Risk assessment of lymph node metastasis in early gastric cancer: Establishment and validation of a Seven-point scoring model

Nomogram for pre-procedural prediction of lymph node metastasis in patients with submucosal early gastric cancer

BACKGROUND

The treatment of early gastric cancer (EGC) is contingent upon the status of lymph node metastasis (LNM). Accurate preoperative prediction of LNM is critical for reducing unnecessary surgeries. This study seeks to evaluate the risk factors for LNM in submucosal EGC and develop a predictive model to optimize therapeutic decision-making.

METHODS

A retrospective analysis was performed on clinical data from 389 patients with T1b-stage EGC who underwent radical gastrectomy. Univariate and multivariate analyses were conducted to identify independent risk factors, followed by the development of a nomogram to predict LNM. The model's efficacy was validated through receiver operating characteristic curves, calibration curves, and decision curve analysis.

RESULTS

Of the 389 patients, 77 had LNM. Logistic regression analysis identified gender, CA199 levels, tumor location, degree of differentiation, presence of ulcers, and lymph node enlargement on CT as independent risk factors for LNM. A nomogram was constructed to assess the risk of LNM, demonstrating strong predictive accuracy with an area under the curve of 0.82 in the training set and 0.74 in the validation set, along with good sensitivity and positive predictive value.

CONCLUSIONS

This study presents a reliable preoperative nomogram to estimate the likelihood of LNM in submucosal EGC, providing valuable guidance for determining the most effective treatment strategies for patients.

Risk factors for lymph node metastasis and invasion depth in early gastric cancer: Analysis of 210 cases

BACKGROUND

Gastric cancer is the leading cause of cancer-related deaths worldwide. Early gastric cancer (EGC) is often associated with the risk of lymph node metastasis, which influences treatment decisions. Despite the use of enhanced computed tomography, the prediction of lymph node involvement remains challenging.

AIM

To investigate the risk factors for lymph node metastasis and invasion depth in patients with EGC.

METHODS

In total, 210 patients with pathologically diagnosed EGC were included in this study. Univariate and multivariate statistical analyses were used to predict risk factors for lymph node metastasis and invasion depth in patients with EGC.

RESULTS

Among the 210 patients, 27 (12.9%) had lymph node metastases. Of the 117 patients with submucosal gastric cancer, 24 (20.5%) had lymph node metastases. Both univariate and multivariate analyses indicated that the depth of invasion in EGC was a risk factor for lymph node metastasis in these patients. Additionally, pathological type was identified as a risk factor for cancer cell invasion in patients with EGC.

CONCLUSION

EGC invasion depth, not tumor type, size, age, sex, or location, predicts lymph node spread. Tumor type, not size, age, sex, or location, predicts cancer cell invasion.

Establishment and validation of a risk score model based on EUS: assessment of lymph node metastasis in early gastric cancer

BACKGROUND AND AIMS

Lymph node metastasis significantly affects the prognosis of early gastric cancer patients. EUS plays a crucial role in the preoperative assessment of early gastric cancer. This study evaluated the efficacy of EUS in identifying lymph node metastasis in early gastric cancer patients and developed a risk score model to aid in choosing the best treatment options.

METHODS

We retrospectively analyzed the effectiveness of EUS for detecting lymph node metastasis in early gastric cancer patients. A risk score model for predicting lymph node metastasis preoperatively was created using independent risk factors identified through binary logistic regression analysis and subsequently validated. Receiver operating characteristic curves were generated for both the development and validation cohorts.

RESULTS

The overall accuracy of EUS in identifying lymph node metastasis was 85.3%, although its sensitivity (29.2%) and positive predictive value (38.7%) were relatively low. Patients were categorized based on preoperative risk factors for lymph node metastasis, including tumor size of ≥20 mm, lymph nodes of ≥10 mm, body mass index of ≥24 kg/m2, and lymph node metastasis on CT scans. A 7-point risk score model was developed to assess the likelihood of lymph node metastasis. The areas under the receiver operating characteristic curve for the development and validation sets were 0.842 and 0.837, respectively, with sensitivities of 64% and 79%, respectively.

CONCLUSIONS

We developed a practical risk score model based on preoperative factors to help EUS predict lymph node metastasis in early gastric cancer patients, guiding the selection of optimal treatment approaches for these patients.

Construction and validation of a nomogram model for lymph node metastasis of stage II-III gastric cancer based on machine learning algorithms

Background

Gastric cancer, a pervasive malignancy globally, often presents with regional lymph node metastasis (LNM), profoundly impacting prognosis and treatment options. Existing clinical methods for determining the presence of LNM are not precise enough, necessitating the development of an accurate risk prediction model.

Objective

Our primary objective was to employ machine learning algorithms to identify risk factors for LNM and establish a precise prediction model for stage II-III gastric cancer.

Methods

A study was conducted at Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine between May 2010 and December 2022. This retrospective study analyzed 1147 surgeries for gastric cancer and explored the clinicopathological differences between LNM and non-LNM cohorts. Utilizing univariate logistic regression and two machine learning methodologies-Least absolute shrinkage and selection operator (LASSO) and random forest (RF)-we identified vascular invasion, maximum tumor diameter, percentage of monocytes, hematocrit (HCT), and lymphocyte-monocyte ratio (LMR) as salient factors and consolidated them into a nomogram model. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves were used to evaluate the test efficacy of the nomogram. Shapley Additive Explanation (SHAP) values were utilized to illustrate the predictive impact of each feature on the model's output.

Results

Significant differences in tumor characteristics were discerned between LNM and non-LNM cohorts through appropriate statistical methods. A nomogram, incorporating vascular invasion, maximum tumor diameter, percentage of monocytes, HCT, and LMR, was developed and exhibited satisfactory predictive capabilities with an AUC of 0.787 (95% CI: 0.749-0.824) in the training set and 0.753 (95% CI: 0.694-0.812) in the validation set. Calibration curves and decision curves affirmed the nomogram's predictive accuracy.

Conclusion

In conclusion, leveraging machine learning algorithms, we devised a nomogram for precise LNM risk prognostication in stage II-III gastric cancer, offering a valuable tool for tailored risk assessment in clinical decision-making.

A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria

BACKGROUND

We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance with that of the most common clinical risk scoring system, the eCura system.

METHODS

We used data from 4,042 consecutive patients with EGC from 21 institutions who underwent endoscopic submucosal dissection (ESD) and/or surgery between 2010 and 2021. All resected EGCs were histologically confirmed not to satisfy the current Japanese endoscopic curability criteria. Of all patients, 3,506 constituted the training cohort to develop the neural network-based ML model, and 536 constituted the validation cohort. The performance of our ML model, as measured by the area under the receiver operating characteristic curve (AUC), was compared with that of the eCura system in the validation cohort.

RESULTS

LNM rates were 14% (503/3,506) and 7% (39/536) in the training and validation cohorts, respectively. The ML model identified patients with LNM with an AUC of 0.83 (95% confidence interval, 0.76-0.89) in the validation cohort, while the eCura system identified patients with LNM with an AUC of 0.77 (95% confidence interval, 0.70-0.85) (P = 0.006, DeLong's test).

CONCLUSIONS

Our ML model performed better than the eCura system for predicting LNM risk in patients with EGC who did not meet the existing Japanese endoscopic curability criteria. We developed a neural network-based machine learning model that predicts the risk of lymph node metastasis in patients with early gastric cancer who did not meet the endoscopic curability criteria.

Clinicopathologic factors correlated with lymph node metastasis in gastric cancer: a retrospective cohort study involving 5606 patients

BACKGROUND

The identification of risk factors associated with lymph node metastasis (LNM) in gastric cancer will establish a crucial foundation for the implementation of endoscopic operation and multidisciplinary treatment programs.

METHODS

A total of 5606 patients with gastric cancer with comprehensive clinicopathologic data were enrolled through systematic searching and rigorous screening. Of the 5606 patients, 1438 were diagnosed with early gastric cancer (EGC), which would be used for further analysis. Subsequently, univariate and multivariate logistic regression analyses were performed to identify the risk factors.

RESULTS

The rates of LNM in T1a, T1b, T2, T3, T4a, and T4b stage gastric cancer were 7.0%, 19.4%, 48.4%, 77.1%, 83.8%, and 89.6%, respectively. Female (odds ratio [OR], 1.559; P = .032), lower tumor location (OR, 1.773; P = .023), tumor size of >2 cm (OR, 2.007; P < .001), mixed (OR, 2.371; P = .001) and undifferentiated histologic types (OR, 2.952; P < .001), T1b stage (OR, 2.041; P < .001), presence of ulceration (OR, 1.758; P = .027), and lymphovascular invasion (OR, 5.722; P < .001) were identified as independent risk factors for LNM in EGC. A nomogram was constructed using appropriate predictors to preoperatively predict the risk of LNM in patients with EGC.

CONCLUSION

This study identified the clinicopathologic factors associated with LNM in patients with EGC and developed a prediction model, thereby facilitating the integration of diverse treatment modalities in managing patients with EGC.

Analysis of lymph node metastasis and survival prognosis in early gastric cancer patients: A retrospective study

BACKGROUND

Early gastric cancer (EGC) is a common malignant tumor of the digestive system, and its lymph node metastasis and survival prognosis have been concerning. By retrospectively analyzing the clinical data of EGC patients, we can better understand the status of lymph node metastasis and its impact on survival and prognosis.

AIM

To evaluate the prognosis of EGC patients and the factors that affect lymph node metastasis.

METHODS

The clinicopathological data of 1011 patients with EGC admitted to our hospital between January 2015 and December 2023 were collected in a retrospective cohort study. There were 561 males and 450 females. The mean age was 58 ± 11 years. The patient underwent radical gastrectomy. The status of lymph node metastasis in each group was determined according to the pathological examination results of surgical specimens. The outcomes were as follows: (1) Lymph node metastasis in EGC patients; (2) Analysis of influencing factors of lymph node metastasis in EGC; and (3) Analysis of prognostic factors in patients with EGC. Normally distributed measurement data are expressed as mean ± SD, and a t test was used for comparisons between groups. The data are expressed as absolute numbers or percentages, and the chi-square test was used for comparisons between groups. Rank data were compared using a nonparametric rank sum test. A log-rank test and a logistic regression model were used for univariate analysis. A logistic stepwise regression model and a Cox stepwise regression model were used for multivariate analysis. The Kaplan-Meier method was used to calculate the survival rate and construct survival curves. A log-rank test was used for survival analysis.

RESULTS

Analysis of influencing factors of lymph node metastasis in EGC. The results of the multifactor analysis showed that tumor length and diameter, tumor site, tumor invasion depth, vascular thrombus, and tumor differentiation degree were independent influencing factors for lymph node metastasis in patients with EGC (odds ratios = 1.80, 1.49, 2.65, 5.76, and 0.60; 95%CI: 1.29-2.50, 1.11-2.00, 1.81-3.88, 3.87-8.59, and 0.48-0.76, respectively; P < 0.05). Analysis of prognostic factors in patients with EGC. All 1011 patients with EGC were followed up for 43 (0-13) months. The 3-year overall survival rate was 97.32%. Multivariate analysis revealed that age > 60 years and lymph node metastasis were independent risk factors for prognosis in patients with EGC (hazard ratio = 9.50, 2.20; 95%CI: 3.31-27.29, 1.00-4.87; P < 0.05). Further analysis revealed that the 3-year overall survival rates of gastric cancer patients aged > 60 years and ≤ 60 years were 99.37% and 94.66%, respectively, and the difference was statistically significant (P < 0.05). The 3-year overall survival rates of patients with and without lymph node metastasis were 95.42% and 97.92%, respectively, and the difference was statistically significant (P < 0.05).

CONCLUSION

The lymph node metastasis rate of EGC patients was 23.64%. Tumor length, tumor site, tumor infiltration depth, vascular cancer thrombin, and tumor differentiation degree were found to be independent factors affecting lymph node metastasis in EGC patients. Age > 60 years and lymph node metastasis are independent risk factors for EGC prognosis.

Risk Factors for Lymph Node Metastasis in a Western Series of Patients with Distal Early Gastric Cancer

Background: Assessment of potential lymph node metastasis is mandatory in the appropriate treatment of early gastric cancers. This study analysed factors associated with lymph node metastasis to identify differences between node-negative and node-positive patients and between T1a and T1b cancers. Methods: The clinicopathological features of 129 early gastric cancer patients who had undergone radical gastrectomy were analysed to identify predictive factors for lymph node metastasis. Results: Lymph node metastasis was detected in 76 (59.0%) patients. Node-positive patients were younger (58.1 ± 11.3 years) than those without metastasis (61.9 ± 9.6 years, p = 0.02). Greater tumour sizes were observed in patients with lymph node metastasis (3.6 ± 1.0 cm) compared to node-negative patients (1.9 ± 0.5 cm, p = 0.00001). Depressed form, ulceration, diffuse histological type, and undifferentiated lesions were more frequent in node-positive patients than in the node-negative group. Tumour size > 3.0 cm showed a correlation with lymph node metastasis in both T1a (p = 0.0001) and T1b (p = 0.006) cancer. The male sex (p = 0.006) had a significant correlation with lymph node metastasis in T1a cancer. Depressed appearance (p = 0.02), ulceration (p = 0.03), differentiation (p = 0.0001), diffuse type (p = 0.0002), and lower third location (p = 0.005) were associated with lymph node metastasis in T1b cancer. Conclusions: Tumour size > 3 cm, undifferentiated lesions, ulceration, diffuse type, lower third location, and submucosal invasion are risk factors for lymph node metastasis in early gastric cancer.

Construction and validation of a risk-scoring model to predict lymph node metastasis in T1b-T2 esophageal cancer

BACKGROUND

Lymph node status is an important factor in determining preoperative treatment strategies for stage T1b-T2 esophageal cancer (EC). Thus, the aim of this study was to investigate the risk factors for lymph node metastasis (LNM) in T1b-T2 EC and to establish and validate a risk-scoring model to guide the selection of optimal treatment options.

METHODS

Patients who underwent upfront surgery for pT1b-T2 EC between January 2016 and December 2022 were analyzed. On the basis of the independent risk factors determined by multivariate logistic regression analysis, a risk-scoring model for the prediction of LNM was constructed and then validated. The area under the receiver operating characteristic curve (AUC) was used to assess the discriminant ability of the model.

RESULTS

The incidence of LNM was 33.5% (214/638) in our cohort, 33.4% (169/506) in the primary cohort and 34.1% (45/132) in the validation cohort. Multivariate analysis confirmed that primary site, tumor grade, tumor size, depth, and lymphovascular invasion were independent risk factors for LNM (all P < 0.05), and patients were grouped based on these factors. A 7-point risk-scoring model based on these variables had good predictive accuracy in both the primary cohort (AUC, 0.749; 95% confidence interval 0.709-0.786) and the validation cohort (AUC, 0.738; 95% confidence interval 0.655-0.811).

CONCLUSION

A novel risk-scoring model for lymph node metastasis was established to guide the optimal treatment of patients with T1b-T2 EC.

Risk factors associated with lymph node metastasis in early-stage distal gastric cancer

OBJECTS

This study was designed to explore the risk factors of lymph node metastasis (LNM) in distal gastric cancer with early stage, and to provide reference for the choice of treatment protocols.

METHODS

In this retrospective observational study, 824 early distal gastric cancer (EDGC) cases who treated at our unit from 2010 to 2020 were selected as research objects. Subsequently, univariate and multivariate logistic regression analyses were conducted to investigate the associations between LNM and clinicopathological features.

RESULTS

Of these 824 EDGC cases, 140 (17.0%) developed LNM, including 72 N1 stage and 68 N2-3 stage LNM. Multivariate logistic regression analysis identified the tumor diameter ≥1.75 cm (odds ratio (OR) = 2.361, p < 0.001), tumor location (OR = 1.552, p = 0.046), histological classification (p = 0.004), tumor infiltration depth (OR = 2.154, p = 0.001), and vascular infiltration (OR = 4.354, p < 0.001) as independent predictors for LNM. Logistic regression analyses based on 756 N0-1 LNM cases identified the smoking history (OR = 0.507, p = 0.043), tumor diameter ≥1.75 cm (OR = 2.265, p = 0.010), tumor location (OR = 1.834, p = 0.036), histological classification (p = 0.018), tumor infiltration depth (OR = 1.939, p = 0.034), and vascular infiltration (OR = 3.225, p < 0.001) as independent predictors for N1 LNM. Moreover, preoperative hypoalbuminemia (OR = 7.087, p = 0.015), significant preoperative weight loss (OR = 2.724, p = 0.023), tumor diameter ≥1.75 cm (OR = 5.484, p = 0.001), multiple tumors (OR = 9.986, p = 0.038), histological classification (p = 0.029), and vascular infiltration (OR = 33.704, p < 0.001) were proved to be associated with LNM for T1a tumors.

CONCLUSIONS

The tumor diameter, location and infiltration depth, histological classification, and vascular infiltration were expected to be used as predictors of LNM in EDGC, and preoperative hypoalbuminemia, significant weight loss, tumor diameter and number, histological classification, and vascular infiltration were associated with LNM for T1a tumors.

Evaluation of the widths of the mucosal strips in pathological examination of specimens of endoscopic submucosal dissection for early gastric cancer

BACKGROUND

Endoscopic submucosal dissection (ESD) is the standard treatment for early gastric cancer in Japan. Pathological evaluation of ESD specimens is considered essential to determine if additional gastrectomy is necessary. Usually, specimens resected by ESD are sliced into 2-3 mm wide sections, and each section is examined for depth of tumor and lymphovascular invasion. Nevertheless, in most cases of additional gastrectomy, lymph node metastasis is not present. Given that there are few-studies on how clinical-decisions based on the pathologic-evaluation-method, in particular the specimen cut-width, influence patient outcomes, we retrospectively evaluated whether reducing the number of cuts to one-half or one-third would result in underestimation of the real need for additional surgery. The effect of the actual cut-width on recommended treatment (referral to operation) and patient-outcomes was also assessed.

METHODS

Pathological records of 498 lesions from 439 patients were reviewed and re-evaluated. All pathological descriptions are based on the gastric cancer classification system of the Japanese Gastric Cancer Association, 15th edition.

RESULTS

In 5.8% and 8.5% of the total specimens, underdiagnosis of tumor-depth and lymphovascular invasion occurred when the number of sections was reduced to one-half and one-third, respectively. Significantly more submucosal invasions were found in the group in which the cut-with was between 3 and 4 mm than in the group in which the cut width was less than 3 mm.

CONCLUSION

Evaluation of the appropriate cut-width is important and should be discussed from the standpoint of labor costs and lost opportunities to search for molecular markers in ESD materials.

Polymorphism rs1057147 located in mesothelin gene predicts lymph node metastasis in patients with gastric cancer

Lymph node metastasis, a crucial factor in the spread of gastric cancer (GC), is strongly associated with a negative prognosis for patients. This study aimed to investigate the association of the mesothelin (MSLN) gene polymorphisms (rs3764247, rs3764246, rs12597489, rs1057147, and rs3765319) with the risk of lymph node metastasis of GC patients in a Chinese Han population. The PCR-LDR genotyping was used to detect the genotypes of MSLN polymorphisms in GC patients with lymph node metastasis (n = 610) or without (n = 356). Our research indicates that certain genetic markers, specifically rs3764247, rs3764246, rs12597489, and rs3765319, do not appear to be linked with an increased risk of lymph node metastasis in GC. However, we did observe that patients with the rs1057147 GA genotype exhibited a higher likelihood of lymph node metastasis in GC when compared to those with the GG genotype (OR = 1.33, 95% CI = 1.01 - 1.76, P = 0.045). Patients with rs1057147 GA + AA genotype were found to have a higher likelihood of lymph node involvement (OR = 1.35, 95% CI = 1.03 - 1.77, P = 0.029) when compared to those with GG genotype in the dominant model. The allelic model revealed that the A allele of rs1057147 exhibited a stronger correlation with lymph node metastasis compared to the G allele (OR = 1.28, 95% CI = 1.02 - 1.60, P = 0.031). In addition, we found that rs1057147 polymorphism revealed a poor prognosis for GC patients with lymph node metastasis. Further stratified analysis revealed that the prognostic effect of rs1057147 was more pronounced in patients with GC who had lymph node metastasis and had a tumor size of 4 cm or greater, as well as more than 2 lymph node metastases. Bioinformatics studies showed that the binding mode of miR-3144-5p or miR-3619-3p to MSLN was altered by the mutation of rs1057147. Our study confirmed the important role of MSLN rs1057147 polymorphism locus in GC lymph node metastases and suggested a potential prognostic factor during GC progression. KEY POINTS: • Rs1057147 GA genotype had an increased risk of lymph node metastasis in gastric cancer. • The A allele of rs1057147 had a stronger association with lymph node metastasis than the G allele. • The binding mode of miR-3144-5p or miR-3619-3p to MSLN was altered by the mutation of rs1057147.

Construction of a nomogram for preoperative prediction of the risk of lymph node metastasis in early gastric cancer

BACKGROUND

The status of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) is particularly important for the formulation of clinical treatment. The purpose of this study was to construct a nomogram to predict the risk of LNM in EGC before operation.

METHODS

Univariate analysis and logistic regression analysis were used to determine the independent risk factors for LNM. The independent risk factors were included in the nomogram, and the prediction accuracy, discriminant ability and clinical practicability of the nomogram were evaluated by the receiver operating characteristic curve (ROC), calibration curve and clinical decision curve (DCA), and 100 times ten-fold cross-validation was used for internal validation.

RESULTS

33 (11.3%) cases of AGC were pathologically confirmed as LNM. In multivariate analysis, T stage, presence of enlarged lymph nodes on CT examination, carbohydrate antigen 199 (CA199), undifferentiated histological type and systemic inflammatory response index (SIRI) were risk factors for LNM. The area under the ROC curve of the nomogram was 0.86, the average area under the ROC curve of the 100-fold ten-fold cross-validation was 0.85, and the P value of the Hosmer-Lemeshow test was 0.60. In addition, the clinical decision curve, net reclassification index (NRI) and Integrated Discriminant Improvement Index (IDI) showed that the nomogram had good clinical utility.

CONCLUSIONS

We found that SIRI is a novel biomarker for preoperative prediction of LNM in EGC, and constructed a nomogram for preoperative prediction of the risk of LNM in EGC, which is helpful for the formulation of the clinical treatment strategies.

Early gastric cancer: A challenge in Western countries

Early gastric cancer (EGC) is an invasive carcinoma involving only the stomach mucosa or submucosa, independently of lymph node status. EGC represents over 50% of cases in Japan and in South Korea, whereas it accounts only for approximately 20% of all newly diagnosed gastric cancers in Western countries. The main classification systems of EGC are the Vienna histopathologic classification and the Paris endoscopic classification of polypoid and non-polypoid lesions. A careful endoscopic assessment is fundamental to establish the best treatment of EGC. Generally, EGCs are curable if the lesion is completely removed by endoscopic resection or surgery. Some types of EGC can be resected endoscopically; for others the most appropriate treatment is surgical resection and D2 lymphadenectomy, especially in Western countries. The favorable oncological prognosis, the extended lymphadenectomy and the reconstruction of the intestinal continuity that excludes the duodenum make the prophylactic cholecystectomy mandatory to avoid the onset of biliary complications.