Endoscopic Submucosal Dissection of Early Gastric Cancer with Mixed-Type Histology: A Systematic Review

A Machine Learning-Driven Surface-Enhanced Raman Scattering Analysis Platform for the Label-Free Detection and Identification of Gastric Lesions

Background

Gastric lesions pose significant clinical challenges due to their varying degrees of malignancy and difficulty in early diagnosis. Early and accurate detection of these lesions is crucial for effective treatment and improved patient outcomes.

Methods

This paper proposed a label-free and highly sensitive classification method for serum of patients with different degrees of gastric lesions by combining surface-enhanced Raman scattering (SERS) and machine learning analysis. Specifically, we prepared Au lotus-shaped (AuLS) nanoarrays substrates using seed-mediated and liquid-liquid interface self-assembly method for measuring SERS spectra of serum, and then the collected spectra were processed by principal component analysis (PCA) - multi-local means based nearest neighbor (MLMNN) model to achieve differentiation.

Results

By employing this pattern analysis, AuLS nanoarray substrates can achieve fast, sensitive, and label-free serum spectral detection. The classification accuracy can reach 97.5%, the sensitivity is higher than 96.7%, and the specificity is higher than 95.0%. Moreover, by analyzing the PCs loading plots, the most critical spectral features distinguishing different degrees of gastric lesions were successfully captured.

Conclusion

This discovery lays the foundation for combining SERS with machine learning for real-time diagnosis and recognition of gastric lesions.

Mixed Histologic Type is a Risk Factor for Lymph Node Metastasis in Submucosal Invasive Early Gastric Cancer

INTRODUCTION

The treatment regimen for early gastric cancer (EGC) with mixed histologic type remains controversial. We aimed to clarify the relationship between mixed histologic type and lymph node metastasis (LNM) in EGC, with emphasis on submucosal invasive EGC.

METHODS

We collected data on 730 consecutive EGC patients at Nanjing Drum Tower hospital between June 2010 and May 2019. Risk factors of LNM and overall survival were analyzed to compare the prognostic differences between different histologic types.

RESULTS

Mixed-type EGC patients had higher LNM rates than differentiated-type patients (29.2 % versus 10.6 %, P < 0.001), while no significant difference was found between mixed-type and undifferentiated-type EGC patients (29.2% versus 24.0%, P = 0.225). Multivariate analyses identified tumor location (cardiac and bottom versus antrum), larger tumor size, submucosal invasion, histologic differentiation (undifferentiated-type, mixed-type versus differentiated-type), and lymphovascular invasion as independent risk factors for LNM in EGC patients. Subgroup analysis further elucidated that mixed histologic type was associated with LNM in submucosa invasive EGC, but not in mucosa-confined EGC. There was no statistical significance in overall survival and disease-specific survival of submucosal invasive EGC patients who underwent radical gastrectomy with lymphadenectomy between different histologic types (P = 0.151).

CONCLUSIONS

Mixed histologic type may be an independent risk factor for LNM in submucosal invasive EGC. Curative resection with lymphadenectomy should be considered the appropriate treatment for submucosal invasive EGC with mixed histologic type.

Endoscopic Resection of Undifferentiated Early Gastric Cancer

Endoscopic resection (ER) is widely performed for early gastric cancer (EGC) with a negligible risk of lymph node metastasis (LNM) in Eastern Asian countries. In particular, endoscopic submucosal dissection (ESD) leads to a high en bloc resection rate, enabling accurate pathological evaluation. As undifferentiated EGC (UD-EGC) is known to result in a higher incidence of LNM and infiltrative growth than differentiated EGC (D-EGC), the indications for ER are limited compared with those for D-EGC. Previously, clinical staging as intramucosal UD-EGC ≤2 cm, without ulceration, was presented as 'weakly recommended' or 'expanded indications' for ER in the guidelines of the United States, Europe, Korea, and Japan. Based on promising long-term outcomes from a prospective multicenter study by the Japan Clinical Oncology Group (JCOG) 1009/1010, the status of this indication has expanded and is now considered 'absolute indications' in the latest Japanese guidelines published in 2021. In this study, which comprised 275 patients with UD-EGC (cT1a, ≤2 cm, without ulceration) treated with ESD, the 5-year overall survival (OS) was 99.3% (95% confidence interval, 97.1%-99.8%), which was higher than the threshold 5-year OS (89.9%). Currently, the levels of evidence grades and recommendations for ER of UD-EGC differ among Japan, Korea, and Western countries. Therefore, a further discussion is warranted to generalize the indications for ER of UD-EGC in countries besides Japan.

Therapeutic approach to non-curative resection after endoscopic treatment in early gastric cancer

Background: Endoscopic resection is indicated for early or superficial gastrointestinal neoplasms with a negligible risk of lymph node metastasis. This procedure could preserve the organ while allowing en bloc resection of tumors, irrespective of the size and location of the lesion. Histological evaluation of the resected specimen determines whether curative resection, which implies a favorable long-term outcome, was achieved. If the resected specimen reveals non-curative, additional treatment is necessary as it is strongly associated with recurrence.Current Concepts: Surgical resection is recommended after non-curative resection of gastrointestinal neoplasms. However, rather than surgical resection, additional endoscopic treatment can be recommended if non-curative resection is solely because of the positive involvement at the horizontal resection margin without any other findings compatible with the non-curative resection criteria.Discussion and Conclusion: Adopting precise indications of endoscopic resection is important to reduce the risk of non-curative resection. If curative resection is not achieved after endoscopic resection, additional treatment should be considered to prevent local recurrence as well as lymph node metastasis.

Establishing Machine Learning Models to Predict Curative Resection in Early Gastric Cancer with Undifferentiated Histology: Development and Usability Study

Background

Undifferentiated type of early gastric cancer (U-EGC) is included among the expanded indications of endoscopic submucosal dissection (ESD); however, the rate of curative resection remains unsatisfactory. Endoscopists predict the probability of curative resection by considering the size and shape of the lesion and whether ulcers are present or not. The location of the lesion, indicating the likely technical difficulty, is also considered.

Objective

The aim of this study was to establish machine learning (ML) models to better predict the possibility of curative resection in U-EGC prior to ESD.

Methods

A nationwide cohort of 2703 U-EGCs treated by ESD or surgery were adopted for the training and internal validation cohorts. Separately, an independent data set of the Korean ESD registry (n=275) and an Asan medical center data set (n=127) treated by ESD were chosen for external validation. Eighteen ML classifiers were selected to establish prediction models of curative resection with the following variables: age; sex; location, size, and shape of the lesion; and whether ulcers were present or not.

Results

Among the 18 models, the extreme gradient boosting classifier showed the best performance (internal validation accuracy 93.4%, 95% CI 90.4%-96.4%; precision 92.6%, 95% CI 89.5%-95.7%; recall 99.0%, 95% CI 97.8%-99.9%; and F1 score 95.7%, 95% CI 93.3%-98.1%). Attempts at external validation showed substantial accuracy (first external validation 81.5%, 95% CI 76.9%-86.1% and second external validation 89.8%, 95% CI 84.5%-95.1%). Lesion size was the most important feature in each explainable artificial intelligence analysis.

Conclusions

We established an ML model capable of accurately predicting the curative resection of U-EGC before ESD by considering the morphological and ecological characteristics of the lesions.

Mixed histology poses a greater risk for noncurative endoscopic resection in early gastric cancers regardless of the predominant histologic types

OBJECTIVES

Clinicopathologic characteristics and treatment outcomes of mixed-histological-type (MT) early gastric cancers (EGCs) treated with endoscopic submucosal dissection (ESD) have not been sufficiently elucidated. We aimed to clarify them in comparison with pure-histological-type EGCs.

METHODS

We used 3022 consecutive EGCs in 2281 patients treated with ESD from our prospectively maintained database. Cases were stratified into four groups according to the final diagnosis of the resected specimen are as follows: 2780 pure differentiated-type (DT), 127 DT-predominant MT (D-MT), 87 pure undifferentiated-type (UDT), and 28 UDT-predominant MT (U-MT). Clinicopathologic characteristics and treatment outcome were compared between pure DT and D-MT, and between pure UDT and U-MT separately. Risk factors for deep submucosal invasion, lymphovascular invasion, and a final diagnosis of MT were identified using multivariate analysis.

RESULTS

Both D-MT (41.7 vs. 92.0%; P < 0.0001) and U-MT (35.7 vs. 75.9%; P = 0.0002) showed a significantly lower curative resection rate than their pure histologic counterparts. Multivariate analysis revealed that MT was an independent risk factor for deep submucosal (OR 6.55; 95% CI, 4.18-10.14) and lymphovascular (OR 4.74; 95% CI, 2.72-8.29) invasion. Preoperative biopsy results that did not show well-differentiated tubular adenocarcinoma (OR 28.2; 95% CI, 18.9-42.9) were an independent risk factor for a final diagnosis of MT.

CONCLUSIONS

MT poses a greater risk for noncurative resection regardless of the predominant histologic types, reflecting more aggressive malignant potential. Although a biopsy examination rarely shows MT, clinicians should consider the possibility of MT when a biopsy examination does not show well-differentiated tubular adenocarcinoma.

Predictive Factors and Long-Term Outcomes of Early Gastric Carcinomas in Patients with Non-Curative Resection by Endoscopic Submucosal Dissection

Purpose

Non-curative resection (NCR) remains problematic in some cases of early gastric carcinomas (EGCs) treated by endoscopic submucosal dissection (ESD). The aim of this study was to identify predictors of NCR, especially of eCura C1 and eCura C2 resections, before ESD and study long-term outcomes of EGC patients with NCR.

Patients and Methods

A retrospective review of medical records was conducted over an 8-year period for EGCs undergoing ESD. Clinicopathologic and endoscopic characteristics and patients’ survival were analyzed. Risk factors for NCR and eCura C1 and C2 resections were assessed by logistic analyses. Survival of patients was estimated with the Kaplan–Meier method with a Log rank test.

Results

A total of 463 patients with 472 lesions were qualified. By univariate and multivariate analyses, the predictors for NCR and eCura C2 resections were tumor size >20 mm, tumors located in cardia-fundus, uneven surface, margin elevation, and mixed and undifferentiated types, and those for eCura C1 resection were tumors located in cardia-fundus, negative lifting sign, and mixed and undifferentiated types. The 5-year cancer-specific and cancer-free survival rates were 100.0% and 94.2%, and 95.3% and 83.4% in the curative resection (CR) and NCR groups, respectively. The 5-year cancer-specific and cancer-free survival rates were significantly greater in the CR group than that in the NCR group (P <0.0001).

Conclusion

In this cohort, we identified various endoscopic and pathologic features of EGCs to predict NCR, especially eCura C1 and eCura C2 resections before ESD. These clinically valuable factors would be very informative to endoscopists and surgeons who perform ESD to resect EGCs.

Prediction of Submucosal Invasion for Gastric Neoplasms in Endoscopic Images Using Deep-Learning

Endoscopic resection is recommended for gastric neoplasms confined to mucosa or superficial submucosa. The determination of invasion depth is based on gross morphology assessed in endoscopic images, or on endoscopic ultrasound. These methods have limited accuracy and pose an inter-observer variability. Several studies developed deep-learning (DL) algorithms classifying invasion depth of gastric cancers. Nevertheless, these algorithms are intended to be used after definite diagnosis of gastric cancers, which is not always feasible in various gastric neoplasms. This study aimed to establish a DL algorithm for accurately predicting submucosal invasion in endoscopic images of gastric neoplasms. Pre-trained convolutional neural network models were fine-tuned with 2899 white-light endoscopic images. The prediction models were subsequently validated with an external dataset of 206 images. In the internal test, the mean area under the curve discriminating submucosal invasion was 0.887 (95% confidence interval: 0.849–0.924) by DenseNet−161 network. In the external test, the mean area under the curve reached 0.887 (0.863–0.910). Clinical simulation showed that 6.7% of patients who underwent gastrectomy in the external test were accurately qualified by the established algorithm for potential endoscopic resection, avoiding unnecessary operation. The established DL algorithm proves useful for the prediction of submucosal invasion in endoscopic images of gastric neoplasms.

Endoscopic Submucosal Dissection of Papillary Gastric Adenocarcinoma; Systematic Review

This study evaluated the possibility of endoscopic submucosal dissection (ESD) for early gastric cancer with papillary adenocarcinoma (EGC-PAC). PAC, an uncommon pathologic type of stomach cancer, is classified into differentiated-type histology. However, aggressive features, including a high rate of submucosal invasion, lymphovascular invasion (LVI), and lymph node metastasis (LNM), have been reported in studies with surgical specimens. Treatment outcomes of ESD for EGC-PAC have not been precisely demonstrated. Core databases were sought for the following inclusion criteria: studies of endoscopic resection or surgery of EGC-PAC presenting the following therapeutic indicators; en bloc resection, complete resection, curative resection, recurrence, complications associated with procedures, LVI, or LNM that enabled an analysis of ESD possibility. Overall, 15 studies were included for systematic review. Frequent submucosal invasion and high LVI were noted in EGC-PAC. However, PAC was not significantly associated with LNM. Pooled en bloc resection, complete resection, and curative resection rates were 89.7% (95% confidence interval: 55.3%-98.4%), 85.3% (67.7%-94.2%), and 67% (43%-84.5%), respectively. No LNM was observed if EGC-PAC satisfied the curative resection criteria. ESD seems technically feasible, although a high LVI rate results in a lower rate of curative resection.

Risk factors of tumor invasion and node metastasis in early gastric cancer with undifferentiated component: a multicenter retrospective study on biopsy specimens and clinical data

Background

Early gastric cancer (EGC) with undifferentiated component (UDC) is a more aggressive entity, where the significance of preoperative data to tumor invasion and lymph node metastasis (LNM) remains unclarified.

Methods

A total of 5,020 GC patients undergoing radical gastrectomy in three centers were reviewed, of which, EGC with UDC in preoperative biopsy specimens were enrolled. The histology of biopsy and surgical specimens was graded according to the proportion of UDC and signet ring cells (SRCs). Risk factors of tumor invasion and LNM were evaluated with histological, clinical and demographic data.

Results

Lower body mass index (BMI), melena and larger tumor size were the independent preoperative risk factors of both LNM and LVI, while ulcerative lesion (UL) and the lower third stomach were only correlated with LNM. No relevance was found between the histological features of biopsy specimens and LNM, but SRC or >50% UDC lowered the risk of lymphovascular invasion (LVI) and/or submucosal (SM) invasion. When surgical data (depth of invasion and LVI included) were added, lower BMI, melena and the lower third stomach were still the independent preoperative risk factors of LNM, and LVI, SRC and SM invasion also showed relevance to LNM. The performance of predictive models using pre- or postoperative histological data was comparable.

Conclusions

The preoperative data were significantly relevant to tumor invasion and LNM, showing comparable risk strength with surgical specimens in histology.