Advanced Gastric Cancer Metastasis Research Articles

Dual-energy CT quantitative parameters to evaluate occult peritoneal metastasis in advanced gastric cancer preoperatively.

To explore whether dual-energy CT (DECT) quantitative parameters could provide analytic value for the diagnosis of patients with occult peritoneal metastasis (OPM) in advanced gastric cancer preoperatively. This retrospective study included 219 patients with advanced gastric cancer and DECT scans. The patient's clinical data and DECT related iodine concentration (IC) parameters and effective atomic number (Zeff) were collated and analyzed among noun-peritoneal metastasis (NPM), OPM and radiologically peritoneal metastasis (RPM) groups. The predictive performance of the DECT parameters was compared with that of the conventional CT features and clinical characteristics through evaluating area under curve of the precision-recall (AUC-PR), F1 score, balanced accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Borrmann IV type diagnosed on CT and serum tumor indicator CA125 index were statistically different between the NPM and OPM groups. DECT parameters included IC, normalized IC (NIC), and Zeff of PM group were lower than the NPM group. The DECT predictive nomogram combined three independent DECT parameters produced a better diagnostic performance than the conventional CT feature Borrmann IV type and serum CA125 index in AUC-PR with 0.884 vs 0.368 vs 0.189, but similar to the combined indicator which was based on the DECT parameters, the conventional CT feature, and serum CA125 index in AUC-PR with 0.884 vs 0.918. The lower quantitative NIC, IC ratio, and Zeff on DECT was associated with peritoneal metastasis in advanced gastric cancer and was promising to identify patients with OPM noninvasively.

Machine learning-based CT radiomics approach for predicting occult peritoneal metastasis in advanced gastric cancer preoperatively

PurposeTo develop a machine learning-based CT radiomics model to preoperatively diagnose occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients. MethodsA total of 177 AGC patients were retrospectively analyzed. Four region of interests (ROIs) along the largest area of tumor (core ROI) and corresponding tumor mesenteric fat space (peri ROI) were manually delineated on the arterial (A-core and A-peri) and venous phase (V-core and V-peri) of CT images. A total of 1316 radiomics features were extracted from each ROI. Then, ten machine learning classification algorithms were used to develop model. An integrated radiomics nomogram was established to predict OPM individually. ResultsFor the radiomics of tumor mesenteric fat space, the AUCs of A-peri in training set and test set were 0.881 and 0.800. And the AUCs of V-peri were 0.838 and 0.815, respectively. In terms of primary tumor’ s radiomics signature, the AUCs of A-core in training set and test set were 0.862 and 0.691. The AUCs of V-core were 0.831 and 0.620. Integrated radiomics model showed the highest AUC value compare to each single radiomics score in the training (0.943 vs 0.831-0.881) and test set (0.835 vs 0.620-0.815). Radiomics nomogram demonstrated good diagnostic accuracy with a C-index of 0.948. ConclusionsBoth the radiomics of tumor mesenteric fat space and primary tumor were associated with OPM. A CT radiomics nomogram had a relatively good predictive performance for detecting OPM in patients with AGC.

ASO Author Reflections: Preoperative Noninvasive Prediction of Peritoneal Metastasis in Advanced Gastric Cancer: A 18F-FDG PET/CT Radiomics-Based Multimodality Fusion Model.

ASO Author Reflections: Preoperative Noninvasive Prediction of Peritoneal Metastasis in Advanced Gastric Cancer: A 18F-FDG PET/CT Radiomics-Based Multimodality Fusion Model.

18F-FDG PET/CT Radiomics-Based Multimodality Fusion Model for Preoperative Individualized Noninvasive Prediction of Peritoneal Metastasis in Advanced Gastric Cancer.

This study was designed to develop and validate a machine learning-based, multimodality fusion (MMF) model using 18F-fluorodeoxyglucose (FDG) PET/CT radiomics and kernelled support tensor machine (KSTM), integrated with clinical factors and nuclear medicine experts' diagnoses to individually predict peritoneal metastasis (PM) in advanced gastric cancer (AGC). A total of 167 patients receiving preoperative PET/CT and subsequent surgery were included between November 2006 and September 2020 and were divided into a training and testing cohort. The PM status was confirmed via laparoscopic exploration and postoperative pathology. The PET/CT signatures were constructed by classic radiomic, handcrafted-feature-based model and KSTM self-learning-based model. The clinical nomogram was constructed by independent risk factors for PM. Lastly, the PET/CT signatures, clinical nomogram, and experts' diagnoses were fused using evidential reasoning to establish the MMF model. The MMF model showed excellent performance in both cohorts (area under the curve [AUC] 94.16% and 90.84% in training and testing), and demonstrated better prediction accuracy than clinical nomogram or experts' diagnoses (net reclassification improvement p < 0.05). The MMF model also had satisfactory generalization ability, even in mucinous adenocarcinoma and signet ring cell carcinoma which have poor uptake of 18F-FDG (AUC 97.98% and 89.71% in training and testing). The 18F-FDG PET/CT radiomics-based MMF model may have significant clinical implications in predicting PM in AGC, revealing that it is necessary to combine the information from different modalities for comprehensive prediction of PM.

MUC16 as a serum-based prognostic indicator of prometastatic gastric cancer

Metastatic gastric cancer (GC) presents significant clinical challenges due to its poor prognosis and limited treatment options. To address this, we conducted a targeted protein biomarker discovery study to identify markers predictive of metastasis in advanced GC (AGC). Serum samples from 176 AGC patients (T stage 3 or higher) were analyzed using the Olink Proteomics Target panels. Patients were retrospectively categorized into nonmetastatic, metastatic, and recurrence groups, and differential protein expression was assessed. Machine learning and gene set enrichment analysis (GSEA) methods were applied to discover biomarkers and predict prognosis. Four proteins (MUC16, CAIX, 5’-NT, and CD8A) were significantly elevated in metastatic GC patients compared to the control group. Additionally, GSEA indicated that the response to interleukin-4 and hypoxia-related pathways were enriched in metastatic patients. Random forest classification and decision-tree modeling showed that MUC16 could be a predictive marker for metastasis in GC patients. Additionally, ELISA validation confirmed elevated MUC16 levels in metastatic patients. Notably, high MUC16 levels were independently associated with metastatic progression in T3 or higher GC. These findings suggest the potential of MUC16 as a clinically relevant biomarker for identifying GC patients at high risk of metastasis.

N6-methyladenosine-modified MIB1 promotes stemness properties and peritoneal metastasis of gastric cancer cells by ubiquitinating DDX3X.

Peritoneal metastasis (PM), one of the most typical forms of metastasis in advanced gastric cancer (GC), indicates a poor prognosis. Exploring the potential molecular mechanism of PM is urgently necessary, as it has not been well studied. E3 ubiquitin ligase has been widely established to exert a biological function in various cancers, but its mechanism of action in GC with PM remains unknown. The effect of MIB1 on PM of GC was confirmed in vitro and in vivo. Co-immunoprecipitation (Co-IP) and mass spectrometry demonstrated the association between MIB1 and DDX3X. Western blot, flow cytometry and immunofluorescence determined that DDX3X was ubiquitylated by MIB1 and promoted stemness. We further confirmed that METTL3 promoted the up-regulation of MIB1 by RNA immunoprecipitation (RIP), luciferase reporter assay and other experiments. We observed that the E3 ubiquitin ligase Mind bomb 1 (MIB1) was highly expressed in PMs, and patients with PM with high MIB1 expression showed a worse prognosis than those with low MIB1 expression. Mechanistically, our study demonstrated that the E3 ubiquitin ligase MIB1 promoted epithelial-mesenchymal transition (EMT) progression and stemness in GC cells by degrading DDX3X. In addition, METTL3 mediated m6A modification to stabilize MIB1, which required the m6A reader IGF2BP2. Our study elucidated the specific molecular mechanism by which MIB1 promotes PM of GC, and suggested that targeting the METTL3-MIB1-DDX3X axis may be a promising therapeutic strategy for GC with PM.

Peritoneal Washing Cytology Positivity in Gastric Cancer: Role of Lymph Node Metastasis as a Risk Factor.

Peritoneal washing cytology (PWC) is a widely used diagnostic tool for detecting peritoneal metastasis of advanced gastric cancer. However, the prognosis of patients with positive PWC remains poor even after gastrectomy, and treatments vary among institutions and eras. In this study, we identified the clinical factors that can help predict cytology-positive (CY(+)) gastric cancer. We retrospectively reviewed the national data of patients with gastric cancer from 2019, as provided by the Information Committee of the Korean Gastric Cancer Association. Of the 13,447 patients with gastric cancer, 3,672 underwent PWC. Based on cytology results, we analyzed the clinicopathological characteristics and assessed the possibility of CY(+) outcomes in relation to T and N stages. Of the 3,270 patients who underwent PWC without preoperative chemotherapy, 325 were CY(+), whereas 2,945 were negative. CY(+) was more commonly observed in patients with Borrmann type IV gastric cancer, an undifferentiated histological type, and advanced pathological stages. Multivariate analysis revealed Borrmann type IV (odds ratio [OR], 1.821), tumor invasion to T3-4 (OR, 2.041), and lymph node metastasis (OR, 3.155) as independent predictors of CY(+). Furthermore, for circular tumor location, the N stage emerged as a significant risk factor for CY(+), particularly when the tumor was located on the posterior wall (PW) side. Lymph node metastasis significantly affects CY(+) outcomes, particularly when the tumor is located on the PW side. Therefore, PWC should be considered not only in suspected serosal exposure cases but also in cases of lymph node metastasis.

Establishment and verification of prediction model of occult peritoneal metastasis in advanced gastric cancer

BackgroundTo investigate the risk factors associated with the development of occult peritoneal metastasis in advanced gastric cancer, and establish and externally validate a nomogram for predicting the occurrence of occult peritoneal metastasis in patients with advanced gastric cancer.MethodsA total of 111 patients with advanced gastric cancer who underwent laparoscopic exploration or peritoneal lavage cytology examination at the Affiliated Drum Tower Hospital of Nanjing University Medical School from August 2014 to December 2021 were retrospectively analyzed. The patients diagnosed between 2019 and 2021 were assigned to the training set (n = 64), while those diagnosed between 2014 and 2016 constituted the external validation set (n = 47). In the training set, patients were classified into two groups based on preoperative imaging and postoperative pathological data: the occult peritoneal metastasis group (OPMG) and the peritoneal metastasis negative group (PMNG). In the validation set, patients were classified into the occult peritoneal metastasis group (CY1P0, OPMG) and the peritoneal metastasis negative group (CY0P0, PMNG) based on peritoneal lavage cytology results. A nomogram was constructed using univariate and multivariate analyses. The performance of the nomogram was evaluated using Harrell’s C-index, the area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and calibration plots.ResultsThis study analyzed 22 potential variables of OPM in 111 gastric cancer patients who underwent laparoscopic exploration or peritoneal lavage cytology examination. Logistic regression analysis results showed that Lauren classification, CLDN18.2 score and CA125 were independent risk factors for OPM in patients with gastric cancer. We developed a simple and easy-to-use prediction nomogram of occult peritoneal metastasis in advanced gastric cancer. This nomogram had an excellent diagnostic performance. The AUC of the bootstrap model in the training set was 0.771 and in the validation set was 0.711. This model showed a good fitting and calibration and positive net benefits in decision curve analysis. ConclusionWe have developed a prediction nomogram of OPM for gastric cancer. This novel nomogram has the potential to enhance diagnostic accuracy for occult peritoneal metastasis in gastric cancer patients.

Simultaneous colonic metastasis of advanced gastric cancer: a case report

BackgroundAlthough distant metastasis in gastric cancer can be present at the time of the initial diagnosis, colonic metastasis is extremely rare. This report describes a case of simultaneous colonic metastasis of advanced gastric cancer.Case presentationThe patient was a 78-year-old woman with nausea and epigastric pain. Upper gastrointestinal endoscopy revealed an advanced invasive ulcerative tumor in the lesser curvature of the stomach extending from the anterior to the middle portion. Colonoscopy revealed a 4-mm polyp-like lesion in the mid-transverse colon; therefore, a polypectomy was performed. Both gastric and colonic tumors showed poorly differentiated adenocarcinoma with signet ring cell carcinoma. After providing informed consent, the patient underwent a total gastrectomy. Histologic examination showed similar morphologic features of both gastric and colonic tumors. Immunohistochemistry staining showed that these tumor cells were positive for cytokeratin (CK) 7 and negative for CK20.ConclusionsThis was an extremely rare case of simultaneous colonic metastasis of advanced gastric cancer. Because missed metastasis can result in a poorer prognosis, we propose a systemic search including colonoscopy for patients with advanced gastric cancer, especially cases involving poorly differentiated adenocarcinoma or signet ring cell carcinoma.

Application of Circulating Tumor Cells and Interleukin-6 in Preoperative Prediction of Peritoneal Metastasis of Advanced Gastric Cancer.

The purpose of this study was to explore the clinical significance of circulating tumor cells (CTCs) and cytokines in peripheral blood in preoperative prediction of peritoneal metastasis (PM) in advanced gastric cancer (AGC). The clinicopathological characteristics of 282 patients with AGC were retrospectively analyzed. The patients were divided into training and validation groups according to the time of receiving treatment. We used univariate analysis and multivariate logistic regression analysis to screen out the independent risk factors of PM in AGC. Then, we incorporated independent risk factors into the nomogram, and evaluated the discriminative ability. The levels of CTCs and interleukin-6 (IL-6) of AGC patients with PM were higher than those without PM (P<0.05). Moreover, the levels of CTCs and IL-6 in the occult peritoneal metastasis (OPM) group and the CT-positive PM group were higher than those in the negative PM (P<0.05). Multivariate logistic regression analysis showed that IL-6 > 12.22 pg/mL, CTCs > 4/5mL, CA724 > 6 IU/mL, CA125 > 35 U/mL and tumor size > 5 cm were independent risk factors for PM of AGC. The area under the ROC curve of the nomogram were 0.898 and 0.926 in the training and validation sets, respectively. The clinical decision curve showed that the nomogram had good clinical utility. CTCs and IL-6 in peripheral blood are promising biomarkers for predicting the risk of PM in AGC. The nomogram constructed from five risk factors can effectively assess the risk of PM in AGC patients individually.

Discoidin domain receptor 1 is a potential target correlated with tumor invasion and immune infiltration in gastric cancer.

Discoidin domain receptor 1 (DDR1) has been demonstrated to be able to promote tumor invasion and metastasis and being closely related to tumor immune infiltration. However, DDR1 has rarely been studied in gastric cancer. Here, we primarily evaluated DDR1 expression in gastric cancer and its cell lines using multiple databases. Subsequently, the cancer prognosis was investigated in relation to DDR1 expression. After analysis, we discovered that DDR1 was highly expressed and significantly connected with poor prognosis in gastric cancer. To comprehensively understand the molecular mechanism of DDR1, we explored genes and proteins interacting with DDR1 in gastric cancer using databases. Additionally, we found that the expression level of DDR1 was inversely correlated with immune infiltration and significantly relative to various immune cell markers. Overall, DDR1 was implicated in invasion, metastasis, and immune infiltration of gastric cancer. Inhibition of DDR1 may have the potential to alleviate the strong invasiveness and metastasis of advanced gastric cancer. Meanwhile, immune exclusion by DDR1 may also provide a new strategy for improving the efficacy of immune checkpoints inhibitors (ICIs), such as programmed cell death protein 1 (PD-1) antibody.

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

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

Molecular Mechanisms and Potential Rationale of Immunotherapy in Peritoneal Metastasis of Advanced Gastric Cancer.

Peritoneal metastasis (PM) is one of the most frequent metastasis patterns of gastric cancer (GC), and the prognosis of patients with PM is very dismal. According to Paget’s theory, disseminated free cancer cells are seeded and survive in the abdominal cavity, adhere to the peritoneum, invade the subperitoneal tissue, and proliferate through angiogenesis. In these sequential processes, several key molecules are involved. From a therapeutic point of view, immunotherapy with chemotherapy combination has become the standard of care for advanced GC. Several clinical trials of newer immunotherapy agents are ongoing. Understanding of the molecular process of PM and the potential rationale of immunotherapy for PM treatment is necessary. Beyond understanding of the molecular aspect of PM, many studies have been conducted on the modality of treatment of PM. Notably, intraperitoneal approaches, including chemotherapy or immunotherapy, have been conducted, because systemic treatment of PM has limitations. In this study, we reviewed the molecular mechanisms and immunologic aspects of PM, and intraperitoneal approaches under investigation for treating PM.

Integration of Genomic Biology Into Therapeutic Strategies of Gastric Cancer Peritoneal Metastasis.

The peritoneum is a common site of metastasis in advanced gastric cancer (GC). Diagnostic laparoscopy is now routinely performed as part of disease staging, leading to an earlier diagnosis of synchronous peritoneal metastasis (PM). The biology of GCPM is unique and aggressive, leading to a dismal prognosis. These tumors tend to be resistant to traditional systemic therapy, and yet, this remains the current standard-of-care recommended by most international clinical guidelines. As this is an area of unmet clinical need, several translational studies and clinical trials have focused on addressing this specific disease state. Advances in genomic sequencing and molecular profiling have revealed several promising therapeutic targets and elucidated novel biology, particularly on the role of the surrounding tumor microenvironment in GCPM. Peritoneal-specific clinical trials are being designed with a combination of locoregional therapeutic strategies with systemic therapy. In this review, we summarize the new knowledge of cancer biology, advances in surgical techniques, and emergence of novel therapies as an integrated strategy emerges to address GCPM as a distinct clinical entity.

Deep Convolutional Neural Network Based on Computed Tomography Images for the Preoperative Diagnosis of Occult Peritoneal Metastasis in Advanced Gastric Cancer.

We aimed to develop a deep convolutional neural network (DCNN) model based on computed tomography (CT) images for the preoperative diagnosis of occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC). A total of 544 patients with AGC were retrospectively enrolled. Seventy-nine patients were confirmed with OPM during surgery or laparoscopy. CT images collected during the initial visit were randomly split into a training cohort and a testing cohort for DCNN model development and performance evaluation, respectively. A conventional clinical model using multivariable logistic regression was also developed to estimate the pretest probability of OPM in patients with gastric cancer. The DCNN model showed an AUC of 0.900 (95% CI: 0.851–0.953), outperforming the conventional clinical model (AUC = 0.670, 95% CI: 0.615–0.739; p < 0.001). The proposed DCNN model demonstrated the diagnostic detection of occult PM, with a sensitivity of 81.0% and specificity of 87.5% using the cutoff value according to the Youden index. Our study shows that the proposed deep learning algorithm, developed with CT images, may be used as an effective tool to preoperatively diagnose OPM in AGC.

The value of spectral CT-based radiomics in preoperative prediction of lymph node metastasis of advanced gastric cancer

Objective: To investigate the spectral CT-based radiomics in predicting preoperatively the lymph node metastasis (LNM) of advanced gastric cancer. Methods: From January 2014 to October 2018, the spectral CT imaging and clinical data of 196 gastric adenocarcinoma patients confirmed by pathology in the First Affiliated Hospital of Zhengzhou University were retrospectively enrolled (training set and test set were randomly divided according to the ratio of 1∶1). These 196 patients include143 males and 53 females, aged from 28 to 81 years, with an average age of (59±11) years, and were divided into nodular metastasis group and non-metastasis group according to clinicopathological data. The spectral parameters were measured and calculated, and the CT-reported lymph node (LN) status from CT images were obtained. 273 radiomics features were extracted from the dual-phases CT images in different energy level (40, 65 and 100 keV) to build the radiomics signature respectively. Univariate analysis was used to compare the differences of spectral parameters and radiomics features between two groups, and then the significant indicators were put into multivariable logistic regression analysis to construct combined prediction model and radiomics nomogram. In addition, the performance of prediction model in training and test set were measured using the receiver operating characteristics (ROC) curves and were compared using DeLong test. Results: Both in training set and in test set, the iodine concentration (IC) of tumor in venous phase (VP) in nodular metastasis group were higher than that in non-metastasis group [training set: 22.98 (100 mg/L)>20.31 (100 mg/L), P=0.086; test set: 25.14 (100 mg/L)>21.07 (100 mg/L), P=0.009]. The CT-reported LN status showed significant differences between the two group (P<0.001, P=0.001). The radiomics signatures 40 keV-arterial phase, 65 keV-venous phase, IC-VP of tumor and CT-reported LN status were independent indicators for prediction of preoperative LNM of advanced gastric cancer in combined prediction model (P<0.05). The radiomics nomogram predicated LNM with an area under curve (AUC) and 95% confidence interval (CI) of 0.822 (0.739-0.906) in training set and 0.819(0.732-0.906) in test set, and there were no significant differences in AUC between two sets (P>0.05). Conclusions: The spectral CT-based radiomics can be used to quantitatively predict the LNM of advanced gastric cancer preoperatively.

Diagnostic ability of multi-detector spiral computed tomography for pathological lymph node metastasis of advanced gastric cancer.

BACKGROUNDThe reliability of preoperative nodal diagnosis of advanced gastric cancer by multi-detector spiral computed tomography (MDCT) is still unclear.AIMTo examine the diagnostic ability of MDCT more precisely by using data on intranodal pathological metastatic patterns.METHODSA total of 108 patients with advanced gastric cancer who underwent MDCT and curative gastrectomy at Kanazawa Medical University Hospital were enrolled in this study. The nodal sizes measured on computed tomography (CT) images were compared with the pathology results. A receiver-operating characteristic curve was constructed, from which the critical value (CV) was calculated by using the data of the first 69 patients retrospectively. By using the CV, sensitivity and specificity were calculated with prospectively collected data from 39 consecutive patients. This enabled a more precise one-to-one correspondence of lymph nodes between CT and pathological examination by using the size data of lymph node mapping. The intranodal pathological metastatic patterns were classified into the following four types: Small nodular, peripheral, large nodular, and diffuse.RESULTSAlthough all the cases were clinically suspected as having metastasis, 81 had lymph node metastasis and 27 had no metastasis. The number of dissected, detected on CT, and metastatic nodes were, 4241, 897, and 801, respectively. The CV obtained from the receiver-operating characteristic was 7.6 mm for the long axis. The sensitivity was 91.4% and the specificity was 47.3% in the prospective phase. The large nodular and diffuse metastases were easy to diagnose because metastatic nodes with a large axis often exhibit these forms.CONCLUSIONThe ability of MDCT to contribute to a nodal diagnosis of advanced gastric cancer was examined prospectively with precise size data from node mapping, using a CV of 7.6 mm for the long axis that was calculated from the retrospectively collected data. The sensitivity was as high as 91%, and would be improved when referring to the enhanced patterns. However, its specificity was as low as 47%, because most of metastatic nodes in gastric cancer being small in size. The small nodular or peripheral type metastatic nodes were often small and considered difficult to diagnose.

Influential factors and prognostic analysis of blood vessel invasion in advanced gastric cancer

The purpose of this study was to analyze the influencing factors of BVI in advanced gastric cancer and explore the factors affecting the prognosis of advanced gastric cancer, so as to accurately evaluate the disease status and enable patients to receive effective treatment. We retrospectively analyzed 622 cases with complete data and successful follow-up. BVI was found in 144 of the 622 patients with advanced gastric cancer, with a detection rate of 23.15%. BVI was closely related to the differentiation degree, infiltration depth and lymph node metastasis of advanced gastric cancer, (P < 0.05). Gender, age, tumor location, tumor size, Lauren classification, tumor M stage, and clinical TNM stage were not the influencing factors of BVI in patients with advanced gastric cancer (P > 0.05). The 5-year survival rate of patients in the positive group of BVI was 34.72%. The 5-year survival rate of patients with advanced gastric cancer was correlated with BVI, Lauren classification, depth of invasion, lymph node metastasis, and clinical TNM staging, (P < 0.05). The 5-year survival rate was independent of gender, age, tumor location, tumor size, tumor tissue differentiation, and M stage (P > 0.05). The results of multi-factor analysis showed that BVI, N stage and clinical TNM stage were independent predictors of prognosis in patients with advanced radical gastric cancer. By analyzing the stage and related prognostic factors of resectable advanced gastric cancer, we found that BVI was not only closely related to lymph node metastasis, but also an independent predictor of prognosis of advanced gastric cancer. As this study was only a single-center retrospective study, there may be a selective bias in clinical data. So large-scale and multi-center collaboration is needed to further explore the influencing factors of BVI in the progression of gastric cancer.

Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer.

To evaluate the predictive value of CT radiomics features derived from the primary tumor in discriminating occult peritoneal metastasis (PM) in advanced gastric cancer (AGC). Preoperative CT images of 233 patients with AGC were retrospectively analyzed. The region of interest (ROI) was manually drawn along the margin of the lesion on the largest slice of venous CT images, and a total of 539 quantified features were extracted automatically. The intra-class correlation coefficient (ICC) and the absolute correlation coefficient (ACC) were calculated for selecting influential features. A multivariate logistic regression model was constructed based on the training cohort, and the testing cohort validated the reliability of the model. Additionally, another model based on the preoperative clinic-pathological features was also developed. The comparison of the diagnostic performance between the two models was performed using ROC analysis and the Akaike information criterion (AIC) value. Six radiomics features (ID_Energy, LoG(0.5)_Energy, Compactness2, Max Diameter, Orientation, and Surface Area Density) differed significantly between AGCs with and without PM and performed well in distinguishing AGCs with PM from those without PM in the primary cohort (AUC = 0.618-0.658). The radiomics model showed a higher AUC value than each single radiomics feature in the primary cohort (0.741 vs. 0.618-0.658) and similar diagnosis performance in the validation cohort. The radiomics model showed slightly worse diagnostic efficacy than the clinic-pathological model (AUC, 0.724 vs. 0.762). Venous CT radiomics analysis based on the primary tumor provided valuable information for predicting occult PM in AGCs. • Venous CT radiomics analysis provided valuable information for predicting occult peritoneal metastases in advanced gastric cancer. • CT-based T stage was an independent predictive factor of occult peritoneal metastases in advanced gastric cancer. • A radiomics model showed slightly worse diagnostic efficacy than a clinic-pathological model.

The ctDNA in peritoneal effusion of advanced gastric cancer for auxiliary diagnosis of peritoneal metastasis.

e15516 Background: Peritoneal metastasis (PM) is the most frequent mode of distant metastasis in advanced gastric cancer (GC). Most PM could cause peritoneal effusion. However, the cytological examination of cancer cells in peritoneal effusion is less sensitive to the identification of PM early; the specificity of serum biomarkers in peritoneal effusion is poor. It is necessary to assess the feasibility of peritoneal effusion circulating tumor DNA (ctDNA) for the PM diagnosis. Methods: 22 GCs with clinically confirmed PM as positive subjects and 11 non-cancers without PM as negative control were recruited. GC tissues were collected from 16 positive subjects. Peritoneal effusion supernatant (AS), peritoneal effusion cells (AC), and plasma were collected from each subject. Next generation sequencing (NGS) was performed using a 1021-gene panel. Sensitivity and specificity for the diagnosis of GC with PM were assessed. Results: Median age of 22 GCs with PM was 57 yrs. 12 patients were male. Diffused and mixed-type accounted for 73%. For 45% of patients, primary tumor was found in gastric body. In tissue samples, recurrent genes were TP53 (63%), CDH1 (50%), RHOA (19%), TGFBR1 (19%), KRAS (13%), and PIK3CA (13%). Private and tumor-derived mutations were found in the AS, AC, and plasma. 95% of (21/22), 82% (18/22), and 77% (17/22) of AS, AC, and plasma samples carried mutations, respectively. AS had more mutations than AC and plasma (Wilcoxon’s matched-pairs signed-ranked test, AS vs AC, p= 0.006; AS vs plasma, p = 0.013). 98% of the AC and 80% plasma mutations were contained in AS, Variant allele frequency (VAF) in AS was higher than others (Mann Whitney test, AS vs AC, p= 0.038; AS vs plasma, p&lt; 0.001). In 22 GCs with PM, 3 were identified with no cancer cells in AS using cytological examination. Of these, 3 were detected with mutations in AS, one with mutations in AC. Mutations were detected in only one negative control AS and AC. Receiver operating characteristic (ROC) curve showed the number of mutations was related to PM (AS: AUC 0.93, 95% CI 0.82-1.00, p&lt; 0.001; AC: AUC 0.85, 95% CI 0.70-1.00, p= 0.001). When the threshold was determined with one mutation, the sensitivity and specificity were 95% and 91% in AS, 82% and 91% in AC, respectively. Conclusions: Peritoneal effusion ctDNA contained tumor-derived and specific mutations. Peritoneal effusion ctDNA can be used for the auxiliary diagnosis of PM.