Principles of Endoscopic Resection: Diagnostic and Curative Resection of Mucosal Neoplasias

Abstract Objective and study aims To evaluate the efficacy and safety of different endoscopic resection techniques for laterally spreading colorectal tumors (LST). Methods Relevant studies were identified in three electronic databases (PubMed, ISI and Cochrane Central Register). We considered all clinical studies in which colorectal LST were treated with endoscopic resection (endoscopic mucosal resection [EMR] and/or endoscopic submucosal dissection [ESD]) and/or transanal minimally invasive surgery (TEMS). Rates of en-bloc/piecemeal resection, complete endoscopic resection, R0 resection, curative resection, adverse events (AEs) or recurrence, were extracted. Study quality was assessed with the Newcastle-Ottawa Scale and a meta-analysis was performed using a random-effects model. Results Forty-nine studies were included. Complete resection was similar between techniques (EMR 99.5 % [95 % CI 98.6 %-100 %] vs. ESD 97.9 % [95 % CI 96.1 – 99.2 %]), being curative in 1685/1895 (13 studies, pooled curative resection 90 %, 95 % CI 86.6 – 92.9 %, I2 = 79 %) with non-significantly higher curative resection rates with ESD (93.6 %, 95 % CI 91.3 – 95.5 %, vs. 84 % 95 % CI 78.1 – 89.3 % with EMR). ESD was also associated with a significantly higher perforation risk (pooled incidence 5.9 %, 95 % CI 4.3 – 7.9 %, vs. EMR 1.2 %, 95 % CI 0.5 – 2.3 %) while bleeding was significantly more frequent with EMR (9.6 %, 95 % CI 6.5 – 13.2 %; vs. ESD 2.8 %, 95 % CI 1.9 – 4.0 %). Procedure-related mortality was 0.1 %. Recurrence occurred in 5.5 %, more often with EMR (12.6 %, 95 % CI 9.1 – 16.6 % vs. ESD 1.1 %, 95 % CI 0.3 – 2.5 %), with most amenable to successful endoscopic treatment (87.7 %, 95 % CI 81.1 – 93.1 %). Surgery was limited to 2.7 % of the lesions, 0.5 % due to AEs. No data of TEMS were available for LST. Conclusions EMR and ESD are both effective and safe and are associated with a very low risk of procedure related mortality.

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Endoscopic resection for gastric epithelial neoplasia: How to solve pathological discrepancy and achieve curative resection?

Journal of Digestive Diseases ◽

10.1111/1751-2980.12032 ◽

2013 ◽

Vol 14 (5) ◽

pp. 231-237 ◽

Cited By ~ 4

Author(s):

Ling Yin Zhu ◽

Jun Dai ◽

Yun Jia Zhao ◽

Han Bin Xue ◽

Zhi Zheng Ge ◽

...

Keyword(s):

Endoscopic Resection ◽

Curative Resection ◽

Gastric Epithelial Neoplasia

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Risk Factors and Prediction Model for Non-curative Resection of Early Gastric Cancer With Endoscopic Resection and the Evaluation

Frontiers in Medicine ◽

10.3389/fmed.2021.637875 ◽

2021 ◽

Vol 8 ◽

Author(s):

Xiaoqian Ma ◽

Qian Zhang ◽

Shengtao Zhu ◽

Shutian Zhang ◽

Xiujing Sun

Keyword(s):

Gastric Cancer ◽

Early Gastric Cancer ◽

Prediction Model ◽

Receiver Operating Characteristic ◽

Predictive Model ◽

Endoscopic Resection ◽

Tumor Size ◽

Curative Resection ◽

Operating Characteristic ◽

Resection Group

Background and Study Aim: EGC, also known as Early Gastric Cancer is known to lack the lymph node metastasis and confined along the mucosa, which is treated through an endoscopic resection procedure that includes ESD (Endoscopic Submucosal dissection) and EMR (Endoscopic Mucosal Resection). However, some cases underwent residual disease, recurrence, or additional gastrectomy because of non-curative resection. The following research aims to delineate the threat factors causing the non-curative resection as well as develop a predictive model.Patient and Methods: Effort was taken to collect all the records about the health history of pathologically diagnosed EGC who experienced endoscopic treatment in the Department of Endoscopy, the Capital Medical University, and Beijing Friendship Hospital from January 2012 to January 2020. Patients were grouped into two categories primarily; a curative resection group and finally a non-curative resection group based on the outcomes of the postoperative pathological and immunohistochemical examination results. The statistical methods used included single factor analysis, a multivariate logistic regression analysis and a chi-square test. A nomogram for the prediction of non-curative resection was constructed, which included information on age, gender, resection method, postoperative pathology, tumor size, ulcer, treatment, and infiltration depth. Receiver operating characteristic (ROC) curve analysis and calibration were performed to present the predictive accuracy of the nomogram.Results: Of 443 patients with 478 lesions who had undergone ESD or EMR for EGCs, 127 were identified as being treated non-curative resection. Older patients (>60 years), a large tumor size (>30 mm), submucosal lesion, piecemeal resection, EMR for treatment and undifferentiated tumor histology were associated with non-curative resection group. Our risk nomogram showed good discriminated performance in internal validation (bootstrap-corrected area under the receiver-operating characteristic curve, 0.881; P < 0.001).Conclusions: A validated prediction model was developed to identify people who were subject to undergoing a non-curative resection for ESD. The predictive model that we formulated is essential in providing reliable information to guide the decision-making process on the treatment for EGC before undertaking an endoscopic resection.

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Management of Non-curative Resection and Local Recurrence after Endoscopic Resection

Endoscopy in Early Gastrointestinal Cancers, Volume 2 ◽

10.1007/978-981-10-6778-5_16 ◽

2020 ◽

pp. 125-131

Author(s):

Yoji Takeuchi ◽

Noriya Uedo ◽

Ryu Ishihara

Keyword(s):

Local Recurrence ◽

Endoscopic Resection ◽

Curative Resection

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Endoscopic prediction of deeply submucosal invasive carcinoma with use of artificial intelligence

Endoscopy International Open ◽

10.1055/a-0849-9548 ◽

2019 ◽

Vol 07 (04) ◽

pp. E514-E520 ◽

Cited By ~ 13

Author(s):

Thomas Lui ◽

Kenneth Wong ◽

Loey Mak ◽

Michael Ko ◽

Stephen Tsao ◽

...

Keyword(s):

Artificial Intelligence ◽

Endoscopic Resection ◽

Curative Resection ◽

Narrow Band Imaging ◽

Colonic Lesion ◽

Endoscopic Images ◽

Well Differentiated ◽

Validation Set ◽

White Light Imaging ◽

Better Than

Abstract Background and study aims We evaluated use of artificial intelligence (AI) assisted image classifier in determining the feasibility of curative endoscopic resection of large colonic lesion based on non-magnified endoscopic images Methods AI image classifier was trained by 8,000 endoscopic images of large (≥ 2 cm) colonic lesions. The independent validation set consisted of 567 endoscopic images from 76 colonic lesions. Histology of the resected specimens was used as gold standard. Curative endoscopic resection was defined as histology no more advanced than well-differentiated adenocarcinoma, ≤ 1 mm submucosal invasion and without lymphovascular invasion, whereas non-curative resection was defined as any lesion that could not meet the above requirements. Performance of the trained AI image classifier was compared with that of endoscopists. Results In predicting endoscopic curative resection, AI had an overall accuracy of 85.5 %. Images from narrow band imaging (NBI) had significantly higher accuracy (94.3 % vs 76.0 %; P < 0.00001) and area under the ROC curve (AUROC) (0.934 vs 0.758; P = 0.002) than images from white light imaging (WLI). AI was superior to two junior endoscopists in terms of accuracy (85.5 % vs 61.9 % or 82.0 %, P < 0.05), AUROC (0.837 vs 0.638 or 0.717, P < 0.05) and confidence level (90.1 % vs 83.7 % or 78.3 %, P < 0.05). However, there was no statistical difference in accuracy and AUROC between AI and a senior endoscopist. Conclusions The trained AI image classifier based on non-magnified images can accurately predict probability of curative resection of large colonic lesions and is better than junior endoscopists. NBI images have better accuracy than WLI for AI prediction.

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