Accuracy evaluation of combining gastroscopy, multi-slice spiral CT, Her-2, and tumor markers in gastric cancer staging diagnosis

The value of serum tumor-associated autoantibodies in screening and diagnosis of gastric cancer

OBJECTIVE

To investigate the clinical value of serum autoantibodies in the screening and diagnosis of gastric cancer.

MATERIALS & METHODS

A total of 570 gastric cancer patients and 373 controls were enrolled in this study. Enzyme-linked immunosorbent assay (ELISA) was employed to quantitatively detect autoantibodies in the tested serum, and statistic modeling was conducted to analyze their relationships with various clinical and pathological parameters.

RESULTS

The results of autoantibody detection in gastric cancer patients were significantly different from those of the control group. A combination of 7 autoantibodies, including CLDN18, CAGE1, CTAG1A, PBRM1, RASSF7, IMMP2L and COPB1, was selected for modeling (AUC = 0.885). The diagnostic specificity was approximately 0.86 when combined 7-TAAs with Helicobacter pylori, while the positive predictive value was increased to 0.94. The abnormal elevation of different TAAs proteins in gastric cancer patients is related to factors such as disease stage, tumor differentiation degree, and invasion depth.

CONCLUSION

The determination of serum autoantibody panel has clinical value in screening and prediction of gastric cancer, and can be used as an auxiliary index in clinical diagnosis. The combination of 7-TAAs and Helicobacter pylori can effectively improve the screening specificity and positive predictive value. The detection results of different proteins were related to the stage of disease, the degree of tumor differentiation and the depth of invasion.

A simple electrochemical immunosensor based on MWCNTs-COOH/Fc-COOH@CoAl-LDH, nanocomposite for sensitive detection of the tumor marker CA724

A novel label-free electrochemical immunosensor for the ultrasensitive determination of cancer antigen (CA724) was developed using a novel composite of CoAl layered double hydroxides (CoAl-LDH) and carboxyl-functionalized multiwall carbon nanotubes (MWCNTs-COOH) as platform and ferrocenecarboxylic acid (Fc-COOH) as signal label. The MWCNTs-COOH/Fc-COOH@CoAl-LDH composite was prepared by a convenient and simple one-step ultrasonic method, and various characterization techniques consisting of scanning electron microscopy (SEM), transimission electronic microscopy (TEM), TEM-Mapping, fourier transform infrared (FT-IR), X-ray diffraction (XRD) and X-ray photoelectronic energy spectrum (XPS) were applied to study the size and morphological features. Due to its large specific surface area and multilayer structure, the CoAl-LDH can be post-doped to embed a large amount of signal probe to realize the amplification of the internal reference signal Fc. In addition, the higher conductivity of MWCNTs-COOH compensates for the deficiency of CoAl-LDH, which effectively strengthened the electron transfer efficiency of electrochemical signaling substances. The optimal experimental conditions were detected to be 2.5 mmol of Fc-COOH, 4.0 mg/mL of concentration, pH 6.0, incubation time of 40 min, and incubation temperature of 37 ℃. Under optimal conditions, the fabricated sensor exhibits linearity in a wide dynamic range covering the physiological concentration, from 0.001 to 100 U/mL and the limit of detection (LOD) was 0.03962 mU/mL, the calibration equation is stated as △I = 7.76363 log10CCA724 + 40.50351 (R2 = 0.99674). The sensor is successfully detects trace levels of CA724 in human serum with excellent recovery rates ranging from 100.52 %-102.30 %, proving the synergy of MWCNTs-COOH/Fc-COOH@CoAl-LDH as a promising platform for electrochemical sensing for clinical detection of other disease markers.

Development and validation of a deep learning model for predicting gastric cancer recurrence based on CT imaging: a multicenter study

INTRODUCTION

The postoperative recurrence of gastric cancer (GC) has a significant impact on the overall prognosis of patients. Therefore, accurately predicting the postoperative recurrence of GC is crucial.

METHODS

This retrospective study gathered data from 2813 GC patients who underwent radical surgery between 2011 and 2017 at two medical centers. Follow-up was extended until May 2023, and cases were categorized as recurrent or nonrecurrent based on postoperative outcomes. Clinical pathological information and imaging data were collected for all patients. A new deep learning signature (DLS) was generated using pretreatment computed tomography images, based on a pretrained baseline (a customized Resnet50), for predicting postoperative recurrence. The deep learning fusion signature (DLFS) was created by combining the score of DLS with the weighted values of identified clinical features. The predictive performance of the model was evaluated based on discrimination, calibration, and clinical usefulness. Survival curves were plotted to investigate the differences between DLFS and prognosis.

RESULTS

In this study, 2813 patients with GC were recruited and allocated into training, internal validation, and external validation cohorts. The DLFS was developed and assessed for its capability in predicting the risk of postoperative recurrence. The DLFS exhibited excellent performance with AUCs of 0.833 (95% CI: 0.809-0.858) in the training set, 0.831 (95% CI: 0.792-0.871) in the internal validation set, and 0.859 (95% CI: 0.806-0.912) in the external validation set, along with satisfactory calibration across all cohorts ( P >0.05). Furthermore, the DLFS model significantly outperformed both the clinical model and DLS ( P <0.05). High-risk recurrent patients exhibit a significantly poorer prognosis compared to low-risk recurrent patients ( P <0.05).

CONCLUSIONS

The integrated model developed in this study, focusing on GC patients undergoing radical surgery, accurately identifies cases at high-risk of postoperative recurrence and highlights the potential of DLFS as a prognostic factor for GC patients.

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

BACKGROUND

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSION

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

Identifying natural products for gastric cancer treatment through pharmacophore creation, 3D QSAR, virtual screening, and molecular dynamics studies

BACKGROUND

Gastric cancer (GC) is known as the fourth leading cause of cancer-related death and the fifth major cancer in the world, and this is a serious threat to general health all over the world. The lack of early detection markers results in a belated diagnosis, i.e. the final stages, which could be associated with the ineffectiveness of the treatment strategies, and naturally, it leads to poor prognosis. Even though a variety of treatments have been developed, there is a trend of studying traditional medicinal plants, due to the worrying side effect of drugs available in the market.

METHODS

In this study, pharmacophore generation and 3D-QSAR model were created using 50 compounds with anti-gastric cancer activity (with IC50 had been reported in the previous studies).

RESULTS

Based on three of the best pharmacophoric hypotheses, virtual screening was performed to discover the top anti-gastric cancer compounds from a database of 183,885 compounds. The selected compounds were used for molecular docking with three protein receptors 7BKG, 4F5B, and 4ZT1 to investigate the intermolecular interactions between these ligands and receptors. Finally, 21 lead compounds with the highest amount of docking score ranging from - 13.366 to -6.404 kcal/mol were selected, and then the ADME/Tox properties of these compounds were calculated. All these compounds have a fitness score above 1.8, a molecular weight of less than 500 g/mol, hydrogen bond donors up to 3, hydrogen bond acceptors up to 8.50, and logP of 1.013 to 4.174. Finally, molecular dynamic simulations for top-scoring ligand-receptor complexes were investigated.

CONCLUSION

These selected lead compounds have the most anti-gastric cancer effects among the 183,885 compounds in the database. Therefore, lead compounds might be considered for gastric cancer therapy in future studies.

Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images

Background

Gastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis.

Methods

In this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score.

Results

The segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set.

Conclusion

The results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications.

Association between multiple coagulation-related factors and lymph node metastasis in patients with gastric cancer: A retrospective cohort study

BACKGROUND

Patients with tumors generally present with accompanying activation of the coagulation system, which may be related to tumor stage. To our knowledge, few studies have examined the activation of the coagulation system in reference to lymph node metastasis within gastric cancer. This study aimed to investigate the correlation between multiple coagulation-related factors and lymph node metastasis in patients with gastric cancer after excluding the influence of tumor T stage.

MATERIALS AND METHODS

We retrospectively evaluated the relationship between lymph node metastasis and coagulation-related factors in 516 patients with T4a stage gastric cancer. We further analyzed influencing factors for lymph node metastasis and verified the predictive value of maximum amplitude (MA, a parameter of thromboelastography which is widely used to assess the strength of platelet-fibrinogen interaction in forming clots) in reference to lymph node metastasis.

RESULTS

Platelet counts (P=0.011), fibrinogen levels (P=0.002) and MA values (P=0.006) were statistically significantly higher in patients with T4a stage gastric cancer presenting with lymph node metastasis than in those without lymph node metastasis. Moreover, tumor N stage was statistically significantly and positively correlated with platelet count (P<0.001), fibrinogen level (P=0.003), MA value (P<0.001), and D-dimer level (P=0.010). The MA value was an independent factor for lymph node metastasis (β=0.098, 95% CI: 1.020-1.193, P=0.014) and tumor N stage (β=0.059, 95% CI: 0.015-0.104, P=0.009), and could be used to predict the presence of lymph node metastasis in patients with gastric cancer (sensitivity 0.477, specificity 0.783, P=0.006). The independent influencing factors for MA value mainly included platelet levels, fibrinogen levels, D-dimer and hemoglobin levels; we found no statistically significant correlations with tumor diameter, tumor area, and other evaluated factors.

CONCLUSION

We conclude that MA value is an independent influencing factor for lymph node metastasis and tumor N stage in patients with T4a stage gastric cancer. The MA value has important value in predicting the presence or absence of lymph node metastasis in patients with gastric cancer.

CLINICAL TRIAL REGISTRATION

http://www.chictr.org.cn, identifier ChiCTR2200064936.

Establishment and validation of the survival prediction risk model for appendiceal cancer

OBJECTIVE

Establishing a risk model of the survival situation of appendix cancer for accurately identifying high-risk patients and developing individualized treatment plans.

METHODS

A total of 4,691 patients who were diagnosed with primary appendix cancer from 2010 to 2016 were extracted using Surveillance, Epidemiology, and End Results (SEER) * Stat software. The total sample size was divided into 3,283 cases in the modeling set and 1,408 cases in the validation set at a ratio of 7:3. A nomogram model based on independent risk factors that affect the prognosis of appendix cancer was established. Single-factor Cox risk regression, Lasso regression, and multifactor Cox risk regression were used for analyzing the risk factors that affect overall survival (OS) in appendectomy patients. A nomogram model was established based on the independent risk factors that affect appendix cancer prognosis, and the receiver operating characteristic curve (ROC) curve and calibration curve were used for evaluating the model. Survival differences between the high- and low-risk groups were analyzed through Kaplan-Meier survival analysis and the log-rank test. Single-factor Cox risk regression analysis found age, ethnicity, pathological type, pathological stage, surgery, radiotherapy, chemotherapy, number of lymph nodes removed, T stage, N stage, M stage, tumor size, and CEA all to be risk factors for appendiceal OS. At the same time, multifactor Cox risk regression analysis found age, tumor stage, surgery, lymph node removal, T stage, N stage, M stage, and CEA to be independent risk factors for appendiceal OS. A nomogram model was established for the multifactor statistically significant indicators. Further stratified with corresponding probability values based on multifactorial Cox risk regression, Kaplan-Meier survival analysis found the low-risk group of the modeling and validation sets to have a significantly better prognosis than the high-risk group (p < 0.001).

CONCLUSION

The established appendix cancer survival model can be used for the prediction of 1-, 3-, and 5-year OS and for the development of personalized treatment options through the identification of high-risk patients.

Common Core Genes Play Vital Roles in Gastric Cancer With Different Stages

Background: Owing to complex molecular mechanisms in gastric cancer (GC) oncogenesis and progression, existing biomarkers and therapeutic targets could not significantly improve diagnosis and prognosis. This study aims to identify the key genes and signaling pathways related to GC oncogenesis and progression using bioinformatics and meta-analysis methods.

Methods: Eligible microarray datasets were downloaded and integrated using the meta-analysis method. According to the tumor stage, GC gene chips were classified into three groups. Thereafter, the three groups’ differentially expressed genes (DEGs) were identified by comparing the gene data of the tumor groups with those of matched normal specimens. Enrichment analyses were conducted based on common DEGs among the three groups. Then protein–protein interaction (PPI) networks were constructed to identify relevant hub genes and subnetworks. The effects of significant DEGs and hub genes were verified and explored in other datasets. In addition, the analysis of mutated genes was also conducted using gene data from The Cancer Genome Atlas database.

Results: After integration of six microarray datasets, 1,229 common DEGs consisting of 1,065 upregulated and 164 downregulated genes were identified. Alpha-2 collagen type I (COL1A2), tissue inhibitor matrix metalloproteinase 1 (TIMP1), thymus cell antigen 1 (THY1), and biglycan (BGN) were selected as significant DEGs throughout GC development. The low expression of ghrelin (GHRL) is associated with a high lymph node ratio (LNR) and poor survival outcomes. Thereafter, we constructed a PPI network of all identified DEGs and gained 39 subnetworks and the top 20 hub genes. Enrichment analyses were performed for common DEGs, the most related subnetwork, and the top 20 hub genes. We also selected 61 metabolic DEGs to construct PPI networks and acquired the relevant hub genes. Centrosomal protein 55 (CEP55) and POLR1A were identified as hub genes associated with survival outcomes.

Conclusion: The DEGs, hub genes, and enrichment analysis for GC with different stages were comprehensively investigated, which contribute to exploring the new biomarkers and therapeutic targets.