A nomogram for predicting the likelihood of lymph node metastasis in early gastric patients

Development and validation of a nomogram for predicting lymph node metastasis in early gastric cancer

BACKGROUND

Lymph node metastasis (LNM) significantly impacts the treatment and prognosis of early gastric cancer (EGC). Consequently, the precise prediction of LNM risk in EGC patients is essential to guide the selection of appropriate surgical approaches in clinical settings.

AIM

To develop a novel nomogram risk model for predicting LNM in EGC patients, utilizing preoperative clinicopathological data.

METHODS

Univariate and multivariate logistic regression analyses were performed to examine the correlation between clinicopathological factors and LNM in EGC patients. Additionally, univariate Kaplan-Meier and multivariate Cox regression analyses were used to assess the influence of clinical factors on EGC prognosis. A predictive model in the form of a nomogram was developed, and its discrimination ability and calibration were also assessed.

RESULTS

The incidence of LNM in the study cohort was 19.6%. Multivariate logistic regression identified tumor size, location, degree of differentiation, and pathological type as independent risk factors for LNM in EGC patients. Both tumor pathological type and LNM independently affected the prognosis of EGC. The model's performance was reflected by an area under the curve of 0.750 [95% confidence interval (CI): 0.701-0.789] for the training group and 0.763 (95%CI: 0.687-0.838) for the validation group.

CONCLUSION

A clinical prediction model was constructed (using tumor size, low differentiation, location in the middle-lower region, and signet ring cell carcinoma), with its score being a significant prognosis indicator.

Development and Validation of Models to Predict Lymph Node Metastasis in Early Gastric Cancer Using Logistic Regression and Gradient Boosting Machine Methods

PURPOSE

To identify important features of lymph node metastasis (LNM) and develop a prediction model for early gastric cancer (EGC) using a gradient boosting machine (GBM) method.

MATERIALS AND METHODS

The clinicopathologic data of 2556 patients with EGC who underwent gastrectomy were used as training set and the internal validation set (set 1) at a ratio of 8:2. Additionally, 548 patients with EGC who underwent endoscopic submucosal dissection (ESD) as the initial treatment were included in the external validation set (set 2). The GBM model was constructed, and its performance was compared with that of the Japanese guidelines.

RESULTS

LNM was identified in 12.6% (321/2556) of the gastrectomy group (training set & set 1) and 4.3% (24/548) of the ESD group (set 2). In the GBM analysis, the top five features that most affected LNM were lymphovascular invasion, depth, differentiation, size, and location. The accuracy, sensitivity, specificity, and the area under the receiver operating characteristics of set 1 were 0.566, 0.922, 0.516, and 0.867, while those of set 2 were 0.810, 0.958, 0.803, and 0.944, respectively. When the sensitivity of GBM was adjusted to that of Japanese guidelines (beyond the expanded criteria in set 1 [0.922] and eCuraC-2 in set 2 [0.958]), the specificities of GBM in sets 1 and 2 were 0.516 (95% confidence interval, 0.502-0.523) and 0.803 (0.795-0.805), while those of the Japanese guidelines were 0.502 (0.488-0.509) and 0.788 (0.780-0.790), respectively.

CONCLUSION

The GBM model showed good performance comparable with the eCura system in predicting LNM risk in EGCs.

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.

Optimizing the Choice for Adjuvant Chemotherapy in Gastric Cancer

Simple Summary

Gastric cancer is the fourth largest cause of tumor-related death worldwide. Despite advances in the management of resectable cancer and improvements in early diagnosis, especially in east Asia where screening campaigns are actively performed, many patients experience recurrence and die because of the disease. Adjuvant systemic chemotherapy is administered after radical surgery in order to reduce the risk of recurrence and death. The modality of administration and regimens of chemotherapy in this setting are different between Eastern and Western countries. In Asia, adjuvant chemotherapy is traditionally given after surgery, while in Europe it is commonly scheduled after preoperative chemotherapy and surgery (perioperative chemotherapy), and in Northern America it is usually combined with radiotherapy (chemoradiotherapy). All these approaches are sustained by well-designed phase III clinical studies, and none may be considered superior to the others in the absence of head-to-head comparisons. The identification of predictive and/or prognostic factors could help to select patients at higher risk of recurrence and those more likely to receive a benefit from the adjuvant treatment. This would allow clinicians to avoid the administration of undue toxicity to non-responder patients and even to reduce the cost of unnecessary treatment.

Abstract

Advances in the management of gastric cancer have improved patient survival in the last decade. Nonetheless, the number of patients relapsing and dying after a diagnosis of localized gastric cancer is still too high, even in early stages (10% in stage I). Adjuvant systemic chemotherapy has been proven to significantly improve outcomes. In the present article we have critically reviewed the clinical trials that guide the current clinical practice in the adjuvant treatment of patients affected by resectable gastric cancer, focusing on the different approaches worldwide, i.e., adjuvant chemotherapy, adjuvant chemoradiotherapy, and perioperative chemotherapy. We also delineate the clinical–pathological characteristics that are commonly taken into account to identify patients at a higher risk of recurrence and requiring adjuvant chemotherapy, and also describe novel biomarkers and therapeutic agents that might allow personalization of the treatment.

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

Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage

Background

Intracerebral hemorrhage (ICH) is a stroke syndrome with an unfavorable prognosis. Currently, there is no comprehensive clinical indicator for mortality prediction of ICH patients. The purpose of our study was to construct and evaluate a nomogram for predicting the 30-day mortality risk of ICH patients.

Methods

ICH patients were extracted from the MIMIC-III database according to the ICD-9 code and randomly divided into training and verification cohorts. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to determine independent risk factors. These risk factors were used to construct a nomogram model for predicting the 30-day mortality risk of ICH patients. The nomogram was verified by the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).

Results

A total of 890 ICH patients were included in the study. Logistic regression analysis revealed that age (OR = 1.05, P < 0.001), Glasgow Coma Scale score (OR = 0.91, P < 0.001), creatinine (OR = 1.30, P < 0.001), white blood cell count (OR = 1.10, P < 0.001), temperature (OR = 1.73, P < 0.001), glucose (OR = 1.01, P < 0.001), urine output (OR = 1.00, P = 0.020), and bleeding volume (OR = 1.02, P < 0.001) were independent risk factors for 30-day mortality of ICH patients. The calibration curve indicated that the nomogram was well calibrated. When predicting the 30-day mortality risk, the nomogram exhibited good discrimination in the training and validation cohorts (C-index: 0.782 and 0.778, respectively). The AUCs were 0.778, 0.733, and 0.728 for the nomogram, Simplified Acute Physiology Score II (SAPSII), and Oxford Acute Severity of Illness Score (OASIS), respectively, in the validation cohort. The IDI and NRI calculations and DCA analysis revealed that the nomogram model had a greater net benefit than the SAPSII and OASIS scoring systems.

Conclusion

This study identified independent risk factors for 30-day mortality of ICH patients and constructed a predictive nomogram model, which may help to improve the prognosis of ICH patients.

18F-FDG PET/CT-based radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer

Objective

Lymph node metastasis (LNM) is not only one of the important factors affecting the prognosis of gastric cancer but also an important basis for treatment decisions. The purpose of this study was to investigate the value of the radiomics nomogram based on preoperative ¹⁸F-deoxyglucose (FDG) PET/CT primary lesions and clinical risk factors for predicting LNM in gastric cancer (GC).

Methods

We retrospectively analyzed radiomics features of preoperative ¹⁸F-FDG PET/CT images in 224 gastric cancer patients from two centers. The prediction model was developed in the training cohort (n = 134) and validated in the internal (n = 59) and external validation cohorts (n = 31). The least absolute shrinkage and selection operator (LASSO) regression was used to select features and build radiomics signatures. The radiomics feature score (Rad-score) was calculated and established a radiomics signature. Multivariate logistic regression analysis was used to screen independent risk factors for LNM. The minimum Akaike’s information criterion (AIC) was used to select the optimal model parameters to construct a radiomics nomogram. The performance of the nomogram was assessed with calibration, discrimination, and clinical usefulness.

Results

There was no significant difference between the internal verification and external verification of the clinical data of patients (all p > 0.05). The areas under the curve (AUCs) (95% CI) for predicting LNM based on the ¹⁸F-FDG PET/CT radiomics signature in the training cohort, internal validation cohort, and external validation cohort were 0.792 (95% CI: 0.712–0.870), 0.803 (95% CI: 0.681–0.924), and 0.762 (95% CI: 0.579–0.945), respectively. Multivariate logistic regression showed that carbohydrate antigen (CA) 19-9 [OR (95% CI): 10.180 (1.267–81.831)], PET/CT diagnosis of LNM [OR (95% CI): 6.370 (2.256–17.984)], PET/CT Rad-score [OR (95% CI): 16.536 (5.506–49.660)] were independent influencing factors of LNM (all p < 0.05), and a radiomics nomogram was established based on those factors. The AUCs (95% CI) for predicting LNM were 0.861 (95% CI: 0.799–0.924), 0.889 (95% CI: 0.800–0.976), and 0.897 (95% CI: 0.683–0.948) in the training cohort, the internal validation cohort, and the external validation cohort, respectively. Decision curve analysis (DCA) indicated that the ¹⁸F-FDG PET/CT-based radiomics nomogram has good clinical utility.

Conclusions

Radiomics nomogram based on the primary tumor of ¹⁸F-FDG PET/CT could facilitate the preoperative individualized prediction of LNM, which is helpful for risk stratification in GC patients.

Establishment and Validation for Predicting the Lymph Node Metastasis in Early Gastric Adenocarcinoma

Lymph node metastasis (LNM) is considered to be one of the important factors in determining the optimal treatment for early gastric cancer (EGC). This study aimed to develop and validate a nomogram to predict LNM in patients with EGC. A total of 842 cases from the Surveillance, Epidemiology, and End Results (SEER) database were divided into training and testing sets with a ratio of 6 : 4 for model development. Clinical data (494 patients) from the hospital were used for external validation. Univariate and multivariate logistic regression analyses were used to identify the predictors using the training set. Logistic regression, LASSO regression, ridge regression, and elastic-net regression methods were used to construct the model. The performance of the model was quantified by calculating the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CIs). Results showed that T stage, tumor size, and tumor grade were independent predictors of LNM in EGC patients. The AUC of the logistic regression model was 0.766 (95% CI, 0.709-0.823), which was slightly higher than that of the other models. However, the AUC of the logistic regression model in external validation was 0.625 (95% CI, 0.537-0.678). A nomogram was drawn to predict LNM in EGC patients based on the logistic regression model. Further validation based on gender, age, and grade indicated that the logistic regression predictive model had good adaptability to the population with grade III tumors, with an AUC of 0.803 (95% CI, 0.606-0.999). Our nomogram showed a good predictive ability and may provide a tool for clinicians to predict LNM in EGC patients.

Construction of a Nomogram Model for Predicting Peritoneal Dissemination in Gastric Cancer Based on Clinicopathologic Features and Preoperative Serum Tumor Markers

Background

Peritoneal dissemination (PD) is the most common mode of metastasis for advanced gastric cancer (GC) with poor prognosis. It is of great significance to accurately predict preoperative PD and develop optimal treatment strategies for GC patients. Our study assessed the diagnostic potential of serum tumor markers and clinicopathologic features, to improve the accuracy of predicting the presence of PD in GC patients.

Methods

In our study, 1264 patients with GC at Fudan University Shanghai Cancer Center and Wenzhou people’s hospital from 2018 to 2020 were retrospectively analyzed, including 316 cases of PD and 948 cases without PD. All patients underwent enhanced CT scan or magnetic resonance imaging (MRI) before surgery and treatment. Clinicopathological features, including tumor diameter and tumor stage (depth of tumor invasion, nearby lymph node metastasis and distant metastasis), were obtained by imaging examination. The independent risk factors for PD were screened through univariate and multivariate logistic regression analyses, and the results were expressed with 95% confidence intervals (CIs). A model of PD diagnosis and prediction was established by using Cox proportional hazards regression model of training set. Furthermore, the accuracy of the prediction model was verified by ROC curve and calibration plots.

Results

Univariate analysis showed that PD in GC was significantly related to tumor diameter (odds ratio (OR)=12.06, p<0.0006), depth of invasion (OR=14.55, p<0.0001), lymph node metastases (OR=5.89, p<0.0001), carcinoembryonic antigen (CEA) (OR=2.50, p<0.0001), CA125 (OR=11.46, p<0.0001), CA72-4 (OR=4.09, p<0.0001), CA19-9 (OR=2.74, p<0.0001), CA50 (OR=5.20, p<0.0001) and CA242 (OR=3.83, p<0.0001). Multivariate analysis revealed that clinical invasion depth and serum marker of CA125 and CA72-4 were independent risk factors for PD. The prediction model was established based on the risk factors using the R program. The area under the curve (AUC) of the receiver operating characteristics (ROC) was 0.931 (95% CI: 0.900–0.960), with the accuracy, sensitivity and specificity values of 90.5%, 86.2% and 82.2%, respectively.

Conclusion

The nomogram model constructed using CA125, CA72-4 and depth of invasion increases the accuracy and sensitivity in predicting the incidence of PD in GC patients and can be used as an important tool for preoperative diagnosis.

A novel DNA methylation signature associated with lymph node metastasis status in early gastric cancer

Background

Lymph node metastasis (LNM) is an important factor for both treatment and prognosis of early gastric cancer (EGC). Current methods are insufficient to evaluate LNM in EGC due to suboptimal accuracy. Herein, we aim to identify methylation signatures for LNM of EGC, facilitate precision diagnosis, and guide treatment modalities.

Methods

For marker discovery, genome-wide methylation sequencing was performed in a cohort (marker discovery) using 47 fresh frozen (FF) tissue samples. The identified signatures were subsequently characterized for model development using formalin-fixed paraffin-embedded (FFPE) samples by qPCR assay in a second cohort (model development cohort, n = 302, training set: n = 151, test set: n = 151). The performance of the established model was further validated using FFPE samples in a third cohorts (validation cohort, n = 130) and compared with image-based diagnostics, conventional clinicopathology-based model (conventional model), and current standard workups.

Results

Fifty LNM-specific methylation signatures were identified de novo and technically validated. A derived 3-marker methylation model for LNM diagnosis was established that achieved an AUC of 0.87 and 0.88, corresponding to the specificity of 80.9% and 85.7%, sensitivity of 80.6% and 78.1%, and accuracy of 80.8% and 83.8% in the test set of model development cohort and validation cohort, respectively. Notably, this methylation model outperformed computed tomography (CT)-based imaging with a superior AUC (0.88 vs. 0.57, p < 0.0001) and individual clinicopathological features in the validation cohort. The model integrated with clinicopathological features demonstrated further enhanced AUCs of 0.89 in the same cohort. The 3-marker methylation model and integrated model reduced 39.4% and 41.5% overtreatment as compared to standard workups, respectively.

Conclusions

A novel 3-marker methylation model was established and validated that shows diagnostic potential to identify LNM in EGC patients and thus reduce unnecessary gastrectomy in EGC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01219-x.

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

Objective: We aimed to establish a nomogram for predicting lymph node metastasis in early gastric cancer (EGC) involving human epidermal growth factor receptor 2 (HER2).

Methods: We collected clinicopathological data of patients with EGC who underwent radical gastrectomy and D2 lymphadenectomy at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine between January 2012 and August 2018. Univariate and multivariate logistic regression analysis were used to examine the relationship between lymph node metastasis and clinicopathological features. A nomogram was constructed based on a multivariate prediction model. Internal validation from the training set was performed using receiver operating characteristic (ROC) and calibration plots to evaluate discrimination and calibration, respectively. External validation from the validation set was utilized to examine the external validity of the prediction model using the ROC plot. A decision curve analysis was used to evaluate the benefit of the treatment.

Results: Among 1,212 patients with EGC, 210 (17.32%) presented with lymph node metastasis. Multivariable analysis showed that age, tumor size, submucosal invasion, histological subtype, and HER2 positivity were independent risk factors for lymph node metastasis in EGC. The area under the ROC curve of the model was 0.760 (95% CI: 0.719–0.800) in the training set (n = 794) and 0.771 (95% CI: 0.714–0.828) in the validation set (n = 418). A predictive nomogram was constructed based on a multivariable prediction model. The decision curve showed that using the prediction model to guide treatment had a higher net benefit than using endoscopic submucosal dissection (ESD) absolute criteria over a range of threshold probabilities.

Conclusion: A clinical prediction model and an effective nomogram with an integrated HER2 status were used to predict EGC lymph node metastasis with better accuracy and clinical performance.

Significance of Lymph Node Metastasis in the Treatment of Gastric Cancer and Current Challenges in Determining the Extent of Metastasis

The stomach exhibits abundant lymphatic flow, and metastasis to lymph nodes is common. In the case of gastric cancer, there is a regularity to the spread of lymph node metastasis, and it does not easily metastasize outside the regional nodes. Furthermore, when its extent is limited, nodal metastasis of gastric cancer can be cured by appropriate lymph node dissection. Therefore, identifying and determining the extent of lymph node metastasis is important for ensuring accurate diagnosis and appropriate surgical treatment in patients with gastric cancer. However, precise detection of lymph node metastasis remains difficult. Most nodal metastases in gastric cancer are microscopic metastases, which often occur in small-sized lymph nodes, and are thus difficult to diagnose both preoperatively and intraoperatively. Preoperative nodal diagnoses are mainly made using computed tomography, although the specificity of this method is low because it is mainly based on the size of the lymph node. Furthermore, peripheral nodal metastases cannot be palpated intraoperatively, nodal harvesting of resected specimens remains difficult, and the number of lymph nodes detected vary greatly depending on the skill of the technician. Based on these findings, gastrectomy with prophylactic lymph node dissection is considered the standard surgical procedure for gastric cancer. In contrast, several groups have examined the value of sentinel node biopsy for accurately evaluating nodal metastasis in patients with early gastric cancer, reporting high sensitivity and accuracy. Sentinel node biopsy is also important for individualizing and optimizing the extent of uniform prophylactic lymph node dissection and determining whether patients are indicated for function-preserving curative gastrectomy, which is superior in preventing post-gastrectomy symptoms and maintaining dietary habits. Notably, advancements in surgical treatment for early gastric cancer are expected to result in individualized surgical strategies with sentinel node biopsy. Chemotherapy for advanced gastric cancer has also progressed, and conversion gastrectomy can now be performed after downstaging, even in cases previously regarded as inoperable. In this review, we discuss the importance of determining lymph node metastasis in the treatment of gastric cancer, the associated difficulties, and the need to investigate strategies that can improve the diagnosis of lymph node metastasis.

Scoring Model Based on Nodal Metastasis Prediction Suggesting an Alternative Treatment to Total Gastrectomy in Proximal Early Gastric Cancer

Purpose

Total gastrectomy (TG) with lymph node (LN) dissection is recommended for early gastric cancer (EGC) but is not indicated for endoscopic resection (ER). We aimed to identify patients who could avoid TG by establishing a scoring system for predicting lymph node metastasis (LNM) in proximal EGCs.

Materials and Methods

Between January 2003 and December 2017, a total of 1,025 proximal EGC patients who underwent TG with LN dissection were enrolled. Patients who met the absolute ER criteria based on pathological examination were excluded. The pathological risk factors for LNM were determined using univariate and multivariate logistic regression analyses. A scoring system for predicting LNM was developed and applied to the validation group.

Results

Of the 1,025 cases, 100 (9.8%) showed positive LNM. Multivariate analysis confirmed the following independent risk factors for LNM: tumor size >2 cm, submucosal invasion, lymphovascular invasion (LVI), and perineural invasion (PNI). A scoring system was created using the four aforementioned variables, and the areas under the receiver operating characteristic curves in both the training (0.85) and validation (0.84) groups indicated excellent discrimination. The probability of LNM in mucosal cancers without LVI or PNI, regardless of size, was <2.9%.

Conclusions

Our scoring system involving four variables can predict the probability of LNM in proximal EGC and might be helpful in determining additional treatment plans after ER, functioning as a good indicator of the adequacy of treatments other than TG in high surgical risk patients.

The risk of lymph node metastasis in gastric cancer conforming to indications of endoscopic resection and pylorus-preserving gastrectomy: a single-center retrospective study

Background

Lymph node metastasis (LNM) status is an important prognostic factor that strongly influences the treatment decision of early gastric cancer (EGC). This study aimed to evaluate the pattern and clinical significance of LNM in EGC.

Methods

A total of 354 patients with carcinoma in situ (n = 42), EGC (n = 312) who underwent radical gastrectomy were enrolled. Their clinicopathological features, pathological reports, and prognostic data were collected and analyzed.

Results

The incidence of LNM in all patients was 18.36% (65/354). The rates of D1 and D2 station metastases were 12.10% (43/354) and 6.21% (22/354), respectively. The rates of LNM in absolute indication of endoscopic resection and expanded indication were 3.27% (2/61) and 28.55% (4/14), respectively. Skip LNM was observed in 3.67% (13/354) of patients. For those with middle-third tumor, the metastasis rate of the No. 5 lymph node was 3.05% (5/164). The independent risk factors for LNM were tumors measuring > 30 mm, poorly differentiated tumors, and lymphovascular invasion (all P < 0.05; area under the curve, 0.783). The 5-year disease-free survival rates of patients with and without LNM were 96.26 and 79.17%, respectively (P = 0.011). Tumors measuring > 20 mm and LNM were independent predictive factors for poor survival outcome in all patients.

Conclusions

Patients with EGC conforming to expanded indications have a relatively high risk of LNM and may not be suitable for endoscopic submucosal dissection. Pylorus-preserving gastrectomy for patients with middle-third EGC remains controversial due to the high metastasis rate of the No. 5 lymph node.

A CT-based Radiomics Model for Prediction of Lymph Node Metastasis in Early Stage Gastric Cancer

RATIONALE AND OBJECTIVES

To develop and validate a CT-based radiomics model for preoperative prediction of lymph node metastasis (LNM) in early stage gastric cancer (EGC).

MATERIALS AND METHODS

Four hundred and sixty-three consecutive EGC patients were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase CT scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator method. The predictive performance of radiomics signature was tested in the training and testing cohorts. Multivariate logistic regression analysis was conducted to build a radiomics-based model combined radiomics signature and lymph node status according to CT. Performance of the model was determined by its discrimination, calibration, and clinical usefulness.

RESULTS

The radiomics signature comprised six robust features showed significant association with LNM in both cohorts. A radiomics model that incorporated radiomics signature and CT-reported lymph node status showed good calibration and discrimination in the training cohort (AUC = 0.91) and testing cohort (AUC = 0.89). Decision curve analysis confirmed the clinical utility of this model.

CONCLUSION

The CT-based radiomics model showed favorable accuracy for prediction of LNM in EGC and may help to improve clinical decision-making.

Deep learning analysis of the primary tumour and the prediction of lymph node metastases in gastric cancer

BACKGROUND

Lymph node metastasis (LNM) in gastric cancer is a prognostic factor and has implications for the extent of lymph node dissection. The lymphatic drainage of the stomach involves multiple nodal stations with different risks of metastases. The aim of this study was to develop a deep learning system for predicting LNMs in multiple nodal stations based on preoperative CT images in patients with gastric cancer.

METHODS

Preoperative CT images from patients who underwent gastrectomy with lymph node dissection at two medical centres were analysed retrospectively. Using a discovery patient cohort, a system of deep convolutional neural networks was developed to predict pathologically confirmed LNMs at 11 regional nodal stations. To gain understanding about the networks' prediction ability, gradient-weighted class activation mapping for visualization was assessed. The performance was tested in an external cohort of patients by analysis of area under the receiver operating characteristic (ROC) curves (AUC), sensitivity and specificity.

RESULTS

The discovery and external cohorts included 1172 and 527 patients respectively. The deep learning system demonstrated excellent prediction accuracy in the external validation cohort, with a median AUC of 0·876 (range 0·856-0·893), sensitivity of 0·743 (0·551-0·859) and specificity of 0·936 (0·672-0·966) for 11 nodal stations. The imaging models substantially outperformed clinicopathological variables for predicting LNMs (median AUC 0·652, range 0·571-0·763). By visualizing nearly 19 000 subnetworks, imaging features related to intratumoral heterogeneity and the invasive front were found to be most useful for predicting LNMs.

CONCLUSION

A deep learning system for the prediction of LNMs was developed based on preoperative CT images of gastric cancer. The models require further validation but may be used to inform prognosis and guide individualized surgical treatment.

Construction and Evaluation of a Sepsis Risk Prediction Model for Urinary Tract Infection

Background: Urinary tract infection (UTI) is one of the common causes of sepsis. However, nomograms predicting the sepsis risk in UTI patients have not been comprehensively researched. The goal of this study was to establish and validate a nomogram to predict the probability of sepsis in UTI patients.

Methods: Patients diagnosed with UTI were extracted from the Medical Information Mart for Intensive Care III database. These patients were randomly divided into training and validation cohorts. Independent prognostic factors for UTI patients were determined using forward stepwise logistic regression. A nomogram containing these factors was established to predict the sepsis incidence in UTI patients. The validity of our nomogram model was determined using multiple indicators, including the area under the receiver operating characteristic curve (AUC), correction curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision-curve analysis (DCA).

Results: This study included 6,551 UTI patients. Stepwise regression analysis revealed that the independent risk factors for sepsis in UTI patients were congestive heart failure, diabetes, liver disease, fluid electrolyte disorders, APSIII, neutrophils, lymphocytes, red blood cell distribution width, urinary protein, urinary blood, and microorganisms. The nomogram was then constructed and validated. The AUC, NRI, IDI and DCA of the nomogram all showed better performance than traditional APSIII score. The calibration curve and Hosmer-Lemeshow test results indicate that the nomogram was well-calibrated. Improved NRI and IDI values indicate that our nomogram scoring system is superior to other commonly used ICU scoring systems. The DCA curve indicates that the DCA map of the nomogram has good clinical application ability.

Conclusion: This study identified the independent risk factors of sepsis in UTI patients and used them to construct a prediction model. The present findings may provide clinical reference information for preventing sepsis in UTI patients.

A genomic-clinicopathologic Nomogram for the preoperative prediction of lymph node metastasis in gastric cancer

BACKGROUND

Preoperative evaluation of lymph node (LN) state is of pivotal significance for informing therapeutic decisions in gastric cancer (GC) patients. However, there are no non-invasive methods that can be used to preoperatively identify such status. We aimed at developing a genomic biosignature based model to predict the possibility of LN metastasis in GC patients.

METHODS

We used the RNA profile retrieving strategy and performed RNA expression profiling in a large GC cohort (GSE62254, n = 300) from Gene Expression Ominus (GEO). In the exploratory stage, 300 GC patients from GSE62254 were involved and the differentially expressed RNAs (DERs) for LN-status were determined using the R software. GC samples in GSE62254 were randomly allocated into a learning set (n = 210) and a verification set (n = 90). By using the Least absolute shrinkage and selection operator (LASSO) regression approach, a set of 23-RNA signatures were established and the signature based nomogram was subsequently built for distinguishing LN condition. The diagnostic efficiency, as well as the clinical performance of this model were assessed using the decision curve analysis (DCA). Metascape was used for bioinformatic analysis of the DERs.

RESULTS

Based on the genomic signature, we established a nomogram that robustly distinguished LN status in the learning (AUC = 0.916, 95% CI 0.833-0.999) and verification sets (AUC = 0.775, 95% CI 0.647-0.903). DCA demonstrated the clinical value of this nomogram. Functional enrichment analysis of the DERs was performed using bioinformatics methods which revealed that these DERs were involved in several lymphangiogenesis-correlated cascades.

CONCLUSIONS

In this study, we present a genomic signature based nomogram that integrates the 23-RNA biosignature based scores and Lauren classification. This model can be utilized to estimate the probability of LN metastasis with good performance in GC. The functional analysis of the DERs reveals the prospective biogenesis of LN metastasis in GC.

A population-based study: how to identify high-risk T1 gastric cancer patients?

In T1 gastric cancer (GC), lymph nodes metastasis (LNM) is considered as a significant prognostic predictor and closely associated with following therapeutic approaches as well as distant metastasis (DM). This study aimed to not only seek risk factors of LNM and DM but also unpack the prognosis in T1 GC patients. We performed a retrospective study enrolling 5547 patients in T1 GC from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression models were produced to recognize independent risk factors of LNM and DM. Cox regression analyses were performed to identify important prognostic factors of overall survival (OS). Cancer-specific cumulative incidence was plotted by cumulative incidence function. Three nomograms of LNM, DM and OS were established and validated by receiver operating characteristic (ROC) and calibration curves to evaluate discrimination and accuracy. Decision curve analysis (DCA), clinical impact curves (CIC) and subgroups based on risk scores were constructed to measure nomograms clinical utility. The area under the curve (AUC) of LNM nomogram and DM nomogram were 0.735 and 0.896, respectively. OS nomogram was constructed and the corresponding C-index was 0.797. In conclusion, our user-friendly nomograms, which aimed to predict LNM, DM and OS in T1 gastric cancer patients, have shown high efficiency of discrimination and accuracy. These useful and visual tools may have advantageous clinical utility to identify high-risk T1 gastric patients and help clinicians to draw up an individual therapeutic strategy.

Establishment of Decision Rules and Risk Assessment Model for Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer

Background: Preoperative accurate prediction of lymph node status is especially important for the formulation of treatment plans for patients with gastric cancer (GC). The purpose of this study was to establish decision rules and a risk assessment model for lymph node metastasis (LNM) in GC using preoperative indicators. Methods: The clinical data of 554 patients who underwent gastrectomy with D2 lymphadenectomy were collected. A 1:1 propensity score matching (PSM) system was used, and the clinical data of the matched 466 patients were further analyzed. The important risk factors for LNM were extracted by the random forest algorithm, and decision rules and nomogram models for LNM were constructed with a classification tree and the "rms" package of R software, respectively. Results: Tumor size (OR: 2.058; P = 0.000), computed tomography (CT) findings (OR: 1.969; P = 0.001), grade (OR: 0.479; P = 0.000), hemoglobin (Hb) (OR: 1.211; P = 0.005), CEA (OR: 1.111; P = 0.017), and CA19-9 (OR: 1.040; P = 0.033) were independent risk factors for LNM in GC. Tumor size did rank first in the ranking of important factors for LNM in GC and was the first-level segmentation of the two initial branches of the classification tree. The accuracy, sensitivity, specificity, and positive predictive value of the decision rules in diagnosing preoperative LNM in GC were 75.6, 85.7, 73.9, 73.5, and 79.3%, respectively. The accuracy, sensitivity, and specificity of the risk assessment model in predicting preoperative LNM in GC were 79.3, 80.3, and 79.4%, respectively. Conclusion: Tumor size was the most important factor for evaluating LNM in GC. This decision rules and nomogram model constructed to take into account tumor size, CT findings, grade, hemoglobin, CEA, and CA19-9 effectively predicted the incidence of LNM in preoperative GC.

Prognostic factors in stage I gastric cancer: A retrospective analysis

Purpose

The purpose of this research is to investigate the prognostic factors of patients with stage I gastric cancer (GC) and to determine whether adjuvant chemotherapy improves the prognosis for high-risk patients.

Methods

We performed a retrospective analysis at Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, and HwaMei Hospital, University of Chinese Academy of Sciences from January 2001 to December 2015. Cox regression and Kaplan-Meier were used to evaluate the relationship between the patients’ clinicopathologic characteristics and prognosis.

Results

A total of 1,550 patients were eligible for the study. The 5-year disease-free survival (DFS) rate of all enrolled patients was 96.5%. The pT and pN stages were significantly associated with the prognosis. The 5-year DFS rates of the three subgroups (T1N0, T2N0, and T1N1) were 97.8%, 95.7%, and 90.5%, respectively (p < 0.001). In the T1N1 subgroup, patients not undergoing chemotherapy showed a lower 5-year DFS rate compared to those undergoing chemotherapy, although the difference was not statistically significant.

Conclusions

Both the pT and pN stages were closely associated with the prognosis of patients with stage I GC. We also found that the danger coefficient of the pN stage was higher than that of the pT stage, and that postoperative adjuvant chemotherapy might be a reasonable approach to improve outcomes of high-risk patients, particularly in the T1N1 group.

Nomogram using F-18 fluorodeoxyglucose positron emission tomography/computed tomography for preoperative prediction of lymph node metastasis in gastric cancer

BACKGROUND

Lymph node (LN) metastasis is an important prognostic factor in patients with gastric cancer (GC). However, the evaluation of LN metastasis status in the preoperative setting is not accurate. Therefore, precise preoperative prediction of LN metastasis status is crucial for optimal treatment in patients with GC.

AIM

To develop a preoperative nomogram for LN metastasis using F-18 fluorodeoxyglucose (F-18 FDG) positron emission tomography/computed tomography (PET/CT) and preoperative laboratory test findings in GC.

METHODS

In this study, the data of 566 GC patients who underwent preoperative F-18 FDG PET/CT and subsequent surgical resection were analyzed. The LN metastasis prediction model was developed in the training cohort and validated in the internal validation cohort. Routine preoperative laboratory tests, including albumin and carbohydrate antigen (CA) 19-9 were performed in all patients. Univariate and multivariable logistic regression was performed to validate the preoperative predictive indicators for LN metastasis.

RESULTS

Of the 566 patients, 232 (41%) had confirmed histopathologic LN metastasis. Univariate logistic regression revealed that the tumor location, blood hemoglobin, serum albumin levels, neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, CA 19-9, maximum standardized uptake value (SUVmax) of the primary tumor (T_SUVmax), and SUVmax of LN (N_SUVmax) were significantly associated with LN metastasis. In multivariate analysis, T_SUVmax (OR = 1.08; 95%CI: 1.02–1.15; P = 0.011) and N_SUVmax (OR = 1.49; 95%CI: 1.19–1.97; P = 0.002) were found to be significant predictive factors for LN metastasis. The LN metastasis prediction model using T_SUVmax, N_SUVmax, serum albumin, and CA 19-9 yielded an area under the curve (AUC) of 0.733 (95%CI: 0.683–0.784, P = 0.025) in the training cohort and AUC of 0.756 (95%CI: 0.678–0.833, P < 0.001) in the test cohort.

CONCLUSION

T_SUVmax and N_SUVmax measured by preoperative F-18 FDG PET/CT are independent predictive factors for LN metastasis in GC.

Development and validation of a nomogram for preoperative prediction of lymph node metastasis in early gastric cancer

Background

The status of lymph nodes in early gastric cancer is critical to make further clinical treatment decision, but the prediction of lymph node metastasis remains difficult before operation. This study aimed to develop a nomogram that contained preoperative factors to predict lymph node metastasis in early gastric cancer patients.

Methods

This study analyzed the clinicopathologic features of 823 early gastric cancer patients who underwent gastrectomy retrospectively, among which 596 patients were recruited in the training cohort and 227 patients in the independent validation cohort. Significant risk factors in univariate analysis were further identified to be independent variables in multivariable logistic regression analysis, which were then incorporated in and presented with a nomogram. And internal and external validation curves were plotted to evaluate the discrimination of the nomogram.

Results

Totally, six independent predictors, including the tumor size, macroscopic features, histology differentiation, P53, carbohydrate antigen 19-9, and computed tomography-reported lymph node status, were enrolled in the nomogram. Both the internal validation in the training cohort and the external validation in the validation cohort showed the nomogram had good discriminations, with a C-index of 0.82 (95%CI, 0.78 to 0.86) and 0.77 (95%CI, 0.60 to 0.94) respectively.

Conclusions

Our study developed a new nomogram which contained the most common and significant preoperative risk factors for lymph node metastasis in patients with early gastric cancer. The nomogram can identify early gastric cancer patients with the high probability of lymph node metastasis and help clinicians make more appropriate decisions in clinical practice.

Development and validation of a prognostic nomogram for predicting early recurrence after curative resection of stage II/III gastric cancer

Background

The biological behavior of early recurrence is more invasive and the prognosis is worse in gastric cancer (GC). The risk of early recurrence (ER) for GC in stage II/III has not been reported of which the majority of GC patients are in China. Therefore, it is necessary to analyze the ER of gastric cancer in stage II/III.

Methods

The medical records of 1511 consecutive stage II/III GC patients who received resections were retrospectively reviewed. They were randomly classified into either a development or validation group at a ratio of 7:3. The nomogram was constructed based on prognostic factors using logistic regression analysis and was validated by bootstrap resampling and validation dataset, respectively. Concordance index (C-index) values and calibration curves were used to evaluate the predictive accuracy and discriminatory capability.

Results

Three hundred eleven patients experienced ER, accounting for 20.58% of the GC patients investigated. Multivariate logistic regression analysis identified tumors located at upper, middle third, or mixed, a positive lymph node ratio ≥ 0.335, pTNM stage III, lymphocyte count < 1.5 × 10⁹/L, postoperative infection complications and adjuvant chemotherapy < 6 cycles were all independent predictors for ER after curative resection of stage II/III GC. The C-index value obtained for the model was 0.780 (95% CI, 0.747–0.813), and the calibration curves of validation group yielded a C-index value of 0.739 (95% CI, 0.684–0.794), suggesting the practicability of the model.

Conclusions

The nomogram which was developed for predicting ER of stage II/III GC after surgery had good accuracy and was verified through both internal and external validation. The nomogram established can assist clinicians in determining the optimal therapy strategies in counseling, adjuvant treatments, and subsequent follow-up planning.

Risk factors of lymph node metastasis or lymphovascular invasion for early gastric cancer: a practical and effective predictive model based on international multicenter data

Background

Most lymph node metastasis (LNM) models for early gastric cancer (EGC) include lymphovascular invasion (LVI) as a predictor. However, LVI must be confirmed by postoperative pathology. In this study, we aimed to develop a model for predicting the risk of LNM/LVI in EGC using preoperative factors.

Methods

EGC patients who underwent radical gastrectomy at Fujian Medical University Union Hospital and Sun Yat-sen University Cancer Center (n = 1460) were selected as the training set. The risk factors of LNM/LVI were investigated. Data from the International study group on Minimally Invasive surgery for GASTRIc Cancer trial (n = 172) were selected as the validation set.

Results

In the training set, the incidence of LNM/LVI was 21.6%. The 5-year cancer-specific survival rates of patients with and without LNM/LVI were 92.4 and 95.0%, respectively, with significant difference (P = 0.030). Multivariable logistic regression analysis showed that the four independent risk factors for LNM/LVI were female, tumor larger than 20 mm, submucosal invasion and undifferentiated tumor histological type (all P <  0.05); the area under the curve (AUC) was 0.694 (95% confidence interval [CI]: 0.659–0.730). Patients were divided into low-risk, intermediate-risk, high-risk and extremely high-risk groups by recursive partitioning analysis; the incidences of LNM/LVI were 5.4, 12.6, 24.2 and 37.8%, respectively (P <  0.001). The AUC of the validation set was 0.796 (95%CI, 0.662–0.851) and the predictive performance of the LNM/LVI risk in the validation set was consistent with that in the training set.

Conclusions

The risk of LNM/LVI in differentiated mucosal EGC is low, which indicated that endoscopic resection is a treatment option. The risk of LNM/LVI in undifferentiated mucosal EGC and submucosa EGC are high and gastrectomy with lymph node dissection is suggested.

Oncologic Safety of Endoscopic Resection Based on Lymph Node Metastasis in Ulcerative Early Gastric Cancer

Background: There is little evidence regarding appropriate therapeutic modalities for ulcerative-type early gastric cancer (EGC) because the risks and implications of lymph node metastasis are unclear. The indication for endoscopic submucosal dissection (ESD) was investigated for ulcerative-type EGC. Methods: We retrospectively analyzed 192 patients with differentiated ulcerative-type EGC who underwent radical gastrectomy with D2 lymph node dissection. Lymph node metastasis (LNM) risk factors were evaluated using multivariate logistic regression. Results: The LNM rate was 15.1% overall, 0% for mucosa-confined lesions, and 28.2% for submucosa-infiltrating lesions. On multivariate analysis, only lymphovascular invasion (P < .001) was significantly associated with LNM. Among patients with minute submucosal invasion and no lymphovascular invasion, LNM was only observed for tumor sizes ≥2.1 cm. Conclusions: Because LNM risks are negligible, curative ESD could be considered in patients with ulcerative EGC that is confined to the mucosa and histologically differentiated, irrespective of tumor size. In addition, ESD can be attempted for ulcerative EGC with minute submucosal invasion and tumor size <2.1 cm.

Incidence, risk factors, and a predictive model for lymph node metastasis of submucosal (T1) colon cancer: A population-based study

OBJECTIVE

This study aimed to assess the incidence, identify independent factors, and develop a lymph node metastasis (LNM) prediction model for patients with T1 colon cancer.

METHODS

Statistics were drawn from the Surveillance, Epidemiology, and End Results database between 2004 and 2014. A multivariate logistic regression analysis was performed to determine independent predictors of LNM. A nomogram for predicting the possibility of LNM was developed based on those factors.

RESULTS

A total of 5397 patients with T1 colon cancer were identified. The overall LNM rate was 15.0% (808/5397). A multivariate analysis showed that age (odds ratio [OR] 0.97, P < 0.001), tumor size (OR 1.01, P < 0.001), moderate (OR 1.77, P = 0.001) or poorly differentiated/undifferentiated tumor (OR 5.60, P < 0.001), right colon cancer (OR 1.39, P = 0.008), and a positive carcinoembryonic antigen level (OR 1.51, P = 0.004) were independent predictive factors for LNM. The area under the receiver operating characteristic curve was 0.68 (95% confidence interval [CI] 0.65-0.71) in the training set and 0.65 (95% CI 0.61-0.67) in the validation set. A calibration plot showed good consistency between the bias-corrected prediction and the ideal reference line with 1000 additional bootstraps (mean absolute error = 0.007).

CONCLUSIONS

The incidence of LNM was high in patients with T1 colon cancer. A nomogram for predicting the probability of LNM for T1 colon cancer may be used to help determine the optimal treatment for these patients.

A Comparison by Meta-Analysis of Papillary Early Gastric Carcinoma to Its Tubular Counterpart for the Risk of Lymph Node Metastasis and Submucosal Invasion

BACKGROUND AND AIM

At present, the decision to perform endoscopic resection for treating either papillary early gastric cancer (EGC) or tubular EGC is made according to identical criteria. However, there is controversy in the literature whether the risk of lymph node metastasis (LNM) and submucosal invasion for both disease modalities is equal, and this prompts investigation to clarify this issue.

METHODS

The PubMed and Web of Science databases were searched for relevant studies published up to January 2017. Data were extracted, and the pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using a random-effects or a fixed-effects model, according to heterogeneity.

RESULTS

A total of 13 studies were included in this analysis. Papillary EGC had a significantly higher LNM risk (OR, 1.97; 95% CI, 1.38-2.82) and submucosal invasion risk (OR, 1.44; 95% CI, 1.08-1.93), compared with tubular EGC. Stratified by geographic location, a significantly increased risk of LNM (OR, 2.28; 95% CI, 1.57-3.30) and submucosal invasion (OR, 1.52; 95% CI, 1.13-2.04) associated with papillary EGC was found in Asian studies. In addition, papillary EGC exhibited significantly more frequent elevated/flat growth patterns (OR, 7.54, 95% CI, 4.76-11.96).

CONCLUSIONS

Our study identifies an increased risk for submucosal invasion and LNM in papillary EGC compared with tubular EGC, indicating that papillary EGC requires more careful clinical management compared with tubular EGC.

Analysis of prognostic factors in patients with early gastric cancer based on SEER database

AIM

To identify the risk factors affecting the survival of patients with early gastric cancer (EGC).

METHODS

The patients who were diagnosed with T1 gastric cancer after operation were selected from the SEER database. The COX proportional hazards model, chi square test, and logistic regression were used to analyze the patients' data.

RESULTS

COX proportional risk model analysis showed that age, race, sex, tumor size, pathological type, degree of differentiation, and lymph node metastasis were independent prognostic factors for overall survival. Tumor size was not an independent risk factor for EGC in patients younger than 60 years of age. With regard to gender, race was an independent risk factor for male patients, but there was no difference among females. Tumor size and pathological type were risk factors for prognosis in males but not in females. The independent risk factors affecting lymph node metastasis were tumor size, pathological type, and degree of differentiation.

CONCLUSION

Age and gender are independent factors affecting the prognosis of EGC patients. There are also differences in risk factors affecting prognosis among different age groups and different gender groups. Understanding the risk factors for patients with different clinical characteristics can provide evidence-based medicine support for accurate treatment of EGC patients.

The clinical significance of platelet-derived growth factors (PDGFs) and their receptors (PDGFRs) in gastric cancer: A systematic review and meta-analysis

BACKGROUND

The overexpression and mutation of platelet-derived growth factors (PDGFs) and their receptors (PDGFRs) are widespread in cancers and have been recognized as attractive oncologic targets with diverse therapeutic targets. Reports of the overexpression of genes, proteins and mutations of PDGFs/PDGFRs in gastric cancer and their associations with clinicopathological features, Western and Asian patients, as well as prognostic role have shown variable outcomes. This study sought to employ meta-analysis to evaluate PDGFs/PDGFRs status prognostic significance and their association with clinicopathological features of gastric cancer.

METHOD

A comprehensive search of PubMed database for studies that investigated the overexpression of mRNA/Protein and mutation of PDGFs/PDGFRs in gastric cancer of Western and Asian patients, their prognostic significance and association with clinicopathological characteristics in May, 2017 or earlier was carried out by two reviewers independently. Pooled odd ratios and hazard ratios at 95% confidence intervals were estimated and summarized using fixed-effect and random-effect Mantel-Haenszel models and Inverse Variance models in Review Manager software version 5.3.

RESULTS

Fourteen studies with 16 datasets of 1178 patients were included in meta-analysis. Fourteen studies of 1178 patients with 1446 cases and 7 studies of 1076 patients with 1280 cases were included in meta-analysis of clinicopathological and prognostic significance of high or positive PDGF/PDGFR status respectively. Odd ratio at 95% confidence intervals for different groups of analysis are as follows: males versus females(OR = 1.38, 95% CI: 1.04-1.83, P_OR = 0.03); ≥T2 stage versus T1 stage(OR = 2.06, 95% CI: 1.22-3.49, P_OR = 0.007); nodal metastasis versus no nodal metastasis(OR = 2.78, 95% CI: 1.48-5.22, P_OR = 0.002); TNM stage ≥II versus TNM stage I(OR = 3.55, 95% CI: 1.89-6.69, P_OR<0.0001). Subgroup analysis of the association of PDGF/PDGFR among Western patients(OR = 0.24 95% CI: 0.10-0.58, P_OR = 0.002) and association of PDGFs/PDGFRs gene mutation among gastric cancer patients(OR = 0.15, 95% CI: 0.05-0.45, P_OR = 0.0008) were significant. The association of PDGFs/PDGFRs in young and middle age versus elderly aged, undifferentiated versus well differentiated tumors, large tumor size group(>6 cm) versus small tumor size group(≤6 cm) were insignificant. Subgroup analysis of the association of PDGFs/PDGFRs among Western Asian patients; PDGF/PDGFR mRNA expression and protein expression among gastric cancer patients were insignificant. In addition, PDGF/PDGFR status among gastric cancer patients was insignificant in overall effect analysis PDGF/PDGFR status has shown to predict reduced overall survival(HR = 1.25, 95% CI: 0.49-3.22, P_HR = 0.64) and relapse free survival(HR = 0.93, 95% CI: 0.36-2.41, P_HR = 0.88) insignificantly. Also, overall prognostic effect analysis(HR = 1.07, 95% CI: 0.58-1.96, P_HR = 0.84) was insignificant.

CONCLUSION

PDGFs/PDGFRs status amongst gastric cancer patients plays a key role in clinical variables and nodal metastasis. These insights might be helpful in providing guidelines for diagnosis, molecular target therapy, and prognosis of gastric cancer.

The overexpression of MDM4: an effective and novel predictor of gastric adenocarcinoma lymph node metastasis

Background

MDM4 is the important negative regulator of the tumor suppressor protein p53, which is overexpressed in various human cancers. This study evaluates the MDM4 expression in patients with gastric adenocarcinoma (GTAC) at the mRNA and protein levels and examines relationships among MDM4 expression, clinicopathological features, and prognosis.

Results

The qRT-PCR and the Western blot analysis showed that the MDM4 expression level was high in GTACN⁺ but not in GTACN⁻. The high expression level of MDM4 was significantly associated with age (P = 0.047), lymph node metastasis (LNM) (P < 0.001), pathological stage (P < 0.001), differentiation status (P = 0.001), and preoperative serum CA19-9 level (P < 0.001). Moreover, the survival analysis showed that Borrmann type, depth of invasion, LNM, and preoperative serum CA19-9 level were independent prognostic factors. The univariate analysis revealed that MDM4 expression influenced GTAC prognosis. Furthermore, the influence of overall prognosis relies on whether or not the high MDM4 expression level could lead to LNM.

Materials and Methods

We investigated MDM4 expression in primary GTAC and paired normal gastric tissues (30 pairs) through qRT-PCR and Western blot analyses. We also performed immunohistochemistry analysis on 336 paraffin-embedded GTAC specimens and 33 matched normal specimens.

Conclusions

MDM4 expression may result in LMN of GTAC. High MDM4 expression levels are associated with LMN of GTAC and influence the prognosis of patients with GTAC.

A nomogram to predict prognosis for gastric cancer with peritoneal dissemination

Objective

To identify independent prognostic factors to be included in a nomogram to predict the prognosis of gastric cancer patients with peritoneal dissemination.

Methods

This is a retrospective study on 684 patients with a histological diagnosis of gastric cancer with peritoneal dissemination from the Sun Yat-sen University Cancer Center as the development set, and 62 gastric cancer patients from the Sixth Affiliated Hospital of Sun Yat-sen University as the validation group. Chi-square test and Cox regression analysis were used to compare the clinicopathological variables and the prognosis of gastric cancer patients with peritoneal dissemination. The Harrell’s concordance index (C-index) and calibration curve were determined for comparisons of predictive ability of the nomogram.

Results

Univariate and multivariate analyses showed that serum carcinoembryonic antigen (CEA) level (P=0.032), ascites grading (P=0.008), presence of extraperitoneal metastasis (P<0.001), seeding status (P=0.016) and performance status (P=0.009) were independent prognostic factors for gastric cancer patients with peritoneal dissemination in the development set. The nomogram model was constructed using these five factors. Internal validation showed that the C-index of the model was 0.641. For the external validation, the C-index of this model was 0.709.

Conclusions

We developed and validated a nomogram to predict the prognosis for gastric cancer patients with peritoneal dissemination. This nomogram may play an important clinical role in guiding palliative therapy for these types of patients, although it may need more data for optimization.

Risk factors for lymph node metastasis in early gastric cancer patients: Report from Eastern Europe country- Lithuania

BACKGROUND

Current risk factors for lymph node metastasis in early gastric cancer have been primarily determined in Asian countries; however their applicability to Western nations is under discussion. The aim of our study was to identify risk factors associated with lymph node metastasis in Western cohort patients from the Eastern European country - Lithuania.

METHODS

A total of 218 patients who underwent open gastrectomy for early gastric cancer were included in this retrospective study. After histolopathological examination, risk factors for lymph node metastasis were evaluated. Overall survival was evaluated and factors associated with long-term outcomes were analyzed.

RESULTS

Lymph node metastases were present in 19.7% of early gastric cancer cases. The rates were 5/99 (4.95%) for pT1a tumors and 38/119 (31.9%) for pT1b tumors. Submucosal tumor invasion, lymphovascular invasion, and high grade tumor differentiation were identified as independent risk factors for lymph node metastasis. Submucosal tumor invasion and lymphovascular invasion were also associated with worse 5-year survival results.

CONCLUSION

Our study established submucosal tumor invasion, lymphovascular invasion, and high grade tumor differentiation as risk factors for lymph node metastasis.

Snail and Axin2 expression predict the malignant transformation of oral leukoplakia

OBJECTIVES

Oral leukoplakia (OL) has a well-documented potential risk of malignant transformation into oral squamous cell carcinoma (OSCC), although biomarker(s) predicting malignant potential are limited in capability. The aim of this cross-sectional and retrospective cohort study was to investigate the predictive role of canonical Wnt genes Axin2 and Snail (SNAI1) expression in the malignant transformation of OL lesions.

MATERIALS AND METHODS

The expression of epithelial-mesenchymal transition (EMT) genes Snail and Axin2, which are regulated by the canonical Wnt pathway, were determined using immunohistochemical staining in an OL cohort consisting of 154 samples of patients with long-term follow-up and then evaluated as risk factors for malignant transformation of OL.

RESULTS

Increased Axin2 and Snail abundance were found in 107 (69.5%) and 58 (37.7%) of OL patients, respectively. In a multivariate analysis using gender, age, lesion site, Axin2, and Snail as cofactors, both Axin2 and Snail were independent risk factors for malignant transformation with a hazard ratio of 7.47 (95% confidence interval, 2.23-25.02; P=0.001) and 4.41 (95% confidence interval, 1.78-10.93; P=0.001), respectively. A nomogram for predicting 5-, 10-, and 15-year cancer-free survival probability was developed in patients with OL by including gender, age, lesion site, Axin2, and Snail expression with ac-index of 0.760.

CONCLUSION

The increased abundance of Snail and Axin2 is highly correlated to malignant transformation of OL, making them novel biomarker(s) predicting oral cancer development.

A nomogram to predict lymph node metastasis in patients with early gastric cancer

Background

Lymph node status is crucial to determining treatment for early gastric cancer (EGC). We aim to establish a nomogram to predict the possibility of lymph node metastasis (LNM) in EGC patients.

Methods

Medical records of 952 EGC patients with curative resection, from 2002 to 2014, were retrospectively retrieved. Univariate and multivariate analysis were performed to examine risk factors associated with LNM. A nomogram for predicting LNM was established and internally validated.

Results

Five variables significantly associated with LNM were included in our model, these are sex (Odd ratio [OR] = 1.961, 95% confidence index [CI], 1.334 to 2.883; P = 0.001), depth of tumor (OR = 2.875, 95% CI, 1.872 to 4.414; P = 0.000), tumor size (OR = 1.986, 95% CI, 1.265 to 3.118; P = 0.003), histology type (OR = 2.926, 95% CI, 1.854 to 4.617; P = 0.000) and lymphovascular invasion (OR = 4.967, 95% CI, 2.996 to 8.235; P = 0.000). The discrimination of the prediction model was 0.786.

Conclusions

A nomogram for predicting lymph node metastasis in patients with early gastric cancer was successfully established, which was superior to the absolute endoscopic submucosal dissection (ESD) indication in terms of the clinical performance.

Differences in Prevalence of Lymphovascular Invasion among Early Gastric Cancers between Korea and Japan

BACKGROUND/AIMS

The presence of invasion is a diagnostic criterion of early gastric cancer (EGC) in Korea, whereas diagnosis in Japan is based on enlarged nuclei and prominent nucleoli. Moreover, the depth of invasion is the location of cancer cell infiltration in Korea, whereas it is the location of lymphovascular invasion (LVI) or cancer cell infiltration in Japan. We evaluated the characteristics of EGC with LVI to uncover the effects of different diagnostic criteria.

METHODS

Consecutive T1-stage EGC patients who underwent complete resection were included after endoscopic or surgical resection. The presence of LVI was evaluated.

RESULTS

LVI was present in 112 of 1,089 T1-stage EGC patients. LVI was associated with depth of invasion (p<0.001) and age (p=0.017). The prevalence of LVI in mucosal cancer was significantly higher in Korea (p<0.001), whereas that of submucosal cancer was higher in Japan (p=0.024). For mucosal EGC types, LVI was positively correlated with diagnostic criteria applied in Korea (p=0.017). For submucosal EGC types, LVI was positively correlated with Japanese criteria (p=0.001) and old age (p=0.045).

CONCLUSIONS

The higher prevalence of LVI for mucosal EGC in Korea and for submucosal EGC in Japan indicates that different diagnostic criteria should be considered when reading publications from other countries.

A nomogram for predicting the likelihood of lymph node metastasis in early gastric signet ring cell carcinoma: A single center retrospective analysis with external validation

Treatment algorithm has not been established for early gastric cancer with signet ring cell carcinoma (SRC), which has a reported low rate of lymph node metastasis (LNM) similar to differentiated cancer. A cohort of 256 patients with early gastric SRC at our center between January 2002 and December 2015 were retrospectively reviewed. Multivariate logistic regression analysis was used to determine the independent factors of LNM. A nomogram for predicting LNM was constructed and internally validated. Additional external validation was performed using the database from Cancer Institute Ariake Hospital in Tokyo (n = 1273). Clinical performance of the model was assessed by decision analysis of curve. The overall LNM incidence was 12.9% (33/256). The multivariate logistic model identified sex, tumor size, and LVI as covariates associated with LNM. Subsequently, a nomogram consisted of sex, tumor size, and depth of invasion was established. The model showed qualified discrimination ability both in internal validation (area under curve, 0.801; 95% confidence interval [CI], 0.729-0.873) and in external dataset (area under curve, 0.707; 95% CI, 0.657-0.758). Based on the nomogram, treatment algorithm for early gastric SRC was proposed to assist clinicians in making better decisions. We developed a nomogram predicting risk of LNM for early gastric SRC, which should be helpful for patient counseling and surgical decision-making.

Analysis of Predictors for Lymph Node Metastasis in Patients with Superficial Esophageal Carcinoma

In order to predict related risk factors for lymph node metastasis (LNM) in patients with superficial esophageal carcinoma (SEC) and provide reference for endoscopic minimally invasive treatment, we included a total of 93 patients with superficial esophageal carcinoma who have underwent esophagectomy and lymph node dissection from 2010 to 2015. The depth of invasion was remeasured and classified into 6 groups according to their wall penetration. The prediction model was founded based on the independent risk factors. The results shows that lymph node metastasis of m1, m2, m3, sm1, sm2, and sm3 of superficial esophageal carcinoma was 0%, 0%, 5.3%, 8.7%, 17.6%, and 37.5%, respectively. The tumor size, differentiation, and lymphvascular invasion were also significantly related to lymph node metastasis by univariate analysis. Multivariate analysis showed that the depth of invasion and lymphovascular invasion were independent risk factors of lymph node metastasis. A prediction model for lymph node metastasis was established as follows: p = e^x/(1 + e^x), and x = −5.469 + 0.839 × depth of invasion + 1.992 × lymphavascular metastasis. The area under ROC curve was 0.858 (95% CI: 0.757–0.959). It was also shown that the depth of invasion was related to tumor differentiation, macroscopic type, and tumor size.

A nomogram composed of clinicopathologic features and preoperative serum tumor markers to predict lymph node metastasis in early gastric cancer patients

Predicting lymph node metastasis (LNM) accurately is of great importance to formulate optimal treatment strategies preoperatively for patients with early gastric cancer (EGC). This study aimed to explore risk factors that predict the presence of LNM in EGC. A total of 697 patients underwent gastrectomy enrolled in this study, were divided into training and validation set, and the relationship between LNM and other clinicopathologic features, preoperative serum combined tumor markers (CEA, CA19-9, CA125) were evaluated. Risk factors for LNM were identified using logistic regression analysis, and a nomogram was created by R program to predict the possibility of LNM in training set, while receiver operating characteristic (ROC) analysis was applied to assess the predictive value of the nomogram model in validation set. Consequently, LNM was significantly associated with tumor size, macroscopic type, differentiation type, ulcerative findings, lymphovascular invasion, depth of invasion and combined tumor marker. In multivariate logistic regression analysis, factors including of tumor size, differentiation type, ulcerative findings, lymphovascular invasion, depth of invasion and combined tumor marker were demonstrated to be independent risk factors for LNM. Moreover, a predictive nomogram with these independent factors for LNM in EGC patients was constructed, and ROC curve demonstrated a good discrimination ability with the AUC of 0.847 (95% CI: 0.789-0.923), which was significantly larger than those produced in previous studies. Therefore, including of these tumor markers which could be convenient and feasible to obtain from the serum preoperatively, the nomogram could effectively predict the incidence of LNM for EGC patients.