Status Of Gastric Cancer Patients Research Articles

Nomogram based on dual-energy CT-derived extracellular volume fraction for the prediction of microsatellite instability status in gastric cancer

PurposeTo develop and validate a nomogram based on extracellular volume (ECV) fraction derived from dual-energy CT (DECT) for preoperatively predicting microsatellite instability (MSI) status in gastric cancer (GC).Materials and methodsA total of 123 patients with GCs who underwent contrast-enhanced abdominal DECT scans were retrospectively enrolled. Patients were divided into MSI (n=41) and microsatellite stability (MSS, n=82) groups according to postoperative immunohistochemistry staining, then randomly assigned to the training (n=86) and validation cohorts (n=37). We extracted clinicopathological characteristics, CT imaging features, iodine concentrations (ICs), and normalized IC values against the aorta (nICs) in three enhanced phases. The ECV fraction derived from the iodine density map at the equilibrium phase was calculated. Univariate and multivariable logistic regression analyses were used to identify independent risk predictors for MSI status. Then, a nomogram was established, and its performance was evaluated by ROC analysis and Delong test. Its calibration performance and clinical utility were assessed by calibration curve and decision curve analysis, respectively.ResultsThe ECV fraction, tumor location, and Borrmann type were independent predictors of MSI status (all P < 0.05) and were used to establish the nomogram. The nomogram yielded higher AUCs of 0.826 (0.729–0.899) and 0.833 (0.675–0.935) in training and validation cohorts than single variables (P<0.05), with good calibration and clinical utility.ConclusionsThe nomogram based on DECT-derived ECV fraction has the potential as a noninvasive biomarker to predict MSI status in GC patients.

Assessing Nutritional Status in Gastric Cancer Patients after Total versus Subtotal Gastrectomy: Cross-Sectional Study.

Gastric cancer (GC) remains a significant global health concern, ranking as the third leading cause of cancer-related deaths. Malnutrition is common in GC patients and can negatively impact prognosis and quality of life. Understanding nutritional issues and their management is crucial for improving patient outcomes. This cross-sectional study included 51 GC patients who underwent curative surgery, either total or subtotal gastrectomy. Various nutritional assessments were conducted, including anthropometric measurements, laboratory tests, and scoring systems such as Eastern Cooperative Oncology Group/World Health Organization Performance Status (ECOG/WHO PS), Observer-Reported Dysphagia (ORD), Nutritional Risk Screening-2002 (NRS-2002), Patient-Generated Subjective Global Assessment (PG-SGA), and Simplified Nutritional Appetite Questionnaire (SNAQ). Serum carcinoembryonic antigen (CEA) levels were significantly higher in the subtotal gastrectomy group. Nutritional assessments indicated a higher risk of malnutrition in patients who underwent total gastrectomy, as evidenced by higher scores on ORD, NRS-2002, and PG-SGA. While total gastrectomy was associated with a higher risk of malnutrition, no single nutritional parameter emerged as a strong predictor of surgical approach. PG-SGA predominantly identified malnutrition, with its occurrence linked to demographic factors such as female gender and age exceeding 65 years.

Prognostic nutritional index in predicting survival of patients with gastric or gastroesophageal junction adenocarcinoma: A systematic review.

Gastric cancer is the third most common cause of cancer related death worldwide. Surgery with or without chemotherapy is the most common approach with curative intent; however, the prognosis is poor as mortality rates remain high. Several indexes have been proposed in the past few years in order to estimate the survival of patients undergoing gastrectomy. The preoperative nutritional status of gastric cancer patients has recently gained attention as a factor that could affect the postoperative course and various indexes have been developed. The aim of this systematic review was to assess the role of the prognostic nutritional index (PNI) in predicting the survival of patients with gastric or gastroesophageal adenocarcinoma who underwent gastrectomy with curative intent. To investigate the role of PNI in predicting the survival of patients with gastric or gastroesophageal junction adenocarcinoma. A thorough literature search of PubMed and the Cochrane library was performed for studies comparing the overall survival (OS) of patients with gastric or gastroesophageal cancer after surgical resection depending on the preoperative PNI value. The PRISMA algorithm was used in the screening process and finally 16 studies were included in this systematic review. The review protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO). Sixteen studies involving 14551 patients with gastric or esophagogastric junction adenocarcinoma undergoing open or laparoscopic or robotic gastrectomy with or without adjuvant chemotherapy were included in this systematic review. The patients were divided into high- and low-PNI groups according to cut-off values that were set according to previous reports or by using receiver operating characteristic curve analysis in each individual study. The 5-year OS of patients in the low-PNI groups ranged between 39% and 70.6%, while in the high-PNI groups, it ranged between 54.9% and 95.8%. In most of the included studies, patients with high preoperative PNI showed statistically significant better OS than the low PNI groups. In multivariate analyses, low PNI was repeatedly recognised as an independent prognostic factor for poor survival. According to the present study, low preoperative PNI seems to be an indicator of poor OS of patients undergoing gastrectomy for gastric or gastroesophageal cancer.

Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning.

Lymphovascular invasion (LVI) and perineural invasion (PNI) are important prognostic factors for gastric cancer (GC) that indicate an increased risk of metastasis and poor outcomes. Accurate preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment decisions. However, prior models using conventional computed tomography (CT) images to predict LVI or PNI separately have had limited accuracy. Spectral CT provides quantitative enhancement parameters that may better capture tumor invasion. We hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC patients. To develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status accurately. This study used a retrospective dataset involving 257 GC patients (training cohort, n = 172; validation cohort, n = 85). First, several clinical indicators, including serum tumor markers, CT-TN stages and CT-detected extramural vein invasion (CT-EMVI), were extracted, as were quantitative spectral CT parameters from the delineated tumor regions. Next, a two-step feature selection approach using correlation-based methods and information gain ranking inside a 10-fold cross-validation loop was utilized to select informative clinical and spectral CT parameters. A logistic regression (LR)-based nomogram model was subsequently constructed to predict LVI/PNI status, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC). In both the training and validation cohorts, CT T3-4 stage, CT-N positive status, and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant (P < 0.05). LR analysis of the training group showed preoperative CT-T stage, CT-EMVI, single-energy CT values of 70 keV of venous phase (VP-70 keV), and the ratio of standardized iodine concentration of equilibrium phase (EP-NIC) were independent influencing factors. The AUCs of VP-70 keV and EP-NIC were 0.888 and 0.824, respectively, which were slightly greater than those of CT-T and CT-EMVI (AUC = 0.793, 0.762). The nomogram combining CT-T stage, CT-EMVI, VP-70 keV and EP-NIC yielded AUCs of 0.918 (0.866-0.954) and 0.874 (0.784-0.936) in the training and validation cohorts, which are significantly higher than using each of single independent factors (P < 0.05). The study found that using portal venous and EP spectral CT parameters allows effective preoperative detection of LVI/PNI in GC, with accuracy boosted by integrating clinical markers.

A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer.

Purpose: To evaluate the potential of machine learning (ML)-based radiomics approach for predicting tumor mutation burden (TMB) in gastric cancer (GC). Methods: The contrast enhanced CT (CECT) images with corresponding clinical information of 256GC patients were retrospectively collected. Patients were separated into training set (n = 180) and validation set (n = 76). A total of 3,390 radiomics features were extracted from three phases images of CECT. The least absolute shrinkage and selection operator (LASSO) model was used for feature screening. Seven machine learning (ML) algorithms were employed to find the optimal classifier. The predictive ability of radiomics model (RM) was evaluated with receiver operating characteristic. The correlation between RM and TMB values was evaluated using Spearman's correlation coefficient. The explainability of RM was assessed by the Shapley Additive explanations (SHAP) method. Results: Logistic regression algorithm was chosen for model construction. The RM showed good predictive ability of TMB status with AUCs of 0.89 [95% confidence interval (CI): 0.85-0.94] and 0.86 (95% CI: 0.74-0.98) in the training and validation sets. The correlation analysis revealed a good correlation between RM and TMB levels (correlation coefficient: 0.62, p < 0.001). The RM also showed favorable and stable predictive accuracy within the cutoff value range 6-16 mut/Mb in both sets. Conclusion: The ML-based RM offered a promising image biomarker for predicting TMB status in GC patients.

Liquid biopsy based HER2 amplification status in gastric cancer patients indicates clinical response

Gastric carcinomas are among the most common cancers in Germany, with approximately 18,000 new cases per year.About 10 years ago, based on results of the Trastuzumab for gastric cancer (ToGA) trial, the addition of the monoclonal antibody trastuzumab to a platinum-fluoropyrimidine chemotherapy backbone became the standard-of-care 1st-line therapy for human epidermal growth factor receptor 2 (HER2)-positive gastric cancers. Only patients with primary HER2 gene amplification benefit from this therapy. Thus, accurate HER2 gene amplification detection is predictive and critical for therapy selection. As a gold standard the HER2 status is currently determined in tumor tissue specimens using immune histochemistry and fluorescent in situ hybridisation. However, HER2 amplification is detectable in only about 20 % of gastric carcinomas. The recent approval of an antibody-drug conjugate Trastuzumab deruxtecan (T-DXd) and the establishment of a new subgroup of HER2-low tumors due to the bystander effect associated with T-DXd increases the relevance of precise HER2 diagnostics.Aim of this analysis was to determine the HER2 amplification status from circulating DNA fragments in blood using a HER2 Copy Number Variation assay to establish a minimal invasive approach.For the present study, a digital droplet PCR-based method was validated relative to established tissue-based methods. Furthermore and most importantly, the changes of HER2 status during therapy were investigated in seven patients indicating that the changes of HER2 status and number of HER2 copies detected in blood can reflect on therapy efficiency and uncover treatment resistance.

The effect of probiotics on surgical outcomes in patients with gastric cancer: a meta-analysis of randomized controlled trials.

The effect of probiotics on postoperative infectious complications and nutritional status in patients with gastric cancer is still controversial, and a comprehensive search and analysis of the current relevant evidence is necessary. Our study aimed to define the effects of probiotics on surgical outcomes in gastric cancer patients undergoing surgery. Up to June 20, 2023, Embase, PubMed, Web of Science, and Cochrane databases were searched for randomized controlled trials of probiotics in gastric cancer patients undergoing surgery. Relative risk (RR) or mean difference (MD) was used to calculate the effect sizes using RevMan 5.3. A total of nine studies reporting on 861 participants were included. Perioperative supplementation with probiotics did not improve weight loss (MD 0.73 kg; 95% CI: -0.56, 2.02) or serum prealbumin levels (MD 9.48 mg/L 95% CI: -3.43, 22.40), but did reduce the incidence of postoperative infectious complications (RR 0.46, 95% CI 0.28, 0.77), shorten the time to first exhaust (MD -11.27 h; 95% CI: -16.83, -5.70), the time to first defecation (MD -15.71 h; 95% CI: -25.62, -5.79), and the length of hospital stay (MD -0.94 days; 95% CI: -1.33, -0.55), and increase serum albumin levels (MD 0.73 g/L; 95% CI: 0.01, 1.46) in gastric cancer patients undergoing surgery. Probiotics are effective in preventing postoperative infectious complications, promoting postoperative recovery, and improving nutritional status in gastric cancer patients undergoing surgery. Our study highlights the importance of probiotics for healthcare systems and offers a potential strategy to improve the prognosis and reduce the medical burden of gastric cancer patients undergoing surgery.

Prognostic value of neutrophil-to-lymphocyte ratio in gastric cancer patients treated with immune checkpoint inhibitors: A meta-analysis.

The neutrophil-to-lymphocyte ratio (NLR) has been extensively studied in patients with gastric cancer (GC) treated with immune checkpoint inhibitors (ICIs), although the results are controversial. Therefore, we performed the present meta-analysis to systematically assess the correlation between NLR and prognosis and clinicopathological factors in GC patients undergoing treatment with ICIs. Among the electronic databases, PubMed, Web of Science, Embase, and Cochrane Library were thoroughly searched. To estimate the prognostic value of NLR for progression-free survival (PFS) and overall survival (OS), hazard ratios (HRs) were calculated, and their 95% confidence intervals were calculated. This meta-analysis included 10 studies with 830 patients. Based on the pooled data, a high NLR was associated with poor OS in GC patients receiving ICIs (HR = 2.13, 95% confidence interval [CI] = 1.70-2.66, p < 0.001). Elevated NLR was a significant biomarker for worse PFS in GC patients treated with ICIs (HR = 1.74, 95% CI = 1.22-2.48, p = 0.002). There was, however, no significant correlation between NLR and sex, age, and Eastern Cooperative Oncology Group Performance Status (PS) of the ECOG, differentiation, human epidermal growth factor receptor 2 (HER2) status, or PD-L1 status in GC patients treated with ICIs. High NLR before treatment was associated with poor OS and PFS in GC patients treated with ICIs, according to our meta-analysis. NLR is an effective biomarker for evaluating the prognosis of GC patients receiving ICIs and provide more valuable information for treatment decisions in the era of immunotherapy for GC.

Predicting metastasis in gastric cancer patients: machine learning-based approaches

Gastric cancer (GC), with a 5-year survival rate of less than 40%, is known as the fourth principal reason of cancer-related mortality over the world. This study aims to develop predictive models using different machine learning (ML) classifiers based on both demographic and clinical variables to predict metastasis status of patients with GC. The data applied in this study including 733 of GC patients, divided into a train and test groups at a ratio of 8:2, diagnosed at Taleghani tertiary hospital. In order to predict metastasis in GC, ML-based algorithms, including Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), Decision Tree (RT) and Logistic Regression (LR), with 5-fold cross validation were performed. To assess the model performance, F1 score, precision, sensitivity, specificity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and precision-recall AUC (PR-AUC) were obtained. 262 (36%) experienced metastasis among 733 patients with GC. Although all models have optimal performance, the indices of SVM model seems to be more appropiate (training set: AUC: 0.94, Sensitivity: 0.94; testing set: AUC: 0.85, Sensitivity: 0.92). Then, NN has the higher AUC among ML approaches (training set: AUC: 0.98; testing set: AUC: 0.86). The RF of ML-based models, which determine size of tumor and age as two essential variables, is considered as the third efficient model, because of higher specificity and AUC (84% and 87%). Based on the demographic and clinical characteristics, ML approaches can predict the metastasis status in GC patients. According to AUC, sensitivity and specificity in both SVM and NN can be regarded as better algorithms among 6 applied ML-based methods.

A noninvasive nomogram model based on CT features to predict DNA mismatch repair deficiency in gastric cancer.

DNA mismatch repair deficiency (dMMR) status has served as a positive predictive biomarker for immunotherapy and long-term prognosis in gastric cancer (GC). The aim of the present study was to develop a computed tomography (CT)-based nomogram for preoperatively predicting mismatch repair (MMR) status in GC. Data from a total of 159 GC patients between January 2020 and July 2021 with dMMR GC (n=53) and MMR-proficient (pMMR) GC (n=106) confirmed by postoperative immunohistochemistry (IHC) staining were retrospectively analyzed. All patients underwent abdominal contrast-enhanced CT. Significant clinical and CT imaging features associated with dMMR GC were extracted through univariate and multivariate analyses. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and internal validation of the cohort data were performed. The nomogram contained four potential predictors of dMMR GC, including gender (odds ratio [OR] 9.83, 95% confidence interval [CI] 3.78-28.20, P < 0.001), age (OR 3.32, 95% CI 1.36-8.50, P = 0.010), tumor size (OR 5.66, 95% CI 2.12-16.27, P < 0.001) and normalized tumor enhancement ratio (NTER) (OR 0.15, 95% CI 0.06-0.38, P < 0.001). Using an optimal cutoff value of 6.6 points, the nomogram provided an area under the curve (AUC) of 0.895 and an accuracy of 82.39% in predicting dMMR GC. The calibration curve demonstrated a strong consistency between the predicted risk and observed dMMR GC. The DCA justified the relatively good performance of the nomogram model. The CT-based nomogram holds promise as a noninvasive, concise and accurate tool to predict MMR status in GC patients, which can assist in clinical decision-making.

Accurate preoperative staging and HER2 status prediction of gastric cancer by the deep learning system based on enhanced computed tomography.

To construct the deep learning system (DLS) based on enhanced computed tomography (CT) images for preoperative prediction of staging and human epidermal growth factor receptor 2 (HER2) status in gastric cancer patients. The raw enhanced CT image dataset consisted of CT images of 389 patients in the retrospective cohort, The Cancer Imaging Archive (TCIA) cohort, and the prospective cohort. DLS was developed by transfer learning for tumor detection, staging, and HER2 status prediction. The pre-trained Yolov5, EfficientNet, EfficientNetV2, Vision Transformer (VIT), and Swin Transformer (SWT) were studied. The tumor detection and staging dataset consisted of 4860 enhanced CT images and annotated tumor bounding boxes. The HER2 state prediction dataset consisted of 38900 enhanced CT images. The DetectionNet based on Yolov5 realized tumor detection and staging and achieved a mean Average Precision (IoU=0.5) (mAP_0.5) of 0.909 in the external validation cohort. The VIT-based PredictionNet performed optimally in HER2 status prediction with the area under the receiver operating characteristics curve (AUC) of 0.9721 and 0.9995 in the TCIA cohort and prospective cohort, respectively. DLS included DetectionNet and PredictionNet had shown excellent performance in CT image interpretation. This study developed the enhanced CT-based DLS to preoperatively predict the stage and HER2 status of gastric cancer patients, which will help in choosing the appropriate treatment to improve the survival of gastric cancer patients.

Improvement of Body Weight and Nutritional Status in Gastric Cancer Patients Enhances the Benefit of Nivolumab Therapy.

Nivolumab improves overall survival (OS) in patients with advanced gastric cancer (AGC) refractory to at least two previous chemotherapy regimens. We investigated whether changes in body weight and nutrition from first-line chemotherapy to nivolumab affected its efficacy. The correlation between weight change and nutritional status up to the start of nivolumab treatment and OS and progression-free survival (PFS) after starting nivolumab treatment was determined. Nutritional status was examined using the C-reactive protein/albumin ratio (CAR). A loss in body weight (LBW) from the onset of the first treatment of <4.5% led to OS prolongation and improved PFS outcomes. The median OS values in the LBW < 4.5% and ≥4.5% groups were 11.4 and 3.6 months, respectively. Similarly, changes in CAR from first-line chemotherapy (ΔCAR) affected OS; the ΔCAR < 0.01 group had a better prognosis than the ΔCAR ≥ 0.01 group. The median OS values in the ΔCAR < 0.01 and ≥0.01 groups were 9.4 and 4.5 months, respectively. The median OS in the group with LBW < 4.5% and ΔCAR < 0.01 was 12.9 months. LBW and deterioration of nutritional status following first-line chemotherapy are poor prognostic factors in AGC patients who received nivolumab as third- or later-line therapy. Early intervention to maintain body weight and nutritional status may improve the efficacy of immune checkpoint inhibitors.

ML-Based Texture and Wavelet Features Extraction Technique to Predict Gastric Mesothelioma Cancer.

Microsatellites are small, repetitive sequences found all across the human genome. Microsatellite instability is the phenomenon of variations in the length of microsatellites induced by the insertion or deletion of repeat units in tumor tissue (MSI). MSI-type stomach malignancy has distinct genetic phenotypes and clinic pathological characteristics, and the stability of microsatellites influences whether or not patients with gastric mesothelioma react to immunotherapy. As a result, determining MSI status prior to surgery is critical for developing treatment options for individuals with gastric cancer. Traditional MSI detection approaches need immunological histochemistry and genetic analysis, which adds to the expense and makes it difficult to apply to every patient in clinical practice. In this study, to predict the MSI status of gastric cancer patients, researchers used image feature extraction technology and a machine learning algorithm to evaluate high-resolution histopathology pictures of patients. 279 cases of raw data were obtained from the TCGA database, 442 samples were obtained after preprocessing and upsampling, and 445 quantitative image features, including first-order statistics of impressions, texture features, and wavelet features, were extracted from the histopathological images of each sample. To filter the characteristics and provide a prediction label (risk score) for MSI status of gastric cancer, Lasso regression was utilized. The predictive label's classification performance was evaluated using a logistic classification model, which was then coupled with the clinical data of each patient to create a customized nomogram for MSI status prediction using multivariate analysis.

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.

A multicenter study on the preoperative prediction of gastric cancer microsatellite instability status based on computed tomography radiomics

To construct and validate a radiomics feature model based on computed tomography (CT) images and clinical characteristics to predict the microsatellite instability (MSI) status of gastric cancer patients before surgery. We retrospectively collected the upper abdominal or the entire abdominal-enhanced CT scans of 189 gastric cancer patients before surgery. The patients underwent postoperative gastric cancer MSI status testing, and the dates of their radiologic images and clinicopathological data were from January 2015 to August 2021. These 189 patients were divided into a training set (n = 90) and an external validation set (n = 99). The patients were divided by MSI status into the MSI-high (H) arm (30 and 33 patients in the training set and external validation set, respectively) and MSI-low/stable (L/S) arm (60 and 66 patients in the training set and external validation set, respectively). In the training set, the clinical characteristics and tumor radiologic characteristics of the patients were extracted, and the tenfold cross-validation method was used for internal validation of the training set. The external validation set was used to assess its generalized performance. A receiver-operating characteristic (ROC) curve was plotted to assess the model performance, and the area under the curve (AUC) was calculated. The AUC of the radiomics modelin thetraining set and external validation set was 0.8228 [95% confidence interval (CI) 0.7355-0.9101] and 0.7603 [95% CI 0.6625-0.8581], respectively, showing that the constructed radiomics model exhibited satisfactory generalization capabilities. The accuracy, sensitivity, and specificity of the training dataset were 0.72, 0.63, and 0.77, respectively. The accuracy, sensitivity, and specificity of the external validation dataset were 0.67, 0.79, and 0.60, respectively. Statistical analysis was carried out on the clinical data, and there was statistical significance for the tumor site and age (p < 0.05). MSI-H gastric cancer was mostly seen in the gastric antrum and older patients. Radiomics markers based on CT images and clinical characteristics have the potential to be a non-invasive auxiliary diagnostic tool for preoperative assessment of gastric cancer MSI status, and they can aid in clinical decision-making and improve patient outcomes.

Radiomics in Gastric Cancer: First Clinical Investigation to Predict Lymph Vascular Invasion and Survival Outcome Using 18F-FDG PET/CT Images.

BackgroundLymph vascular invasion (LVI) is an unfavorable prognostic indicator in gastric cancer (GC). However, there are no reliable clinical techniques for preoperative predictions of LVI. The aim of this study was to develop and validate PET/CT-based radiomics signatures for predicting LVI of GC preoperatively. Radiomics nomograms were also established to predict patient survival outcomes.MethodsThis retrospective study registered 148 GC patients with histopathological confirmation for LVI status, who underwent pre-operative PET/CT scans (Discovery VCT 64 PET/CT system) from December 2014 to June 2019. Clinic-pathological factors (age, gender, and tumor grade, etc.) and metabolic PET data (maximum and mean standardized uptake value, total lesion glycolysis and metabolic tumor volume) were analyzed to identify independent LVI predictors. The dataset was randomly assigned to either the training set or test set in a 7:3 ratios. Three-dimensional (3D) radiomics features were extracted from each PET- and CT-volume of interests (VOI) singularly, and then a radiomics signature (RS) associated with LVI status is built by feature selection. Four models with different modalities (PET-RS: only PET radiomics features; CT-RS: only CT radiomics features; PET/CT-RS: both PET and CT radiomics features; PET/CT-RS plus clinical data) were developed to predict LVI. Patients were postoperatively followed up with PET/CT every 6-12 months for the first two years and then annually up to five years after surgery. The PET/CT radiomics score (Rad-scores) was calculated to assess survival outcome, and corresponding nomograms with radiomics (NWR) or without radiomics (NWOR) were established.ResultsTumor grade and maximum standardized uptake value (SUVmax) were the independent LVI predictor. 1037 CT and PET 3D radiomics features were extracted separately and reduced to 4 and 5 features to build CT-RS and PET-RS, respectively. PET/CT-RS and PET/CT-RS plus clinical data (tumor grade and SUVmax) were also developed. The ROC analysis demonstrated clinical usefulness of PET/CT-RS plus clinical data (AUC values for training and validation, respectively 0.936 and 0.914) and PET/CT-RS (AUC values for training and validation, respectively 0.881 and 0.854), which both are superior to CT-RS (0.838 and 0.824) and PET-RS (0.821 and 0.812). SUVmax and LVI were independent prognostic indicators of both OS and PFS. Decision curve analysis (DCA) demonstrated NWR outperformed NWOR and was established to assess survival outcomes. For estimation of OS and PFS, the C-indexes of the NWR were 0. 88 and 0.88 in the training set, respectively, while the C-indexes of the NWOR were 0. 82 and 0.85 in the training set, respectively.ConclusionsThe PET/CT-based radiomics analysis might serve as a non-invasive approach to predict LVI status in GC patients and provide effective predictors of patient survival outcomes.

Relationship between nutritional biomarkers and occlusal status in gastric cancer patients using the Eichner index: Observational study.

Systemic inflammatory responses and nutritional status are useful prognostic factors in gastric cancer patients. Since oral hypofunction causes undernutrition, we cross-sectionally investigated whether nutritional biomarkers were affected by the occlusal supporting zone status.In 114 gastric cancer patients, the gastric cancer stage, body mass index, albumin levels, total lymphocyte counts, cholesterol levels, C-reactive protein levels, and 4 nutritional biomarkers - the Glasgow prognostic score (GPS), neutrophil-lymphocyte ratio, prognostic nutrition index (PNI), and controlling nutritional status (CONUT) - were evaluated. Oral conditions were assessed by determining the number of remaining teeth. The occlusal supporting status was based on the Eichner classification. Patients were assigned into 3 groups per their occlusal status, and mean values were compared using the Kruskal-Wallis test. The mean age and body mass index were 72.2 ± 8.5 (50-89) years and 22.0 ± 3.6 (14.8-33.4), respectively. There were 42, 39, 23, and 10 patients in stages I, II, III, and IV, respectively. The mean number of remaining teeth was 18.1 ± 9.5. According to the Eichner classification, there were 45, 42, and 27 patients in groups A, B, and C, respectively. The GPS and neutrophil-lymphocyte ratio values and CONUT frequencies between groups A and C were significantly different (P = .033, P = .00097, P = .04, respectively; Mann-Whitney U test). PNI values were lower in group C with poor occlusal support zones than in group A with stable occlusal support zones.Occlusal supporting zone reductions were undernutrition associated. Eichner Class C patients with few occlusal supporting zones had poor GPS, PNI, and CONUT values and were undernourished.

Expression of HOTAIR and MEG3 are negatively associated with H. pylori positive status in gastric cancer patients.

Background: Chronic infection with Helicobacter pylori is one of the main causes of gastric cancer (GC). Besides, lncRNAs play crucial roles in cancer pathobiology including GC. Here we aimed to investigate the expression of MEG3 and HOTAIR in gastric cancer tissues and evaluate their association with the H. pylori status.Materials and Methods: One hundred samples were obtained. Total RNA was extracted, cDNA was synthesized and expression of MEG3 and HOTAIR was assessed using qRT-PCR. Association of their expression with H. pylori status and other clinicopathological characteristics were investigated. Furthermore, sensitivity and specificity of the MEG3 and HOTAIR expression levels for discrimination of the tumor and non-tumor samples were evaluated by Receiver operating characteristic (ROC) curve analysis.Results: We observed upregulation of HOTAIR but downregulation of MEG3 in tumor compared to the non-tumor tissues. We also found a significant negative association between their expression levels and H. pylori positive status. However, only the expression level of HOTAIR was significantly associated with the size and stage of the tumor (P < 0.05). The ROC curve analysis revealed that the expression levels of MEG3 and HOTAIR might discriminate GC tumor and non-tumor tissues.Conclusions: In conclusion, this study revealed a negative association between H. pylori infection and expression of MEG3 and HOTAIR. The results suggested that the expression level of these lncRNAs might be considered as potential biomarkers for GC.

A novel robust nomogram based on preoperative hemoglobin and albumin levels and lymphocyte and platelet counts (HALP) for predicting lymph node metastasis of gastric cancer.

Accurate assessment of lymph node status in gastric cancer (GC) patients can help to select appropriate treatment strategies for GC, but the diagnostic accuracy of conventional methods needs to be improved. The aim of this study was to investigate the predictive value of preoperative hemoglobin and albumin levels and lymphocyte and platelet counts (HALP) on lymph node status in GC patients and to construct a risk prediction model. This study retrospectively analyzed the clinicopathological characteristics of 349 patients with GC who underwent radical gastrectomy, among which 250 patients were recruited in the training cohort and 99 patients in the independent validation cohort. Significant risk factors in univariate analysis were further identified as independent variables in multivariate logistic regression analysis, which were then incorporated and presented in a nomogram. Receiver operating characteristic (ROC) curves, Calibration curve and decision curve analysis (DCA) curves were used to evaluate the discrimination, prediction accuracy and clinical effectiveness of the model. Multifactorial logistic regression analysis showed that alcohol use (OR =2.203, P=0.036), Depth of invasion (OR =7.756, P<0.001), differentiation (OR =2.252, P=0.018), carcinoembryonic antigen (CEA) (OR =2.443, P=0.017), carbohydrate antigen 19-9 (CA199) (OR =2.715, P=0.008) and HALP (OR =2.276, P=0.032) were independent risk factors for lymph node metastasis (LNM) in GC. We used these factors to construct a nomogram for predicting LNM in GC patients, and the ROC curves showed good discrimination of the model with AUC values of 0.854 (training cohort) and 0.868 (validation cohort), respectively, and the calibration curves showed good predictive ability of the nomogram, in addition to the DCA curves results showed the clinical usefulness of the model. In conclusion, we established a nomogram for predicting LNM in patients with GC.

Clinical significance of novel identified high-frequency tumor-specific peptides associated signature in predicting disease status of gastric cancer patients.

The effectively early detection and determination of disease progression of gastric cancer (GC) are still required. An emerging demand for identifying the novel targets adherent to cancer cells has been still challenged since those valuable profilings not only could act as for early gastric tumor discovery but also being potential therapeutic views. We have retrospectively analyzed GC biopsies to identify those specific target peptides in association with disease progression. We have detected the polypeptide by liquid mass technology initiated BIO-HIGH innovational assay technology for tumor-specific target peptide identification. We have validated the accessibility and feasibility of multiple target cytotoxic T-lymphocyte for the assessment of potential molecular markers by equally comparing the frequencies of tumor peptides' loci identified in 138 GC patients. The aim was to separate peripheral blood lymphocytes by density gradient centrifugation and use specific target peptides in in vitro culture of lymphocytes. The Cell Counting Kit-8 assay was set up to prove the lymphocytes' proliferation stimulated by identified peptides. Both of GC-specific peptide and shared peptide were detected in the peripheral blood, and the frequencies and quantities were correlated with disease status and cancer differentiation, in which BHGa1510 (78%), BHGa1310 (66%), BHGa0910 (57%), BHGa0310 (54%), BHGa0210 (40%), BHGa0810 (35%), BHGa0110 (33%), and BHGa1410 (30%) were apparently scoped out as high-frequency (HF) peptides could be potentially specific tumor markers. Moreover, BHGa1410 was significantly associated with cancer progression, and BHGa0910 and BHGa0210 were significantly associated with TNM stage. The IHC data have shown that both the HF BHGa1510 and HF BHGa1310 were expressions by 100% in contrast with paracancerous tissues of 40% (p < 0.05) and 33%, respectively (p < 0.05). Those specific peptide pools could be valued in assessment of advanced tumor and differential status in GC patients.