Additional laparoscopic gastrectomy after noncurative endoscopic submucosal dissection for early gastric cancer: A single-center experience

Development and External Validation of a CT-Based Radiomics Nomogram to Predict Perineural Invasion and Survival in Gastric Cancer: A Multi-institutional Study

RATIONALE AND OBJECTIVES

To develop and validate a radiomics nomogram utilizing CT data for predicting perineural invasion (PNI) and survival in gastric cancer (GC) patients.

MATERIALS AND METHODS

A retrospective analysis of 408 GC patients from two institutions: 288 patients from Institution I were divided 7:3 into a training set (n = 203) and a testing set (n = 85); 120 patients from Institution II served as an external validation set. Radiomics features were extracted and screened from CT images. Independent radiomics, clinical, and combined models were constructed to predict PNI. Model discrimination, calibration, clinical utility, and prognostic significance were evaluated using area under the curve (AUC), calibration curves, decision curves analysis, and Kaplan-Meier curves, respectively.

RESULTS

15 radiomics features and three clinical factors were included in the final analysis. The AUCs of the radiomics model in the training, testing, and external validation sets were 0.843 (95% CI: 0.788-0.897), 0.831 (95% CI: 0.741-0.920), and 0.802 (95% CI: 0.722-0.882), respectively. A nomogram was developed by integrating significant clinical factors with radiomics features. The AUCs of the nomogram in the training, testing, and external validation sets were 0.872 (95% CI: 0.823-0.921), 0.862 (95% CI: 0.780-0.944), and 0.837 (95% CI: 0.767-0.908), respectively. Survival analysis revealed that the nomogram could effectively stratify patients for recurrence-free survival (Hazard Ratio: 4.329; 95% CI: 3.159-5.934; P < 0.001).

CONCLUSION

The radiomics-derived nomogram presented a promising tool for predicting PNI in GC and held significant prognostic implications.

IMPORTANT FINDINGS

The nomogram functioned as a non-invasive biomarker for determining the PNI status. The predictive performance of the nomogram surpassed that of the clinical model (P < 0.05). Furthermore, patients in the high-risk group stratified by the nomogram had a significantly shorter RFS (P < 0.05).

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.

Short-term efficacy of additional laparoscopic-assisted radical gastrectomy after non-curative endoscopic submucosal dissection for early gastric cancer

OBJECTIVE

To investigate short-term efficacy of direct laparoscopic-assisted radical gastrectomy (LAG) versus non-curative endoscopic submucosal dissection (ESD) plus additional LAG for early gastric cancer.

MATERIALS AND METHODS

286 patients were retrospectively assigned into two groups: direct LAG group (n = 255) and additional LAG (ESD plus LAG, n = 31) group. A 1:2 propensity score matching was performed to equalize relevant confounding factors between two groups for analysis.

RESULTS

Ninety-three patients were successfully matched, including 62 in the direct LAG group and 31 in the additional LAG group. A significant (P = 0.013) difference existed in the drainage removal time between the additional LAG and direct LAG group (7 d vs. 6 d). Age, sex, tumor location and surgical approach were significantly (P < 0.05) associated with complications, with age ≥ 60 years (P = 0.002) and total gastrectomy (P = 0.011) as significant independent risk factors. A significant (P = 0.023) difference existed in the surgical time between the early and late groups (193.3 ± 37.6 min vs. 165.5 ± 25.1 min).

CONCLUSION

Additional LAG (D1 + lymphadenectomy) after ESD may be safe and effective even though non-curative ESD may prolong the drainage removal time and increase the difficulty of surgery.

Using nomogram, decision tree, and deep learning models to predict lymph node metastasis in patients with early gastric cancer: a multi-cohort study

The accurate assessment of lymph node metastasis (LNM) in patients with early gastric cancer is critical to the selection of the most appropriate surgical treatment. This study aims to develop an optimal LNM prediction model using different methods, including nomogram, Decision Tree, Naive Bayes, and deep learning methods. In this study, we included two independent datasets: the gastrectomy set (n=3158) and the endoscopic submucosal dissection (ESD) set (n=323). The nomogram, Decision Tree, Naive Bayes, and fully convolutional neural networks (FCNN) models were established based on logistic regression analysis of the development set. The predictive power of the LNM prediction models was revealed by time-dependent receiver operating characteristic (ROC) curves and calibration plots. We then used the ESD set as an external cohort to evaluate the models' performance. In the gastrectomy set, multivariate analysis showed that gender (P=0.008), year when diagnosed (2006-2010 year, P=0.265; 2011-2015 year, P=0.001; and 2016-2020 year, P<0.001, respectively), tumor size (2-4 cm, P=0.001; and ≥4 cm, P<0.001, respectively), tumor grade (poorly-moderately, P=0.016; moderately, P<0.001; well-moderately, P<0.001; and well, P<0.001, respectively), vascular invasion (P<0.001), and pT stage (P<0.001) were independent risk factors for LNM in early gastric cancer. The area under the curve (AUC) for the validation set using the nomogram, Decision Tree, Naive Bayes, and FCNN models were 0.78, 0.76, 0.77, and 0.79, respectively. In conclusion, our multi-cohort study systematically investigated different LNM prediction methods for patients with early gastric cancer. These models were validated and shown to be reliable with AUC>0.76 for all. Specifically, the FCNN model showed the most accurate prediction of LNM risks in early gastric cancer patients with AUC=0.79. Based on the FCNN model, patients with LNM rates of >4.77% are strong candidates for gastrectomy rather than ESD surgery.

A retrospective analysis based on multiple machine learning models to predict lymph node metastasis in early gastric cancer

Background

Endoscopic submucosal dissection has become the primary option of treatment for early gastric cancer. However, lymph node metastasis may lead to poor prognosis. We analyzed factors related to lymph node metastasis in EGC patients, and we developed a construction prediction model with machine learning using data from a retrospective series.

Methods

Two independent cohorts’ series were evaluated including 305 patients with EGC from China as cohort I and 35 patients from Spain as cohort II. Five classifiers obtained from machine learning were selected to establish a robust prediction model for lymph node metastasis in EGC.

Results

The clinical variables such as invasion depth, histologic type, ulceration, tumor location, tumor size, Lauren classification, and age were selected to establish the five prediction models: linear support vector classifier (Linear SVC), logistic regression model, extreme gradient boosting model (XGBoost), light gradient boosting machine model (LightGBM), and Gaussian process classification model. Interestingly, all prediction models of cohort I showed accuracy between 70 and 81%. Furthermore, the prediction models of the cohort II exhibited accuracy between 48 and 82%. The areas under curve (AUC) of the five models between cohort I and cohort II were between 0.736 and 0.830.

Conclusions

Our results support that the machine learning method could be used to predict lymph node metastasis in early gastric cancer and perhaps provide another evaluation method to choose the suited treatment for patients.

Clinical significance of additional gastrectomy after non-curative endoscopic submucosal dissection for early gastric cancer: a retrospective single-center study

Purpose

Additional surgery is recommended for patients after a non-curative endoscopic submucosal dissection (ESD) to prevent residual cancer (RC) or lymph node metastasis (LNM). We aimed to evaluate the clinicopathologic characteristics of patients who underwent an additional gastrectomy after a non-curative ESD procedure and identify the risk factors of RC and LNM.

Methods

We retrospectively assessed the clinicopathological factors of 73 patients who underwent additional gastrectomy following a non-curative ESD between January 2009 and December 2019 at our center.

Results

RC and LNM rates after additional gastrectomy were 9.6% and 8.2%, respectively. Invasion deeper than 500 μm (P=0.045), positive horizontal resection margin (P<0.001), and positive ESD margin (P=0.001) were identified as statistically significant factors in univariate analysis for RC, but not in multivariate analysis. Lymphatic invasion was the only risk factor found to be significant in both univariate and multivariate analyses (P=0.005 and P=0.012).

Conclusion

Additional gastrectomy is necessary to prevent RC or LNM after non-curative ESD. Lymphatic invasion was also associated with LNM in patients who underwent an additional gastrectomy after a non-curative ESD, and in such cases, active treatment is required.

Endoscopic instruments and techniques in endoscopic submucosal dissection for early gastric cancer

Introduction: Endoscopic submucosal dissection (ESD) is a less invasive local treatment for early gastric cancer (EGC).Areas covered: Japanese guidelines have recently expanded the endoscopic treatment indications for EGC. ESD is sometimes performed for lesions with a relative indication that has not previously been considered for ESD. ESD procedures are technically difficult, especially for larger and more invasive lesions. Complete resection and accurate histological assessment of EGC lesions are essential to evaluate endoscopic curability. Various endoscopic instruments, including three types of endoscopic knives and specific hemostatic forceps, have been developed, and endoscopic techniques, including the traction method, have been modified to simplify the procedure.Expert opinion: An insulated-tip knife with large tissue contact allows us to perform faster incisions, while a needle-type knife with minimal tissue contact allows us to perform precise incisions. Furthermore, a scissor-type knife with grasping tissues allows us to perform more secure incision. The traction method enables the operator to proceed with submucosal dissection procedures. In cases with small lesions, snaring can be used, as well as knife dissection. Understanding the features of each endoscopic instrument and technique is essential because appropriate selection and usage contribute to successful and safe ESD procedures.

Impact of endoscopic resection on the choice of surgical procedure in patients with additional laparoscopic gastrectomy

BACKGROUND

Additional surgery is recommended for patients with noncurative resection after endoscopic submucosal dissection (ESD) for early gastric cancer. Additional resection requires the excision of an area larger than that of the resected mucosa in ESD, which is larger than the lesion, with convergence of the gastric mucosa due to scarring. Thus, the selection of the surgical procedure for lesion removal in specific areas can be affected by ESD. This study therefore aimed to evaluate the impact of ESD on the selection of additional gastrectomy in patients with early gastric cancer in the boundary area between the upper third and middle third of the stomach (UM boundary region).

METHODS

Between January 2013 and June 2018, laparoscopic gastrectomy was performed in 89 patients with cT1N0M0 gastric cancer located only in the UM boundary region. The patients' backgrounds and surgical and pathological results were retrospectively investigated. The predictive factors for performing laparoscopic distal gastrectomy (LDG) were evaluated by multivariate analysis.

RESULTS

Among 89 patients, 23 patients underwent ESD before surgery. LDG was significantly less often performed in the ESD-surgery group than in the surgery-only group (34.8% vs. 72.7%; p = 0.003). Preoperative ESD was an independent negative predictor of LDG (odds ratio = 0.266; p = 0.025).

CONCLUSIONS

Preoperative ESD has an impact on the selection of the type of additional gastrectomy, including reducing the conduct of LDG for early gastric cancer in the UM boundary region.