Multi-institutional validation of the 8th AJCC TNM staging system for gastric cancer: Analysis of survival data from high-volume Eastern centers and the SEER database

A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients

BACKGROUND

At the time of cancer diagnosis, it is crucial to accurately classify malignant gastric tumors and the possibility that patients will survive.

OBJECTIVE

This study aims to investigate the feasibility of identifying and applying a new feature extraction technique to predict the survival of gastric cancer patients.

METHODS

A retrospective dataset including the computed tomography (CT) images of 135 patients was assembled. Among them, 68 patients survived longer than three years. Several sets of radiomics features were extracted and were incorporated into a machine learning model, and their classification performance was characterized. To improve the classification performance, we further extracted another 27 texture and roughness parameters with 2484 superficial and spatial features to propose a new feature pool. This new feature set was added into the machine learning model and its performance was analyzed. To determine the best model for our experiment, Random Forest (RF) classifier, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) (four of the most popular machine learning models) were utilized. The models were trained and tested using the five-fold cross-validation method.

RESULTS

Using the area under ROC curve (AUC) as an evaluation index, the model that was generated using the new feature pool yields AUC = 0.98 ± 0.01, which was significantly higher than the models created using the traditional radiomics feature set (p < 0.04). RF classifier performed better than the other machine learning models.

CONCLUSIONS

This study demonstrated that although radiomics features produced good classification performance, creating new feature sets significantly improved the model performance.

A Prognostic Model for Survival in Patients with Gastric Signet Ring Cell Carcinoma

INTRODUCTION

The objective of our study was to develop a nomogram to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRCC).

METHODS

A total of 3,408 GSRCC patients between 1975 and 2017 were screened from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation cohorts. Univariate and multivariate Cox analyses were conducted to identify independent prognostic factors for the construction of a nomogram. The performance of the model was then assessed by the concordance index (C-index), calibration plot, and area under the receiver operating characteristic curve (AUC). Then, the novel nomogram was further assessed by 64 GSRCC patients from our hospital as the external cohort.

RESULTS

We identified age, tumor lymph node metastasis (TNM) staging system, surgery, and chemotherapy as significant independent elements of prognosis. On this basis, a nomogram was constructed, with a C-index of OS in the training and validation cohorts of 0.763 (95% CI: 0.751-0.774) and 0.766 (95% CI: 0.748-0.784) and a C-index of CSS of 0.765 (95% CI: 0.753-0.777) and 0.773 (95% CI: 0.755-0.791), respectively. The AUCs of the nomogram for predicting 2- and 5-year OS were 0.848 and 0.885, respectively, and those for predicting CSS were 0.854 and 0.899, respectively, demonstrating the excellent predictive value of the constructed nomogram compared to the traditional AJCC staging system. Similar results were also observed in both the internal and external validation sets.

CONCLUSION

The nomogram provided an accurate tool to predict OS and CSS in patients with GSRCC, which can assist clinicians in making predictions about individual patient survival.

Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer

BACKGROUND

The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet.

PURPOSE

This multicenter study aimed to develop a deep learning-based radiomics model to preoperatively identify ESTM and evaluate its prognostic value.

METHODS

A total of 959 GC patients were enrolled from two centers and split into a training cohort (N = 551) and a test cohort (N = 236) for ESTM evaluation. Additionally, an external survival cohort (N = 172) was included for prognostic analysis. Four models were established based on clinical characteristics and multiphase computed tomography (CT) images for preoperative identification of ESTM, including a deep learning model, a hand-crafted radiomic model, a clinical model, and a combined model. C-index, decision curve, and calibration curve were utilized to assess the model performances. Survival analysis was conducted to explore the ability of stratifying overall survival (OS).

RESULTS

The combined model showed good discrimination of the ESTM [C-indices (95% confidence interval, CI): 0.770 (0.729-0.812) and 0.761 (0.718-0.805) in training and test cohorts respectively], which outperformed deep learning model, radiomics model, and clinical model. The stratified analysis showed this model was not affected by patient's tumor size, the presence of lymphovascular invasion, and Lauren classification (p < 0.05). Moreover, the model score showed strong consistency with the OS [C-indices (95%CI): 0.723 (0.658-0.789, p < 0.0001) in the internal survival cohort and 0.715 (0.650-0.779, p < 0.0001) in the external survival cohort]. More interestingly, univariate analysis showed the model score was significantly associated with occult distant metastasis (p < 0.05) that was missed by preoperative diagnosis.

CONCLUSIONS

The model combining CT images and clinical characteristics had an impressive predictive ability of both ESTM and prognosis, which has the potential to serve as an effective complement to the preoperative TNM staging system.

Evaluation the benefits of additional radiotherapy for gastric cancer patients after D2 resection using CT based radiomics

OBJECTIVES

The value of adding radiotherapy (RT) is still unclear for patients with gastric cancer (GC) after D2 lymphadenectomy. The purpose of this study is to predict and compare the overall survival (OS) and disease-free survival (DFS) of GC patients treated by chemotherapy and chemoradiation based on contrast-enhanced CT (CECT) radiomics.

METHODS

A total of 154 patients treated by chemotherapy and chemoradiation in authors' hospital were retrospectively reviewed and randomly divided into the training and testing cohorts (7:3). Radiomics features were extracted from contoured tumor volumes in CECT using the pyradiomics software. Radiomics score and nomogram with integrated clinical factors were developed to predict the OS and DFS and evaluated with Harrell's Consistency Index (C-index).

RESULTS

Radiomics score achieved a C index of 0.721(95%CI: 0.681-0.761) and 0.774 (95%CI: 0.738-0.810) in the prediction of DFS and OS for GC patients treated by chemotherapy and chemoradiation, respectively. The benefits of additional RT only demonstrated in subgroup of GC patients with Lauren intestinal type and perineural invasion (PNI). Integrating clinical factors further improved the prediction ability of radiomics models with a C-index of 0.773 (95%CI: 0.736-0.810) and 0.802 (95%CI: 0.765-0.839) for DFS and OS, respectively.

CONCLUSIONS

CECT based radiomics is feasible to predict the OS and DFS for GC patients underwent chemotherapy and chemoradiation after D2 resection. The benefits of additional RT only observed in GC patients with intestinal cancer and PNI.

Development of the oncolytic virus, CF33, and its derivatives for peritoneal-directed treatment of gastric cancer peritoneal metastases

BACKGROUND

Gastric cancer (GC) that metastasizes to the peritoneum is fatal. CF33 and its genetically modified derivatives show cancer selectivity and oncolytic potency against various solid tumors. CF33-hNIS and CF33-hNIS-antiPDL1 have entered phase I trials for intratumoral and intravenous treatments of unresectable solid tumors (NCT05346484) and triple-negative breast cancer (NCT05081492). Here, we investigated the antitumor activity of CF33-oncolytic viruses (OVs) against GC and CF33-hNIS-antiPDL1 in the intraperitoneal (IP) treatment of GC peritoneal metastases (GCPM).

METHODS

We infected six human GC cell lines AGS, MKN-45, MKN-74, KATO III, SNU-1, and SNU-16 with CF33, CF33-GFP, or CF33-hNIS-antiPDL1 at various multiplicities of infection (0.01, 0.1, 1.0, and 10.0), and performed viral proliferation and cytotoxicity assays. We used immunofluorescence imaging and flow cytometric analysis to verify virus-encoded gene expression. We evaluated the antitumor activity of CF33-hNIS-antiPDL1 following IP treatment (3×105 pfu × 3 doses) in an SNU-16 human tumor xenograft model using non-invasive bioluminescence imaging.

RESULTS

CF33-OVs showed dose-dependent infection, replication, and killing of both diffuse and intestinal subtypes of human GC cell lines. Immunofluorescence imaging showed virus-encoded GFP, hNIS, and anti-PD-L1 antibody scFv expression in CF33-OV-infected GC cells. We confirmed GC cell surface PD-L1 blockade by virus-encoded anti-PD-L1 scFv using flow cytometry. In the xenograft model, CF33-hNIS-antiPDL1 (IP; 3×105 pfu × 3 doses) treatment significantly reduced peritoneal tumors (p<0.0001), decreased amount of ascites (62.5% PBS vs 25% CF33-hNIS-antiPDL1) and prolonged animal survival. At day 91, seven out of eight mice were alive in the virus-treated group versus one out of eight in the control group (p<0.01).

CONCLUSIONS

Our results show that CF33-OVs can deliver functional proteins and demonstrate effective antitumor activity in GCPM models when delivered intraperitoneally. These preclinical results will inform the design of future peritoneal-directed therapy in GCPM patients.

Identification of a Novel Long Non-coding RNA, lnc-ATMIN-4:2, and its Clinicopathological and Prognostic Significance in Advanced Gastric Cancer

BACKGROUND/AIM

Long non-coding RNAs (lncRNAs) are emerging as significant regulators of gene expression and a novel promising biomarker for cancer diagnosis and prognosis. This study identified a novel, differentially expressed lncRNA in advanced gastric cancer (AGC), Inc-ATMIN-4:2, and evaluated its clinicopathological and prognostic significance.

PATIENTS AND METHODS

Whole transcriptome sequencing was performed to identify differentially expressed lncRNAs in AGC tissue samples. We also analyzed lnc-ATMIN-4:2 expression in 317 patients with AGC using RNA in situ hybridization.

RESULTS

High (>30 dots) lnc-ATMIN-4:2 expression significantly correlated with younger age, poorly differentiated histology, diffuse type, deeper invasion depth, perineural invasion, lymph node metastasis, and higher stage group. In addition, high lnc-ATMIN-4:2 expression was significantly associated with worse overall survival in patients with AGC.

CONCLUSION

This study elucidated the significance of lncRNAs in AGC and indicated the value of lnc-ATMIN-4:2 expression as a predictive biomarker for the overall survival of patients with AGC.

Global leaders malnutrition initiative-defined malnutrition affects long-term survival of different subgroups of patients with gastric cancer: A propensity score-matched analysis

As defined by the Global Leaders Malnutrition Initiative (GLIM), malnutrition is strongly associated with a lower quality of life and poor prognosis in gastric cancer patients. However, few studies have precisely explored the predictors of malnutrition, as defined by the GLIM, for overall survival (OS) after gastric cancer surgery in subgroups of patients stratified according to population characteristics. Our research aimed to analyze whether the predictors of malnutrition defined by the GLIM for postoperative OS in gastric cancer patients differ across subgroups. Patients who underwent radical gastric cancer surgery at our center between July 2014 and February 2019 were included in the study. Propensity score matching (PSM) was used to minimize bias. The study population was divided into malnourished and normal groups based on whether they were malnourished as defined by the GLIM. Univariate and multivariate analyses were performed to identify the risk factors affecting OS. The Kaplan–Meier curve and log-rank test were performed to determine the survival rate difference between subgroups. Overall, 1,007 patients were enrolled in the research. Multivariate analysis showed that malnutrition among the patients was 33.47%. Additionally, GLIM-defined malnutrition was an independent risk factor [hazard ratio (HR): 1.429, P = 0.001] for a shorter OS in gastric cancer patients. Furthermore, subgroup analysis showed that the GLIM was more appropriate for predicting OS in older aged patients (≥65 years), females, those with comorbidities (Charlson comorbidity index ≥ 2), and those with advanced gastric cancer (TNM stage = 3). GLIM-defined malnutrition affects the long-term survival of gastric cancer patients, especially older patients, females, patients with comorbidities, and patients with advanced gastric cancer.

Prevalence of Epstein-Barr Virus Infection and Mismatch Repair Protein Deficiency and the Correlation of Immune Markers in Tibetan Patients with Gastric Cancer

Background

Gastric cancer (GC) is a major cause of cancer-related death in China. Immunotherapies based on PD-1/PD-L1 inhibitors have improved the survival of some patients with GC. Epstein–Barr virus (EBV) infection, mismatch repair (MMR) deficiency, and tumor immune microenvironment (TIME) markers (such as CD3, CD8, and PD-L1) may help to identify specific patients who will respond to PD-1/PD-L1 inhibitors. Considering racial heterogeneity, the pattern of TIME markers in Tibetan patients with GC is still unclear. We aimed to identify the prevalence of EBV infection and the MMR status and their association with immune markers in Tibetan GC to aid in patient selection for immunotherapy.

Materials and Methods

From 2001 to 2015, we retrospectively collected 120 tissue samples from consecutive Tibetan GC patients and constructed tissue microarrays. EBV infection was assessed by Epstein–Barr-encoded RNA (EBER) in situ hybridization, and MMR protein levels were measured. Immune markers (including CD3 and CD8) in intraepithelial, stromal, and total areas were detected by immunohistochemistry (IHC). PD-L1 expression was assessed by the combined positive score (CPS). We also analyzed the relationships of EBV infection and MMR status with immune markers.

Results

Of the 120 samples, 11 (9.17%) were EBV positive (+), and 6 (5%) were MMR deficient (dMMR). PD-L1 CPS ≥1% was found in 32.5% (39/120) of Tibetan GC patients. EBV infection was associated with higher numbers of CD3+ T cells (P < 0.05) and CD8+ T cells (P < 0.05) and higher PD-L1 expression (P < 0.05). For the limited number of dMMR patients, no significant relationship was observed between dMMR and TIME markers (P > 0.05).

Conclusions

In Tibetan GC patients, the rates of EBV infection, dMMR, and positive PD-L1 expression were 9.17%, 5%, and 32.5%, respectively. EBV infection was associated with the numbers of CD3+ T cells and CD8+ T cells and PD-L1 expression within the tumor. These markers may guide the selection of Tibetan GC patients for immunotherapy.

How to examine anastomotic integrity intraoperatively in totally laparoscopic radical gastrectomy? Methylene blue testing prevents technical defect-related anastomotic leaks

BACKGROUND

Intraoperative methylene blue testing (IMBT), air leak testing, or endoscopy is used to assess the anastomotic integrity of esophagojejunostomy during open total gastrectomy for gastric cancer. Totally laparoscopic radical gastrectomy has been widely used to treat gastric cancer in the last few decades. However, reports on testing anastomotic integrity in totally laparoscopic radical gastrectomy are limited.

AIM

To explore the effects of IMBT on the incidence of postoperative anastomotic leaks (PALs) and identify the risk factors for PALs in totally laparoscopic radical gastrectomy.

METHODS

From January 2017 to December 2019, patients who underwent totally laparoscopic radical gastrectomy at the Shaanxi Provincial People's Hospital were retrospectively analyzed. According to whether or not they experienced an IMBT, the patients were divided into an IMBT group and a control group. If the IMBT was positive, an intraoperative suture was required to reinforce the anastomosis. The difference in the incidence of PALs was compared, and the risk factors were investigated.

RESULTS

This study consisted of 513 patients, 211 in the IMBT group and 302 in the control group. Positive IMBT was shown in seven patients (3.3%) in the IMBT group, and no PAL occurred in these patients after suture reinforcement. Multivariate analysis showed that risk factors for predicting positive IMBT were body mass index (BMI) > 25 kg/m² (hazard ratio [HR] = 8.357, P = 0.009), operation time > 4 h (HR = 55.881, P = 0.002), and insufficient surgical experience (HR = 15.286, P = 0.010). Moreover, 15 patients (2.9%) developed PALs in 513 patients, and the rates of PALs were significantly lower in the IMBT group than in the control group [2 of 211 patients (0.9%) vs 13 of 302 patients (4.3%), P = 0.0026]. Further analysis demonstrated that preoperative complications (HR = 13.128, P = 0.017), totally laparoscopic total gastrectomy (HR = 9.075, P = 0.043), and neoadjuvant chemotherapy (HR = 7.150, P = 0.008) were independent risk factors for PALs.

CONCLUSION

IMBT is an effective method to evaluate the integrity of anastomosis during totally laparoscopic radical gastrectomy, thus preventing technical defect-related anastomotic leaks. Preoperative complications, totally laparoscopic total gastrectomy, and neoadjuvant chemotherapy are independent risk factors for PALs.

Inhibition of MACC1-Induced Metastasis in Esophageal and Gastric Adenocarcinomas

Simple Summary

Esophageal and Gastric Adenocarcinomas (AGE/S) are characterized by early metastasis and poor survival. MACC1 (Metastasis Associated in Colon Cancer 1) acts in colon cancer as a metastasis inducer and is linked to reduced survival. In this study, we analyzed the prognostic role of MACC1 in a large AGE/S cohort and the potential of MACC1 inhibition in vitro and in vivo. MACC1 is an independent negative prognostic marker in our cohort. In vitro, migration was enhanced by MACC1 in overexpressing cells. This MACC1-related effect could be inhibited by using selumetinib in vitro. In vivo, MACC1 induced faster and larger metastasis development, which could be inhibited by selumetinib. In conclusion, MACC1 is a strong negative prognostic factor in AGE/S and is a potential target for therapy with selumetinib.

Abstract

Esophageal and Gastric Adenocarcinomas (AGE/S) are characterized by early metastasis and poor survival. MACC1 (Metastasis Associated in Colon Cancer 1) acts in colon cancer as a metastasis inducer and is linked to reduced survival. This project illuminates the role and potential for the inhibition of MACC1 in AGE/S. Using 266 of 360 TMAs and survival data of AGE/S patients, we confirm the value of MACC1 as an independent negative prognostic marker in AGE/S patients. MACC1 gene expression is correlated with survival and morphological characteristics. In vitro analysis of lentivirally MACC1-manipulated subclones of FLO-1 and OE33 showed enhanced migration induced by MACC1 in both cell line models, which could be inhibited by the MEK1 inhibitor selumetinib. In vivo, the efficacy of selumetinib on tumor growths and metastases of MACC1-overexpressing FLO-1 cells xenografted intrasplenically in NOG mice was tested. Mice with high-MACC1-expressing cells developed faster and larger distant metastases. Treatment with selumetinib led to a significant reduction in metastasis exclusively in the MACC1-positive xenografts. MACC1 is an enhancer of tumor aggressiveness and a predictor of poor survival in AGE/S. This effect can be inhibited by selumetinib.

S100A4 Is a Strong Negative Prognostic Marker and Potential Therapeutic Target in Adenocarcinoma of the Stomach and Esophagus

Deregulated Wnt-signaling is a key mechanism driving metastasis in adenocarcinoma of the gastroesophageal junction and stomach (AGE/S). The oncogene S100A4 was identified as a Wnt-signaling target gene and is known to promote metastasis. In this project, we illuminate the role of S100A4 for metastases development and disease prognosis of AGE/S. Five gastric cancer cell lines were assessed for S100A4 expression. Two cell lines with endogenous high S100A4 expression were used for functional phenotyping including analysis of proliferation and migration after stable S100A4 knock-down. The prognostic value of S100A4 was evaluated by analyzing the S100A4 expression of tissue microarrays with samples of 277 patients with AGE/S. S100A4 knock-down induced lower migration in FLO1 and NCI-N87 cells. Treatment with niclosamide in these cells led to partial inhibition of S100A4 and to reduced migration. Patients with high S100A4 expression showed lower 5-year overall and disease-specific survival. In addition, a larger share of patients in the S100A4 high expressing group suffered from metachronous metastasis. This study identifies S100A4 as a negative prognostic marker for patients with AGE/S. The strong correlation between S100A4 expression, metastases development and patient survival might open opportunities to use S100A4 to improve the prognosis of these patients and as a therapeutic target for intervention in this tumor entity.

Identifying Prognostic Markers From Clinical, Radiomics, and Deep Learning Imaging Features for Gastric Cancer Survival Prediction

BACKGROUND

Gastric cancer is one of the leading causes of cancer death in the world. Improving gastric cancer survival prediction can enhance patient prognostication and treatment planning.

METHODS

In this study, we performed gastric cancer survival prediction using machine learning and multi-modal data of 1061 patients, including 743 for model learning and 318 independent patients for evaluation. A Cox proportional-hazard model was trained to integrate clinical variables and CT imaging features (extracted by radiomics and deep learning) for overall and progression-free survival prediction. We further analyzed the prediction effects of clinical, radiomics, and deep learning features. Concordance index (c-index) was used as the model performance metric, and the predictive effects of multi-modal features were measured by hazard ratios (HRs) at pre- and post-operative settings.

RESULTS

Among 318 patients in the independent testing group, the hazard predicted by Cox from multi-modal features is associated with their survival. The highest c-index was 0.783 (95% CI, 0.782-0.783) and 0.770 (95% CI, 0.769-0.771) for overall and progression-free survival prediction, respectively. The post-operative variables are significantly (p<0.001) more predictive than the pre-operative variables. Pathological tumor stage (HR=1.336 [overall survival]/1.768 [progression-free survival], p<0.005), pathological lymph node stage (HR=1.665/1.433, p<0.005), carcinoembryonic antigen (CEA) (HR=1.632/1.522, p=0.02), chemotherapy treatment (HR=0.254/0.287, p<0.005), radiomics signature [HR=1.540/1.310, p<0.005], and deep learning signature [HR=1.950/1.420, p<0.005]) are significant survival predictors.

CONCLUSION

Our study showed that CT radiomics and deep learning imaging features are significant pre-operative predictors, providing additional prognostic information to the pathological staging markers. Lower CEA levels and chemotherapy treatments also increase survival chances. These findings can enhance gastric cancer patient prognostication and inform treatment planning.

A novel nomogram for predicting survival of patients with poorly differentiated gastric adenocarcinoma

Background

Poorly differentiated gastric adenocarcinoma (PDGA) is a common adenocarcinoma with less glandular structure in gastric cancer. To date, the factors affecting its prognosis remain unclear. In this study, we establish a novel prognostic nomogram for PDGA.

Methods

We screened the Surveillance, Epidemiology, and End Results (SEER) database and downloaded data from PDGA patients who underwent surgery between 2010 and 2015. We explored their clinicopathological characteristics and important prognostic factors such as overall survival (OS), using univariate and multivariate Cox proportional hazards regression analyses, then constructed a prognostic nomogram using the resulting significant variables to predict the OS. We verified performance of the nomogram externally using a separate Chinese set, and further compared its ability as well as the 8^th edition of the American Joint Committee on Cancer (AJCC) staging system to predict prognosis.

Results

A total of 3,887 patients in the SEER database met our inclusion criteria and were therefore included in the analysis. Multivariate analysis showed that age, sex, tumor size, prime site of tumor, T stage, N stage, and M stage were all independent prognostic factors for PDGA. These factors allowed successful establishment of a nomogram model with high predictive power, based on external verification using a Chinese set comprising 632 PDGA patients. The nomogram showed a better discrimination advantage than the 8^th edition of the AJCC staging system in predicting OS (C-index of nomogram vs. AJCC staging for SEER set: 0.707 vs. 0.663; Chinese set: 0.788 vs. 0.713).

Conclusions

The nomogram, established herein, was more accurate in predicting the 1-, 3-, and 5-year OS of PDGA patients than the traditional AJCC TNA staging system. Successful establishment of a PDGA prognostic nomogram is a further step towards individualized and precise treatment of gastric cancer.

Impact of neoadjuvant therapy followed by laparoscopic radical gastrectomy with D2 lymph node dissection in Western population: A multi-institutional propensity score-matched study

Background and Objectives

In the setting of a minimally invasive approach, we aimed to compare short and long‐term postoperative outcomes of patients treated with neoadjuvant therapy (NAT) + surgery or upfront surgery in Western population.

Methods

All consecutive patients from six Italian and one Serbian center with locally advanced gastric cancer who had undergone laparoscopic gastrectomy with D2 lymph node dissection were selected between 2005 and 2019. After propensity score‐matching, postoperative morbidity and oncologic outcomes were investigated.

Results

After matching, 97 patients were allocated in each cohort with a mean age of 69.4 and 70.5 years. The two groups showed no difference in operative details except for a higher conversion rate in the NAT group (p = 0.038). The overall postoperative complications rate significantly differed between NAT + surgery (38.1%) and US (21.6%) group (p = 0.019). NAT was found to be related to a higher risk of postoperative morbidity in patients older than 60 years old (p = 0.013) but not in patients younger (p = 0.620). Conversely, no difference in overall survival (p = 0.41) and disease‐free‐survival (p = 0.34) was found between groups.

Conclusions

NAT appears to be related to a higher postoperative complication rate and equivalent oncological outcomes when compared with surgery alone. However, poor short‐term outcomes are more evident in patients over 60 years old receiving NAT.

Prediction Model of Lymph Node Metastasis Risk in Elderly Patients with Early Gastric Cancer before Endoscopic Resection: A Retrospective Analysis Based on International Multicenter Data

Background: Patients with early gastric cancer (EGC) must suffer reoperation if diagnosed with a high possibility of lymph node (LN) metastasis. The purpose of the current study was to develop and validate a model to predict the risk of LN metastasis in elderly patients before endoscopic resection.

Methods: A total of 1911 EGC patients who had undergone radical surgery were selected and assigned randomly (2:1) to either the training cohort or the validation cohort. A nomogram was established based on the univariate and multivariate logistic regression models using the training cohort. Cox proportional hazards regression models were applied to identify the prognostic factors in univariate and multivariable analyses.

Results: Three variables—tumor size, grade, and T stage—were derived from the multivariate analyses in the training cohort and incorporated into the nomogram. The AUC of the nomogram was 0.732 in the training cohort and 0.706 in the validation cohort. There were significant differences in survival among patients with different degrees of LN metastasis risk (training cohort: five-year disease-specific survival (DSS): low risk 88.1% vs. moderate risk 80.0% vs. high risk 72.9%, P < 0.001; validation cohort five-year DSS: low risk 89.0% vs. moderate risk 84.3% vs. high risk 72.2%, P < 0.001). The LN metastasis risk assessed from the model was also an independent prognostic factor.

Conclusion: We established a nomogram that accurately predicts LN metastasis risk for elderly patients with EGC before endoscopic resection to avoid further injury from reoperation.

Attrition during neoadjuvant chemotherapy for gastric adenocarcinoma is associated with decreased survival: A United States Safety-Net Collaborative analysis

BACKGROUND

Neoadjuvant chemotherapy (NAC) is standard management for localized gastric cancer (GC). Attrition during NAC due to treatment-related toxicity or functional decline is considered a surrogate for worse biologic outcomes; however, data supporting this paradigm are lacking. We investigated factors predicting attrition and its association with overall survival (OS) in GC.

METHODS

Patients with nonmetastatic GC initiating NAC were identified from the US Safety-Net Collaborative (2012-2014). Patient/treatment-related characteristics were compared between attrition/nonattrition cohorts. Cox models determined factors associated with OS.

RESULTS

Of 116 patients initiating NAC, attrition during prescribed NAC occurred in 24%. No differences were observed in performance status, comorbidities, treatment at safety-net hospital, or clinicopathologic factors between cohorts. Despite absence of distinguishing factors, attrition was associated with worse OS (median: 11 vs. 37 months; p = 0.01) and was an independent predictor of mortality (hazard ratio [HR]: 4.7, 95% confidence interval [CI]: 1.5-15.2; p = 0.02). Fewer patients with attrition underwent curative-intent surgery (39% vs. 89%; p < 0.001). Even in patients undergoing surgical exploration (n = 89), NAC attrition remained an independent predictor of worse OS (HR: 50.8, 95% CI: 3.6-717.8; p = 0.004) despite similar receipt of adjuvant chemotherapy.

CONCLUSION

Attrition during NAC for nonmetastatic GC is independently associated with worse OS, even in patients undergoing surgery. Attrition during NAC may reflect unfavorable tumor biology not captured by conventional staging metrics.

Laparoscopic gastrectomy for stage II and III advanced gastric cancer: long‑term follow‑up data from a Western multicenter retrospective study

Introduction

There has been an increasing interest for the laparoscopic treatment of early gastric cancer, especially among Eastern surgeons. However, the oncological effectiveness of Laparoscopic Gastrectomy (LG) for Advanced Gastric Cancer (AGC) remains a subject of debate, especially in Western countries where limited reports have been published. The aim of this paper is to retrospectively analyze short- and long-term results of LG for AGC in a real-life Western practice.

Materials and methods

All consecutive cases of LG with D2 lymphadenectomy for AGC performed from January 2005 to December 2019 at seven different surgical departments were analyzed retrospectively. The primary outcome was diseases-free survival (DFS). Secondary outcomes were overall survival (OS), number of retrieved lymph nodes, postoperative morbidity and conversion rate.

Results

A total of 366 patients with stage II and III AGC underwent either total or subtotal LG. The mean number of harvested lymph nodes was 25 ± 14. The mean hospital stay was 13 ± 10 days and overall postoperative morbidity rate 27.32%, with severe complications (grade ≥ III) accounting for 9.29%. The median follow-up was 36 ± 16 months during which 90 deaths occurred, all due to disease progression. The DFS and OS probability was equal to 0.85 (95% CI 0.81–0.89) and 0.94 (95% CI 0.92–0.97) at 1 year, 0.62 (95% CI 0.55–0.69) and 0.63 (95% CI 0.56–0.71) at 5 years, respectively.

Conclusion

Our study has led us to conclude that LG for AGC is feasible and safe in the general practice of Western institutions when performed by trained surgeons.

Prediction of Survival Outcomes Based on Preoperative Clinical Parameters in Gastric Cancer

BACKGROUND

Few current preoperative risk assessment tools provide essential, optimized treatment for gastric cancer. The purpose of this study was to develop and validate a nomogram that uses preoperative data to predict survival and risk assessments.

METHODS

A survival prediction model was constructed using data from a developmental cohort of 1251 patients with stage I to III gastric cancer who underwent curative resection between January 2005 and December 2008 at Ajou University Hospital, Korea. The model was internally validated for discrimination and calibrated using bootstrap resampling. To externally validate the model, data from a validation cohort of 2012 patients with stage I to III gastric cancer who underwent surgery at multiple centers in Korea between January 2001 and June 2006 were analyzed. Analyses included the model's discrimination index (C-index), calibration plots, and decision curve that predict overall survival.

RESULTS

Eight independent predictors, including age, sex, clinical tumor size, macroscopic features, body mass index, histology, clinical stages, and tumor location, were considered for developing the nomogram. The discrimination index was 0.816 (adjusted C-index) in the developmental cohort and 0.781 (adjusted C-index) in the external validation cohort. Additionally, in both the developmental and validation datasets, age and tumor size were significantly correlated with each other and were independent indicators for survival (P < 0.05).

CONCLUSIONS

We developed a new nomogram by using the most common and significant preoperative parameters that can help to identify high-risk patients before treatment and help clinicians to make appropriate decisions for patients with stage I to III gastric cancer.

Development and Validation of a Prognostic Nomogram for Gastric Signet Ring Cell Carcinoma: A Multicenter Population-Based Study

Background

Gastric signet ring cell carcinoma (GSRCC) is a rare disease associated with poor prognosis. A prognostic nomogram was developed and validated in this study to assess GSRCC patients’ overall survival (OS).

Methods

Patients diagnosed with GSRCC from the Surveillance, Epidemiology, and End Results (SEER) database (2004–2016) and the First Hospital of China Medical University (CMU1h) were enrolled in this retrospective cohort study. Univariate and multivariate COX analysis was used to determine independent prognostic factors to construct the prognostic nomogram. Predictions were evaluated by the C-index and calibration curve. In addition, the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and Kaplan-Meier analysis were employed to assess the clinical utility of the survival prediction model.

Results

Patients were classified into two cohorts. We randomly divided patients in the SEER database and CMU1h cohort into a training group (n=3068, 80%) and a validation group (n=764, 20%). Age, race, T stage, N stage, M stage, therapy, and tumor size were significantly associated with the prognosis of GSRCC patients. On this basis, a nomogram was constructed, with a C-index in the training and the validation cohorts at 0.772 (95% CI: 0.762–0.782) and 0.774 (95% CI: 0.752–0.796), respectively. The accuracy of the generated nomogram was verified through calibration plots. Similarly, compared with the traditional AJCC staging system, the results of the area under curve (AUC) calculated by ROC, DCA, and Kaplan-Meier curves, demonstrated a good predictive value of the constructed nomogram, compared to the traditional AJCC staging system.

Conclusion

In the present study, seven independent prognostic factors of GSRCC were screened out. The established nomogram models based on seven variables provided a visualization of each prognostic factor’s risk and assisted clinicians in predicting the 1-, 3-, and 5-year OS of GSRCC.

A novel prognostic nomogram utilizing the 2018 FIGO staging system for cervical cancer: A large multicenter study

OBJECTIVE

To evaluate the prognostic performance of the revised 2018 FIGO staging system for cervical cancer.

METHODS

This retrospective multicenter study enrolled cervical cancer patients with 2009 FIGO Stage IA1-IIA2 who underwent surgeries between January 2006 and December 2017 in four tertiary hospitals. Patients were restaged according to the 2018 FIGO staging system by reviewing their medical data.

RESULTS

Of 3238 cervical cancer patients included, 1841 (56.9%) patients were restaged: 641 (34.9%) due to tumor size, 544 (29.5%) due to lymph node metastasis, 614 (33.4%) due to the inconsistency between pre- and postoperative assessments, and 42 due to the cancellation of invasion width in Stage IA. After restaging, a clear tendency of decreased recurrence-free survival (RFS) and overall survival (OS) with increasing stage was observed. Multivariate Cox analysis showed that 2018 FIGO stage, parametrial involvement, and histology were independent prognostic factors for both OS and RFS (P < 0.05). Based on these factors, we established predictive nomograms with c-indexes of 0.735 and 0.721, showing good predictive ability for cervical cancer.

CONCLUSION

The revised 2018 FIGO staging system can better reflect the survival of cervical cancer patients. Based on it, we established a nomogram that can predict the prognosis of cervical cancer patients more precisely.

The Clinical Significance of Lymphovascular Invasion in Gastric Cancer

BACKGROUND/AIM

Lymphovascular invasion (LVI) is recognized as a prognostic predictor of recurrence in certain carcinomas. According to current Japanese guidelines, however, in gastric cancer, LVI is not clinically useful information, except for predicting the curability of endoscopic resection. We clarified the clinical significance of LVI in gastric cancer and its correlation with disease prognosis.

PATIENTS AND METHODS

A total of 2,090 cases of gastric cancer undergoing radical gastrectomy were enrolled. The correlation of LVI and other histopathological factors was evaluated with regards to patient prognosis.

RESULTS

LVI(+) was noted in 894 cases. A multivariate analysis showed that pT, pN, and LVI were independent risk factors for patient prognosis. In pT2-4 patients with nodal metastasis, a significant difference was revealed, and the 5-year overall survival rates in LVI(+) cases were lower than those in LVI(-) (60.9% vs. 76.7%, p=0.005).

CONCLUSION

LVI in gastric cancer is an independent prognostic factor, and tends to worsen prognosis, especially in advanced cancers with lymph node metastases.

Targeting Fluorescence Imaging of RGD-Modified Indocyanine Green Micelles on Gastric Cancer

Early diagnosis and complete resection of the tumor is an important way to improve the quality of life of patients with gastric cancer. In recent years, near-infrared (NIR) materials show great potential in fluorescence-based imaging of the tumors. To realize a satisfying intraoperative fluorescence tumor imaging, there are two pre-requirements. One is to obtain a stable agent with a relatively longer circulation time. The second is to make it good biocompatible and specific targeting to the tumor. Here, we developed an RGD-modified Distearyl acylphosphatidyl ethanolamine-polyethylene glycol micelle (DSPE-PEG-RGD) to encapsulate indocyanine green (ICG) for targeting fluorescence imaging of gastric cancer, aimed at realizing tumor-targeted accumulation and NIR imaging. ¹H NMR spectroscopy confirmed its molecular structure. The characteristics and stability results indicated that the DSPE-PEG-RGD@ICG had a relatively uniform size of <200 nm and longer-term fluorescence stability. RGD peptides had a high affinity to integrin α_vβ₃ and the specific targeting effect on SGC7901 was assessed by confocal microscopy in vitro. Additionally, the results of cytotoxicity and blood compatibility in vitro were consistent with the acute toxicity test in vivo, which revealed good biocompatibility. The biodistribution and tumor targeting image of DSPE-PEG-RGD@ICG were observed by an imaging system in tumor-bearing mice. DSPE-PEG-RGD@ICG demonstrated an improved accumulation in tumors and longer circulation time when compared with free ICG or DSPE-PEG@ICG. In all, DSPE-PEG-RGD@ICG demonstrated ideal properties for tumor target imaging, thus, providing a promising way for the detection and accurate resection of gastric cancer.

The incidence, risk factors and predictive nomograms for early death among patients with stage IV gastric cancer: a population-based study

Background

Although advances in the treatment of stage IV gastric cancer (GC) patients, some patients were observed to die within 3 months of initial diagnosis. The present study aimed to explore the early mortality and risk factors for stage IV GC and further develop nomograms.

Methods

A total of 2,174 eligible stage IV GC patients were selected from the Surveillance, Epidemiology, and End Results database. Logistic regression analyses were used to determine the risk factors and develop the nomograms to predict all-cause early death and cancer-specific early death. The predictive performance of the nomograms was assessed by receiver operating characteristic curves (ROC), calibration plots and decision curve analyses (DCA) in both training and validation cohorts.

Results

Of 2,174 patients enrolled, 708 died within 3 months of initial diagnosis (n=668 for cancer-specific early death). Early mortality remained stable from 2010-2015. Non-Asian or Pacific Islander (API) race, poorer differentiation, middle sites of the stomach, no surgery, no radiotherapy, no chemotherapy, lung metastases and liver metastases were associated with high risk of both all-causes early death and cancer-specific early death. The nomograms constructed based on these factors showed favorable sensitivity, with the area under the ROC range of 0.816-0.847. The calibration curves and DCAs also exhibited adequate fit and ideal net benefit in prediction and clinical application.

Conclusions

Approximately one-third of stage IV GC patients experienced early death. These associated risk factors and predictive nomograms may help clinicians identify the patients at high risk of early death and be the reference for treatment choices.

A nomogram for prediction of stage III/IV gastric cancer outcome after surgery: A multicenter population-based study

Most patients with gastric cancer (GC) are first diagnosed at stage III‐IV and surgery resection remains the primary therapeutic modality for these patients. However, clinical staging used for prediction of those patients provides limited information. We collected clinicopathological data and disease‐progression information from 508 patients with stage III‐IV GC at three Chinese hospitals and 1298 patients from the Surveillance, Epidemiology, and End Results database. Based on the stepwise multivariate regression model, we constructed a novel nomogram to predict overall survival (OS). The performance of discrimination for this model was measured using Harrell's concordance index (C‐index) and receiver‐operating characteristic curve (ROC), and was validated using calibration plots. Multivariate Cox regression analyses showed that tumor size, age at diagnosis, N stage, tumor grade, and distant metastases were outstanding independent prognostic factors of stage III‐IV GC. We developed a nomogram based on these five prognostic predictors. In the training set, the C‐index of the nomogram was 0.645 (95% CI: 0.611‐0.679), which was higher than that of the American Joint Committee on Cancer TNM system alone (sixth TNM: 0.544; seventh TNM: 0.575; eighth TNM: 0.568). Similar results were observed in validation cohort. Moreover, calibration blots demonstrated good consistency between the actual and predicted OS probabilities. According to the nomogram, GC individuals could be classified into three groups (low‐, middle‐, and high‐risk) (P < .001). Our nomogram complements the current staging system for prediction of individual prognosis with stage III‐IV GC, and may be helpful for making individualized treatment decisions.