein–Barr virus (EBV) infection and vascular invasion between the two groups (p < 0.05). Significant differences were observed in the distribution of location, enhancement pattern, homogeneous enhancement, CT-defined lymph node status, and attenuations in the non-contrast, arterial, and venous phases (all p < 0.05). Enhancement pattern, CT-defined lymph node status, and attenuation in venous phase were independent predictors of LELGC. The optimal cutoff score of distinguishing LELGC from non-LELGC was 3.5. The area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of risk identification model in the training cohort were 0.904, 87.5%, 80.0%, and 85.0%, respectively. The area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of risk identification model in the validation cohort were 0.705 (95% CI 0.434–0.957), 75.0%, 63.6%, and 66.7%, respectively.

Conclusion

A preoperative risk identification model based on CT imaging data could be helpful for distinguishing LELGC from non-LELGC.

Gastric Cancer Invading the Pancreas: A Review of the Role of Pancreatectomy

Gastric cancer is quite a common type of cancer, with significant associated mortality. Traditionally, combined resections of affected organs have been advocated in cases of locally advanced gastric cancer, in order to achieve an R0 resection. The purpose of the present study was to evaluate the role of pancreatectomy in the treatment of gastric cancer invading the pancreas by reviewing the relevant literature. The oncological benefits to survival rates of multivisceral resection are not always obvious from the relevant survival charts, especially when the pancreas is the organ invaded by the gastric cancer and gastrectomy needs to be combined with a pancreatectomy, an operation with high morbidity rates. In conclusion, careful patient selection is essential to achieving optimal results, balancing the oncological benefits in these properly selected patients against the associated morbidity of extensive resection.

Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

Objective

To evaluate the diagnostic performance of machine learning (ML) in predicting lymph node metastasis (LNM) in patients with gastric cancer (GC) and to identify predictors applicable to the models.

Methods

PubMed, EMBASE, Web of Science, and Cochrane Library were searched from inception to March 16, 2022. The pooled c-index and accuracy were used to assess the diagnostic accuracy. Subgroup analysis was performed based on ML types. Meta-analyses were performed using random-effect models. Risk of bias assessment was conducted using PROBAST tool.

Results

A total of 41 studies (56182 patients) were included, and 33 of the studies divided the participants into a training set and a test set, while the rest of the studies only had a training set. The c-index of ML for LNM prediction in training set and test set was 0.837 [95%CI (0.814, 0.859)] and 0.811 [95%CI (0.785-0.838)], respectively. The pooled accuracy was 0.781 [(95%CI (0.756-0.805)] in training set and 0.753 [95%CI (0.721-0.783)] in test set. Subgroup analysis for different ML algorithms and staging of GC showed no significant difference. In contrast, in the subgroup analysis for predictors, in the training set, the model that included radiomics had better accuracy than the model with only clinical predictors (F = 3.546, p = 0.037). Additionally, cancer size, depth of cancer invasion and histological differentiation were the three most commonly used features in models built for prediction.

Conclusion

ML has shown to be of excellent diagnostic performance in predicting the LNM of GC. One of the models covering radiomics and its ML algorithms showed good accuracy for the risk of LNM in GC. However, the results revealed some methodological limitations in the development process. Future studies should focus on refining and improving existing models to improve the accuracy of LNM prediction.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022320752

Prognostic value of molecular cytology by one-step nucleic acid amplification (OSNA) assay of peritoneal washings in advanced gastric cancer patients

Peritoneal dissemination is a common form of gastric cancer (GC) recurrence, despite surgery with curative intent. This study aimed to evaluate the prognostic value of intraperitoneal lavage One-Step Nucleic Acid Amplification (OSNA) assay in advanced GC patients. OSNA assay targeting CK-19 mRNA was applied to detect free cancer cells (FCC) in intraperitoneal lavage samples obtained during gastrectomy. A total of 82 GC patients were enrolled to investigate the correlation between OSNA assay and patient's prognosis. Of the 82 patients, OSNA assay was positive in 25 (30.5%) patients. The median OS in OSNA positive patients was significantly lower than in OSNA negative patients (19 vs 45 months). Positive OSNA assay result was a significant unfavourable prognostic factor in both, univariable (HR 3.45, 95% CI 0.95-12.48; p = 0.0030) and multivariable analysis (HR 3.10, 95% CI 1.22-8.54; p = 0.0298). Positive OSNA assay in intraperitoneal lavage is a valuable indicator of poor survival in advanced GC patients after multimodal treatment. After further confirmation on larger sample size, OSNA assay of peritoneal washings could be considered an adjunct tool to conventional cytology, the current gold standard, to provide precise intraoperative staging and additional prognostic information.

Understanding the association between clinical staging accuracy, treatment response, and survival among gastric cancer patients through Bayesian analysis

BACKGROUND

Neoadjuvant therapy (NAT) improves survival among patients with locally advanced gastric cancer (GC), but it remains unclear whether its benefit is contingent on treatment response.

METHODS

This is a national cohort study of stage Ib-III GC patients in the National Cancer Data Base (2006-2015) treated with upfront resection or NAT followed by surgery. Bayesian analysis was used for NAT patients to ascertain staging concordance and to account for down-staging. We used multivariable Cox regression to evaluate the association between staging concordance, treatment, response to NAT, and survival.

RESULTS

The cohort included 13 340 patients treated at 1124 hospitals. Staging concordance ranged from 86.1% for cT3-4N+ to 34.7% for cT2N0 patients. Relative to accurately staged patients treated with upfront surgery, NAT was associated with a decreased risk of death if there was disease down-staging among those with cT1-2N+ (hazard ratio [HR]: 0.43 [0.30-0.61]), cT3-4N0 (HR: 0.69 [0.54-0.88]), and cT3-4N+ (HR: 0.51 [0.48-0.58]) tumors, and in the absence of down-staging among cT3-4N+ patients (HR: 0.83 [0.74-0.92]). Conversely, NAT without down-staging increased the risk of death among those with intermediate-stage disease.

CONCLUSIONS

NAT is associated with improved survival for GC, but it seems to be contingent on treatment response among patients with intermediate-stage disease.

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.

Novel CT based clinical nomogram comparable to radiomics model for identification of occult peritoneal metastasis in advanced gastric cancer

BACKGROUND

Occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients remains a major diagnostic challenge. The aim of this study was to develop novel predictive models for identification of OPM in AGCs.

METHOD

A total of 810 patients with primary AGCs from two hospitals were retrospectively selected and divided into training (n = 393), internal validation (n = 215) and external validation cohorts (n = 202). CT based machine learning models were built and tested to predict the OPM status in AGCs., which are 1) Radiomic signatures: using venous CT imaging features, 2) Clinical models: integrating tumor location, differentiation and extent of serosal exposure, and 3) Radiomics models: combining of radiomic signature, tumor location and tumor differentiation.

RESULT

Total incidence of OPM was 8.27% (67/810). Clinical models yielded comparable classification accuracy with the corresponding radiomics models with similar AUCs (0.902-0.969 vs. 0.896-0.975) while the radiomic signatures showed relatively low AUCs of 0.863-0.976. In the case where the specificity is higher than 90%, the overall sensitivity of clinical model and radiomics model for OPM positive cases was 76.1% (51/67) and 82.1% (55/67). A nomogram based on the logistic clinical model was drawn to facilitate the usage and verification of the clinical model.

CONCLUSION

Both the novel CT based clinical nomogram and radiomics model provide promising method to yield high accuracy in identification of OPM in AGC patients.

Nomograms for Predicting Disease-Free Survival in Patients With Siewert Type II/III Adenocarcinoma of the Esophagogastric Junction Receiving Neoadjuvant Therapy and Radical Surgery

Objective

This study aimed to develop prognostic prediction models for patients with Siewert type II/III adenocarcinoma of the esophagogastric junction (AEG) who received neoadjuvant therapy (neoadjuvant chemoradiotherapy or neoadjuvant chemotherapy) and radical surgery. A baseline nomogram and a post-operative nomogram were constructed before neoadjuvant therapy and after surgery. The predictive performance of the constructed nomograms was internally validated and compared to the TNM staging system.

Materials and Methods

A total of 245 patients diagnosed with Siewert type II/III AEG and treated with neoadjuvant therapy followed by radical surgery at The Fourth Hospital of Hebei Medical University between January 2011 and December 2017 were enrolled. The variables before neoadjuvant therapy were defined as baseline factors, while the variables of baseline factors along with the variables of treatment and postoperative pathology were defined as post-operative factors. To construct the corresponding nomograms, independent predictors of baseline and post-operative factors were identified. The C-index and a time-dependent receiver operating characteristic curve were used to evaluate the model’s discrimination ability. The calibration ability of the model was determined by comparing the probability of predicted free-recurrence to the actual free-recurrence. Decision curve analysis (DCA) was used to determine the clinical usefulness of the nomogram.

Results

Among the baseline factors, age, cT stage, cN stage, Borrmann type, and staging laparoscopy were independent prognostic predictors. In contrast, among the post-operative factors, age, cN stage, staging laparoscopy, ypT stage, clinical response, number of positive lymph nodes, number of negative lymph nodes, laurén classification, and lymphatic, or perineural invasion (VELPI) were independent prognostic predictors. The two nomograms were constructed using the independent predictors of prognosis. The C-indexes for the baseline and post-operative nomograms were 0.690 (95% CI, 0.644-0.736) and 0.817 (95% CI, 0.782-0.853), respectively. The AUCs of the baseline nomogram at 3 and 5 years were both greater than cTNM (73.1 vs 58.8, 76.1 vs 55.7). Similarly, the AUCs of the post-operative nomogram were both greater than ypTNM (85.2 vs 69.1, 88.2 vs 71.3) at 3 and 5 years. The calibration curves indicated that both models had a high degree of calibration ability. By comparing the DCA at 3 and 5 years, we determined that the two nomograms constructed had better clinical utility than the TNM staging system.

Conclusions

The constructed nomograms have a more accurate predictive ability than the eighth edition TNM staging system, which can be useful for treatment selection and follow-up monitoring of patients.

Association between neutrophil-lymphocyte ratio and lymph node metastasis in gastric cancer: A meta-analysis

INTRODUCTION AND AIM

The prognostic role of neutrophil to lymphocyte ratio (NLR) has been explored extensively in the literature. The aim of this meta-analysis was to evaluate the link between NLR and lymph node metastasis in gastric cancer. A method for increasing specificity and sensitivity of pre-treatment staging has implications on treatment algorithms and survival.

SEARCH STRATEGY

The relevant databases were searched as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart. After selection, 12 full text articles that met the inclusion criteria were included for quantitative analysis. 2 × 2 squares were generated using lymph node positive/negative, and NLR high/low data. The effect size for each study was calculated using the DerSimonian-Laird random effects model. P values were calculated using the chi-square method. Finally publication bias was evaluated. All statistics were calculated using R Studio.

RESULTS

Meta-analysis showed a 1.90 times (odds ratio, with 95% CI 1.52-2.38) increase in risk of positive lymph node status with high neutrophil to lymphocyte ratio. This has significant implications for cancer screening and staging, as NLR is a highly reproducible, cost-effective, and widely available prognostic factor for gastric cancer patients. Additionally, high or low NLR values may have implications for management pathways. Patients with lymph node metastasis can be offered neoadjuvant chemotherapy, avoiding salvage therapy in the form of adjuvant chemoradiotherapy, which is poorly tolerated.

CONCLUSION

This meta-analysis shows an association between NLR and positive lymph node status in gastric cancer patients with implications for staging, as well as preoperative personalisation of therapy.

Current and future imaging modalities in gastric cancer

Gastric adenocarcinoma treatment can include endoscopic mucosal resection, surgery, chemotherapy, radiation, and palliative measures depending on staging. Both invasive and noninvasive staging techniques have been used to dictate the best treatment pathway. Here, we review the current imaging modalities used in gastric cancer as well as novel techniques to accurately stage and screen these patients.

Computed tomography features and clinical characteristics of gastritis cystica profunda

Background

The diagnostic evidence of gastritis cystica profunda (GCP) are not adequately described due to its extremely low morbidity. This study aimed to analyze and summarize the comprehensive CT features and clinical characteristics of patients with GCP.

Results

Nineteen patients were enrolled, including eight men and eleven women, with a mean age of 55.53 years. Only one patient had the history of gastric polypectomy. Among the nineteen cases, two cases were in the gastric cardia, four in the gastric fundus, eight in the gastric body and five in the gastric antrum. The shapes were sphere in thirteen patients, hemisphere in five patients and diffuse in one patient. The mean size of eighteen local lesions was 1.63 cm. The cystic changes in submucosa were detected in fifteen patients. Compared with the pancreas, most GCP lesions were hypo-attenuated on unenhanced CT (n = 8), in arterial phase (AP) (n = 17) and venous phase (VP) (n = 11). Fifteen patients had the peak enhancement in VP and two in AP. The rim-like enhancement with central low attenuation was clearly observed in thirteen patients. For the GCP accompanied by adenocarcinoma, the enhancement peak was present in AP and the gradual expansion of enhancement area was in VP. All patients underwent surgical or endoscopic resection. Sixteen cases had remission of symptoms and no recurrence.

Conclusions

The careful analysis of CT features and clinical characteristics can provide support for deepening the understanding of the GCP. However, a more accurate diagnosis depends on histopathological features.