Comparable long-term outcomes after endoscopic therapy and gastrectomy of early adenocarcinoma of esophagogastric junction: a population-based study

Guo-Hui, Mei; Zhu, Min; Chang, Zhenyu; Long, Jianhai; Chun-xi, Wang; Yang, Zelong

doi:10.1007/s00464-022-09187-w

Cited by 2 publications

(5 citation statements)

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“…However, endoscopic treatment (including ESD) remains an effective alternative to surgery for the treatment of early AEG based on comparable long-term outcomes ( 18 , 20 ). With the advancements in endoscopic treatment, early AEG can be effectively resected by EMR/ESD with fewer complications, better preservation of gastric function, a shorter duration of hospital stay, and a lower cost compared with traditional gastrectomy or esophagectomy ( 16 , 17 ). Endoscopic treatment is gaining acceptance because it is more tolerable, especially in elderly patients.…”

Section: Discussionmentioning

confidence: 99%

“…Naoki et al ( 23 ) found that LNM was the only independent prognostic factor for AEG in their study. To achieve better endoscopic treatment effects, early AEG should meet certain standards (including no lymph node and distant organ metastasis) ( 12 , 16 , 18 ). In some studies, the location of the LNM has been found to have a significant impact on the surgical method and the scope of lymph node dissection ( 39–41 ).…”

Section: Discussionmentioning

confidence: 99%

“…Frontiers in Medicine 08 frontiersin.org treatment, early AEG can be effectively resected by EMR/ESD with fewer complications, better preservation of gastric function, a shorter duration of hospital stay, and a lower cost compared with traditional gastrectomy or esophagectomy (16,17). Endoscopic treatment is gaining acceptance because it is more tolerable, especially in elderly patients.…”

Section: Discussionmentioning

confidence: 99%

“…However, with gastrointestinal endoscopy screening, patients with digestive tract cancer are diagnosed at an early stage, making it possible to treat early AEG without lymphatic and organ metastasis by endoscopy. The endoscopic resection of superficial AEG, such as endoscopic mucosal resection (EMR) and endoscopic submucosal dissection (ESD), is considered safe and effective (16)(17)(18)(19)(20)(21). Endoscopic resection techniques are increasingly being employed for early AEG, leading to a reduction in the morbidity and mortality associated with gastrectomy or esophagectomy and an improvement in the quality of life (16).…”

Section: Introductionmentioning

confidence: 99%

“…The endoscopic resection of superficial AEG, such as endoscopic mucosal resection (EMR) and endoscopic submucosal dissection (ESD), is considered safe and effective (16)(17)(18)(19)(20)(21). Endoscopic resection techniques are increasingly being employed for early AEG, leading to a reduction in the morbidity and mortality associated with gastrectomy or esophagectomy and an improvement in the quality of life (16).…”

Section: Introductionmentioning

confidence: 99%

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The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction

Lu,

Liu,

Yin

et al. 2024

Front. Med.

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BackgroundLymph node metastasis (LNM) is considered an essential prognosis factor for adenocarcinoma of the esophagogastric junction (AEG), which also affects the treatment strategies of AEG. We aimed to evaluate automated machine learning (AutoML) algorithms for predicting LNM in Siewert type II T1 AEG.MethodsA total of 878 patients with Siewert type II T1 AEG were selected from the Surveillance, Epidemiology, and End Results (SEER) database to develop the LNM predictive models. The patients from two hospitals in Suzhou were collected as the test set. We applied five machine learning algorithms to develop the LNM prediction models. The performance of predictive models was assessed using various metrics including accuracy, sensitivity, specificity, the area under the curve (AUC), and receiver operating characteristic (ROC) curve.ResultsPatients with LNM exhibited a higher proportion of male individuals, a poor degree of differentiation, and submucosal infiltration, with statistical differences. The deep learning (DL) model demonstrated relatively good accuracy (0.713) and sensitivity (0.868) among the five models. Moreover, the DL model achieved the highest AUC (0.781) and sensitivity (1.000) in the test set.ConclusionThe DL model showed good predictive performance among five AutoML models, indicating the advantage of AutoML in modeling LNM prediction in patients with Siewert type II T1 AEG.

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