Nomograms Involving HER2 for Predicting Lymph Node Metastasis in Early Gastric Cancer

Reshaping the immune microenvironment and reversing immunosenescence by natural products: Prospects for immunotherapy in gastric cancer

Gastric cancer (GC) represents a global health-care challenge. Recent progress in immunotherapy has elicited attracted considerable attention as a viable treatment option through modulating the host immune system and unleashing pre-existing immunity, which has profoundly revolutionized oncology, especially GC. Nonetheless, low clinical response and intrinsic and acquired resistance remain persistently challenging. The microenvironment of GC comprising multifarious stromal cell types has remarkable immunosuppressive elements that may impact the efficacy of immunotherapy. Immunosenescence is increasingly regarded as a factor that contributes to cancer development, remodels the tumor microenvironment and affects the efficacy of immunotherapy. Natural products are at the forefront of traditional medicine. Senotherapeutics is a class of drugs and natural products capable of delaying, preventing, or reversing the senescence process (i.e., senolytics) or suppressing senescence-associated secretory phenotype (i.e., senomorphics). Emerging evidence supports that natural products can improve the efficacy of existing immunotherapy and expand their indications in GC mainly based upon remodeling the immunosuppressive microenvironment and reversing immunosenescence. The review provides an integrated review of previously reported and ongoing clinical trials with immunotherapeutic regimens in GC and discusses current challenges. Next, we focus on natural compounds that exert anti-GC functions and possess immunomodulatory properties. More attention is paid to the potential of these natural compounds in modulating the immune microenvironment and immunosenescence. Lastly, we discuss the nanomedicine that can overcome the deficiencies of natural products. Altogether, our review suggests the enormous potential of natural compounds in GC immunotherapy, and provides an important direction for future research.

Functions and Clinical Applications of Exosomes in Gastric Cancer

Gastric cancer is a common and highly invasive type of malignant tumor, the pathogenesis of which remains unclarified. However, exosomes are now known to play important roles in gastric cancer development and treatment. Cells use exosomes for the packaging and transportation of a variety of bioactive molecules, such as proteins, double-stranded DNA, and micro-ribonucleic acids, to other sites. Exosome-specific membrane structures and exosomal contents are widely involved in processes that facilitate material exchange and intercellular communication between gastric cancer cells. They help in forming a pre-metastatic microenvironment, promoting the proliferation and apoptosis of gastric cancer cells, and driving invasion, metastasis, and resistance to anti-tumor drugs. In this review, we aimed to summarize the findings of research articles indexed in the PubMed, Web of Science, and Embase databases and published up to May 31, 2024, on the role of exosomes in the pathogenesis of gastric cancer and their potential clinical applications in its treatment. Thus, research on exosomes may lead to breakthroughs in the early diagnosis of gastric cancer and identification of novel treatments.

Exosomes play a crucial role in remodeling the tumor microenvironment and in the treatment of gastric cancer

Gastric cancer (GC) is a common and frequent malignant cancer of the digestive system with a poor prognosis. In addition to common therapies such as surgical resection and chemotherapy, novel biological interventions are quite valuable for research. Exosomes are extracellular vesicles (EVs) that originate from various cell types and contain proteins, RNA, DNA, and other components that transmit biological signals and mediate intercellular communication. Numerous studies have shown that exosomes shape the tumor microenvironment (TME) by affecting hypoxia, inflammation, immunity, metabolism, and interstitial changes in the tumor, playing a crucial role in the development and metastasis of GC. This article reviews the important role of exosomes in the TME of GC and explores their potential clinical applications in GC treatment.

Latest guideline of endoscopic submucosal dissection of early gastric cancer may not be suitable for Chinese patients: retrospective study findings from two centers

BACKGROUND

To analyze the diagnostic efficiency of the four absolute endoscopic submucosal dissection (ESD) indications for lymph node metastasis (LNM) of Chinese patients with early gastric cancer (EGC).

METHODS

We retrospectively analyzed EGC patients who underwent radical D2 gastrectomy from January 2019 to December 2022. We evaluated the rate of LNM, false-negative rate, and negative predictive value of the four ESD indications.

RESULTS

Of enrolled 2722 EGC patients, 388 (14.3%) patients presented LNM. Tumor size > 2 cm, ulceration, submucosal invasion, undifferentiated type, and lymphovascular invasion were independent risk factors of LNM in patients with EGC. 1062 (39%) cases of EGC conformed to the four EDS indications; however, 4% of them had LNM. 451 cases were fully in accord with the fourth ESD indication (undifferentiated intramucosal carcinoma without ulceration and a maximum lesion diameter of ≤ 2 cm), and 35 of them had LNM, with a false-negative rate (FNR) of 9.02% and a negative predictive value (NPV) of 92.24%. There was significant difference among the four indications in terms of the rate of LNM (1.0% vs 1.5% vs 1.3% vs 7.8%, P < 0.001), FNR (1.03% vs 0.52% vs 0.26% vs 9.02%, P < 0.001), and NPV (98.99% vs 98.53% vs 98.75% vs 92.24%, P < 0.001).

CONCLUSION

Overall, the fourth ESD indication was associated with a high rate of LNM compared to the other three indications. Thus, it might not be safe to classify it as an absolute indication in Chinese patients with EGC.

The Key Role of Tumor Budding in Predicting the Status of Lymph Node Involvement in Early Gastric Cancer Patients: A Clinical Multicenter Validation in China

BACKGROUND

Accurate preoperative prediction of lymph node (LN) involvement is essential for the management of early gastric cancer (EGC). Our objective was to formulate a potent nomogram for predicting LN involvement in EGC by leveraging an innovative predictor of tumor budding.

METHODS

We assembled a cohort of EGC patients who underwent radical surgery at two tertiary cancer centers. Tumor budding was stratified by using an optimal cutoff value and integrated with other clinicopathological variables to ascertain the risk factors associated with LN involvement. A nomogram was developed and its predictive performance was assessed by using receiver operating characteristic (ROC) curves and calibration plots. In addition, we conducted decision curve analysis to evaluate its clinical utility. Finally, an external validation was conducted by using an independent cohort.

RESULTS

Finally, 307 eligible patients (215 in the primary cohort and 92 in the validation cohort) were included. Tumor budding, categorized by a count of two, exhibited a robust association with LN involvement (OR 14.12, p = 0.012). Other significant risk factors include lymphovascular invasion, depth of tumor invasion, ulceration, and tumor differentiation. Notably, the nomogram demonstrated exceptional discriminative power (area under the ROC curve, 0.872 in the primary cohort and 0.885 in the validation cohort) and precise predictive capabilities. Furthermore, the nomogram showed notable clinical applicability through decision curve analysis, particularly in endoscopic curability C-2, by mitigating the risk of overtreatment.

CONCLUSIONS

Tumor budding is a robust predictor of LN involvement in EGC. The incorporation of tumor budding into a nomogram is an effective strategy, thereby informing and enhancing clinical decision-making.

A nomogram based on platelet-to-lymphocyte ratio for predicting lymph node metastasis in patients with early gastric cancer

Background

Preoperative assessment of the presence of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) remains difficult. We aimed to develop a practical prediction model based on preoperative pathological data and inflammatory or nutrition-related indicators.

Methods

This study retrospectively analyzed the clinicopathological characteristics of 1,061 patients with EGC who were randomly divided into the training set and validation set at a ratio of 7:3. In the training set, we introduced the least absolute selection and shrinkage operator (LASSO) algorithm and multivariate logistic regression to identify independent risk factors and construct the nomogram. Both internal validation and external validation were performed by the area under the receiver operating characteristic curve (AUC), C-index, calibration curve, and decision curve analysis (DCA).

Results

LNM occurred in 162 of 1,061 patients, and the rate of LNM was 15.27%. In the training set, four variables proved to be independent risk factors (p < 0.05) and were incorporated into the final model, including depth of invasion, tumor size, degree of differentiation, and platelet-to-lymphocyte ratio (PLR). The AUC values were 0.775 and 0.792 for the training and validation groups, respectively. Both calibration curves showed great consistency in the predictive and actual values. The Hosmer-Lemeshow (H-L) test was carried out in two cohorts, showing excellent performance with p-value >0.05 (0.684422, 0.7403046). Decision curve analysis demonstrated a good clinical benefit in the respective set.

Conclusion

We established a preoperative nomogram including depth of invasion, tumor size, degree of differentiation, and PLR to predict LNM in EGC patients and achieved a good performance.

Systemic immune-inflammation index in predicting non-curative resection of endoscopic submucosal dissection in patients with early gastric cancer

BACKGROUND AND PURPOSE

Although endoscopic submucosal dissection (ESD) is considered standard treatment for early gastric cancer (EGC), patients with non-curative resection (NCR) of ESD may still require gastrectomy. The systemic immune-inflammation index (SII) showed great potential in predicting the prognosis of gastric cancer patients. This study aims to investigate the predictive validity of SII of NCR in EGC patients.

METHODS

We reviewed data from EGC patients who underwent ESD in the past. The relationship between SII and clinicopathologic features was investigated. We used Receiver operating characteristic curves to compare the predictive values of NCR between SII and other inflammation indices. Binary logistic analysis was used to identify independent risk factors for NCR. These factors were then used to construct a predictive nomogram.

RESULTS

SII was associated with larger tumor size, male gender, older age, submucosal invasion, and a greater risk of NCR. SII showed better predictivity of NCR than platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR). SII [odds ratio (OR) = 1.003, P = 0.001], NLR (OR = 1.520, P = 0.029), PLR (OR = 1.009, P = 0.010), upper stomach tumors (OR = 16.393, P < 0.001), poorly differentiated type (OR = 29.754, P < 0.001), ulceration (OR = 4.814, P = 0.001), and submucosal invasion (OR = 48.91, P < 0.001) were independent risk factors for NCR. The nomogram model based on these factors exhibited superior concordance and accuracy.

CONCLUSION

SII could be considered a simple and effective predictor of NCR of ESD in EGC patients.

Research trends in endoscopic applications in early gastric cancer: A bibliometric analysis of studies published from 2012 to 2022

Background

Endoscopy is the optimal method of diagnosing and treating early gastric cancer (EGC), and it is therefore important to keep up with the rapid development of endoscopic applications in EGC. This study utilized bibliometric analysis to describe the development, current research progress, hotspots, and emerging trends in this field.

Methods

We retrieved publications about endoscopic applications in EGC from 2012 to 2022 from Web of Science™ (Clarivate™, Philadelphia, PA, USA) Core Collection (WoSCC). We mainly used CiteSpace (version 6.1.R3) and VOSviewer (version 1.6.18) to perform the collaboration network analysis, co-cited analysis, co-occurrence analysis, cluster analysis, and burst detection.

Results

A total of 1,333 publications were included. Overall, both the number of publications and the average number of citations per document per year increased annually. Among the 52 countries/regions that were included, Japan contributed the most in terms of publications, citations, and H-index, followed by the Republic of Korea and China. The National Cancer Center, based in both Japan and the Republic of Korea, ranked first among institutions in terms of number of publications, citation impact, and the average number of citations. Yong Chan Lee was the most productive author, and Ichiro Oda had the highest citation impact. In terms of cited authors, Gotoda Takuji had both the highest citation impact and the highest centrality. Among journals, Surgical Endoscopy and Other Interventional Techniques had the most publications, and Gastric Cancer had the highest citation impact and H-index. Among all publications and cited references, a paper by Smyth E C et al., followed by one by Gotoda T et al., had the highest citation impact. Using keywords co-occurrence and cluster analysis, 1,652 author keywords were categorized into 26 clusters, and we then divided the clusters into six groups. The largest and newest clusters were endoscopic submucosal dissection and artificial intelligence (AI), respectively.

Conclusions

Over the last decade, research into endoscopic applications in EGC has gradually increased. Japan and the Republic of Korea have contributed the most, but research in this field in China, from an initially low base, is developing at a striking speed. However, a lack of collaboration among countries, institutions, and authors, is common, and this should be addressed in future. The main focus of research in this field (i.e., the largest cluster) is endoscopic submucosal dissection, and the topic at the frontier (i.e., the newest cluster) is AI. Future research should focus on the application of AI in endoscopy, and its implications for the clinical diagnosis and treatment of EGC.

Establishment of a nomogram for predicting lymph node metastasis in patients with early gastric cancer after endoscopic submucosal dissection

Background

Endoscopic submucosal dissection (ESD) has been accepted as the standard treatment for the appropriate indication of early gastric cancer (EGC). Determining the risk of lymph node metastasis (LNM) is critical for the following treatment selection after ESD. This study aimed to develop a predictive model to quantify the probability of LNM in EGC to help minimize the invasive procedures.

Methods

A total of 952 patients with EGC who underwent radical gastrectomy were retrospectively reviewed. LASSO regression was used to help screen the potential risk factors. Multivariate logistic regression was used to establish a predictive nomogram, which was subjected to discrimination and calibration evaluation, bootstrapping internal validation, and decision curve analysis.

Results

Results of multivariate analyses revealed that gender, fecal occult blood test, CEA, CA19-9, histologic differentiation grade, lymphovascular invasion, depth of infiltration, and Ki67 labeling index were independent prognostic factors for LNM. The nomogram had good discriminatory performance, with a concordance index of 0.816 (95% CI 0.781–0.853). The validation dataset yielded a corrected concordance index of 0.805 (95% CI 0.770–0.842). High agreements between ideal curves and calibration curves were observed.

Conclusions

The nomogram is clinically useful for predicting LNM after ESD in EGC, which is beneficial to identifying patients who are at low risk for LNM and would benefit from avoiding an unnecessary gastrectomy.

Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer

Background

This study aims to develop and validate a predictive model combining deep transfer learning, radiomics, and clinical features for lymph node metastasis (LNM) in early gastric cancer (EGC).

Materials and methods

This study retrospectively collected 555 patients with EGC, and randomly divided them into two cohorts with a ratio of 7:3 (training cohort, n = 388; internal validation cohort, n = 167). A total of 79 patients with EGC collected from the Second Affiliated Hospital of Soochow University were used as external validation cohort. Pre-trained deep learning networks were used to extract deep transfer learning (DTL) features, and radiomics features were extracted based on hand-crafted features. We employed the Spearman rank correlation test and least absolute shrinkage and selection operator regression for feature selection from the combined features of clinical, radiomics, and DTL features, and then, machine learning classification models including support vector machine, K-nearest neighbor, random decision forests (RF), and XGBoost were trained, and their performance by determining the area under the curve (AUC) were compared.

Results

We constructed eight pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that 1,048 DTL features extracted based on the pre-trained Resnet152 network combined in the predictive model had the best performance in discriminating the LNM status of EGC, with an AUC of 0.901 (95% CI: 0.847-0.956) and 0.915 (95% CI: 0.850-0.981) in the internal validation and external validation cohorts, respectively.

Conclusion

We first utilized comprehensive multidimensional data based on deep transfer learning, radiomics, and clinical features with a good predictive ability for discriminating the LNM status in EGC, which could provide favorable information when choosing therapy options for individuals with EGC.

Tumor Characteristics Associated with Lymph Node Metastasis and Prognosis in Patients with ERBB2-Positive Gastric Cancer

Gastric cancers (GCs) that express human erb-b2 receptor tyrosine kinase 2 (ERBB2, also known as HER2) account for 7.3%–20.2% of GCs. The pathological and prognostic factors associated with lymph node metastasis of such tumors are still unclear. Therefore, we aimed to identify the risk factors for lymph node metastasis and prognostic factors of patients with ERBB2-positive GC. We conducted a retrospective analysis of pathological specimens after D2 radical surgery for locally advanced GC and D1+ surgery performed for early GC in our hospital from January 2015 to December 2018. Patients with ERBB2-positive GC were selected and the potential risk factors for lymph node metastasis and potential factors affecting prognosis were evaluated. Among 1,124 GC patients, 122 diagnosed with ERBB2-positive GC were included in the study. We found that risk factors for lymph node metastasis included tumor size (hazard ratio (HR)- 6.213, 95% confidence interval (CI)- 2.097–18.407, p = 0.001), neural invasion (HR- 2.876, 95% CI - 1.011–8.184, p = 0.048), and vascular invasion (HR- 16.881, 95% CI - 5.207–54.727, p < 0.001). T stage (HR- 4.615, 95% CI - 2.182–9.759, p < 0.001) and vascular invasion (HR- 3.036, 95% CI - 1.369–6.736, p = 0.006) were significant prognostic variables. These findings shed new light on the pathology and prognosis of patients with ERBB2-positive GC.

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

Objective

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

Methods

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

Results

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

Conclusion

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

Systematic Review Registration

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

Comparison between Endoscopic Submucosal Dissection and Surgery in Patients with Early Gastric Cancer

Simple Summary

Conventional gastrectomy combined with regional lymph node dissection has been the standard treatment for early gastric cancer (EGC). This retrospective case–control study aimed to compare the clinical outcomes of endoscopic submucosal dissection (ESD) and surgical resection for EGC in China. After propensity score-matching, there were no significant differences between the two groups for OS, RFS, and DSS. Additionally, with similar R0 resection and recurrence rates, the ESD group showed less blood loss, fewer adverse events, lower hospital cost, and a shorter operative time and hospital duration than the surgery group. Therefore, ESD can be a first-line treatment of EGC in addition to surgery.

Abstract

Background: Endoscopic submucosal dissection (ESD) has become a preferred treatment option for early gastric cancer (EGC). This study aimed to compare the clinical outcomes of ESD and surgical resection for EGC. Methods: This was a retrospective case–control study. Patients with a diagnosis of EGC who underwent ESD or surgery in our hospital from 2011 to 2020 were enrolled. We compared the clinical characteristics and treatment outcomes of these two groups according to propensity score-matching. The primary outcome comparison was overall survival (OS). Secondary outcomes were disease-specific survival (DSS), recurrence-free survival (RFS), and treatment-related events. Results: In the matched cohort, the ESD group showed comparable OS, RFS, and DSS with the surgery group. Statistical differences were shown in blood loss and adverse events. Furthermore, the ESD group showed lower hospital cost, as well as a shorter operative time and hospital duration than the surgery group. The R0 resection and recurrence rates were similar between the two groups. In Cox regression analysis, age, tumor size, poor differentiation, and lymphovascular invasion were regarded as independent factors of OS. Conclusions: With sufficient safety and advantages, ESD can be a first-line treatment of EGC. Preoperative evaluation is vital to the appropriate treatment and prognosis.