The future of endoscopic resection for early gastric cancer

Ortigão, Raquel; Libânio, Diogo; Dinis-Ribeiro, Mário

doi:10.1002/jso.26851

Cited by 5 publications

(3 citation statements)

References 96 publications

(173 reference statements)

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“…Preoperative evaluation of LNM in gastric cancer is still lacking of effective means. It is of great clinical significance to understand the characteristics and rules of LNM in EGC 16–18 …”

Section: Discussionmentioning

confidence: 99%

“…It is of great clinical significance to understand the characteristics and rules of LNM in EGC. [16][17][18] In this study, risk factors of LNM in EDGC cases were identified. As analysis results showed, preoperative hypoalbuminemia, significant preoperative weight loss, tumor diameter, location, and number, histological classification, tumor infiltration depth, and vascular infiltration tended to be associated with LNM.…”

Section: Discussionmentioning

confidence: 99%

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

Yu,

Liang,

et al. 2023

World j. surg.

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ObjectsThis 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.MethodsIn 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.ResultsOf 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.ConclusionsThe 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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Yu,

Liang,

et al. 2023

World j. surg.

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“…It generated a variety of images. The evaluation methods and the variables were different during a single moment [45]. Moreover, physicians and pathologists will be able to use CNN's findings as a determining the diagnosis of challenging cases.…”

Section: Researchersstudies and Articles Were Publishedmentioning

confidence: 99%

Diagnosis of gastric cancer in role of endoscopic imaging techniques in artificial intelligence and machine learning applications: An overview

K.,

Kanna R.

2024

E3S Web Conf.

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Gastric cancer is a serious medical issue because its occurrence and death rates are increasing all over the world. Furthermore, obesity, tobacco use, alcohol consumption, and a few dietary defense elements are known cancer-causing agents. In some nations, early detection strategies have been shown to reduce GC-related morbidity and mortality. It offers therapies that are minimally invasive like most effective procedure is endoscopic resection. The most appropriate standard for using a procedure that is typically secure to precisely evaluate the lesions region. It is simple method and it can be expected difficult techniques can be viewed as in early stage of tumour in accurate diagnosis. A few uses of computerized method have arisen in the field of gastric malignant growth. For example, image diagnosis-based prediction conclusion and guess expectation, because of its viable computational power and learning capabilities. As a result, a detailed outline of how artificial intelligence can be used to treat gastric cancer through image-based endoscopic diagnosis and machine learning analysis applications this review, which demonstrates the future developments in this field for the early prediction of gastric cancer, it was also thoroughly discussed the possibility of AI models being over fitted, their accuracy, and their usefulness to clinical research in this field of image processing. In addition, in this review article was been detailed about synopsis of the therapy choices of malignant growth.

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The simplified depth-predicting score outperforms the depth-predicting score for predicting the depth of invasion in differentiated early gastric cancer patients among nonexpert endoscopists

Zeng,

Li,

Huang

et al. 2024

Gastroenterología y Hepatología

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The future of endoscopic resection for early gastric cancer

Cited by 5 publications

References 96 publications

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

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

Diagnosis of gastric cancer in role of endoscopic imaging techniques in artificial intelligence and machine learning applications: An overview

The simplified depth-predicting score outperforms the depth-predicting score for predicting the depth of invasion in differentiated early gastric cancer patients among nonexpert endoscopists

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