Endoscopic submucosal dissection (ESD) is considered the treatment of choice for early gastric cancer (EGC) with a negligible risk of lymph node metastasis. Locally recurrent lesions on artificial ulcer scars are difficult to manage. Predicting the risk of local recurrence after ESD is important to manage and prevent the event. We aimed to elucidate the risk factors associated with local recurrence after ESD of EGC. Between November 2008 and February 2016, consecutive patients (n = 641; mean age, 69.3 ± 9.5 years; men, 77.2%) with EGC who underwent ESD at a single tertiary referral hospital were retrospectively analyzed to evaluate the incidence and factors associated with local recurrence. Local recurrence was defined as the development of neoplastic lesions at or adjacent to the site of the post-ESD scar. En bloc and complete resection rates were 97.8% and 93.6%, respectively. The local recurrence rate after ESD was 3.1%. The mean follow-up period after ESD was 50.7 ± 32.5 months. One case of gastric cancer-related death (0.15%) was noted, wherein the patient had refused additive surgical resection after ESD for EGC with lymphatic and deep submucosal invasion. Lesion size ≥15 mm, incomplete histologic resection, undifferentiated adenocarcinoma, scar, and the absence of erythema of the surface were associated with a higher risk of local recurrence. Predicting local recurrence during regular endoscopic surveillance after ESD is important, especially in patients with a larger lesion size (≥15 mm), incomplete histologic resection, surface changes of scars, and no erythema of the surface.

Torsion of the appendix is rare, and appendiceal mucocele can be one of its causes. The first case was of a 49-year-old man who visited the emergency room (ER) for abdominal pain. Abdominal computed tomography (CT) showed appendiceal mucocele with suspected torsion and rupture. The patient underwent laparoscopic exploration and appendectomy. The second case was of a 69-year-old man who visited the ER for epigastric pain. Abdominal CT showed suspicious appendiceal mucocele with ischemic change, indicating torsion of the appendix. The twisted appendix was successfully removed by laparoscopic exploration. An appendiceal mucocele is one of the causes of twisted appendix. With torsion, the mucocele can be diagnosed as rupture by ischemia which may lead to pseudomyxoma peritonei. For this reason, open laparotomy has traditionally been preferred. However, an unruptured appendiceal mucocele or impending rupture with torsion of the appendiceal mucocele can be treated with totally laparoscopic surgery.

Jang S, et al. Ann Surg Treat Res. 2020 Oct;99(4):205-212. https://doi.org/10.4174/astr.2020.99.4.205

The management of gastroduodenal neuroendocrine tumor (NET) has been controversial between radical surgical resection and local excision including endoscopic resection. A gastroduodenal NET grade (G), measured by their mitotic rate and Ki67 proliferation index, is important to predict prognosis. In this study, we aimed to compare the clinical outcomes of gastroduodenal NET according to grades in order to identify poor prognostic factors of gastroduodenal NETs. Fifty-four gastroduodenal NETs diagnosed between December 2008 and December 2020 in a tertiary referral hospital were retrospectively reviewed. The clinical outcomes of gastroduodenal NETs, according to tumor grades and factors associated with NET G2-3, were analyzed. A total of 52 gastroduodenal NET patients was enrolled. The mean follow-up period was 56.2 ± 40.1 months. The mean size of gastric and duodenal NET was 7.9 ± 11.0 mm and 9.8 ± 7.6 mm, respectively. During the study period, 72.7% (16/22) of gastric NETs and 83.3% (25/30) of duodenal NETS were G1. All G1 gastroduodenal NETs showed no lymph node or distant metastasis during the study periods. All G3 gastroduodenal NETs showed metastasis (one lymph node metastasis and 3 hepatic metastases). Among metastatic NETs, the smallest tumor size was a 13 mm gastric G3 NET. Factors associated with G2-3 NETs were larger tumor size, mucosal ulceration, proper muscle or deeper invasion, and lymphovascular invasion. A small-sized gastroduodenal NET confined to submucosa without surface ulceration may be suitable for endoscopic resection. After local resection of a gastroduodenal NET (G1) without lymphovascular and muscle proper invasion, follow-up examination without radical surgical resection can be recommended. G3 NETs may be treated by radical surgical resection, regardless of tumor size.

Purpose: This study aimed to develop a machine learning (ML) model for real-time prediction of duodenal stump leakage (DSL) following gastrectomy in patients with gastric cancer (GC) using a comprehensive set of clinical variables to improve postoperative outcomes and monitoring efficiency.Methods: A retrospective analysis was conducted on 1,107 patients with GC who underwent gastrectomy at Pusan National University Yangsan Hospital between 2019 and 2022. One hundred eighty-nine features were extracted from each patient record, including demographic data, preoperative comorbidities, and blood test outcomes from the subsequent seven postoperative days (POD). Six ML algorithms were evaluated: Logistic Regression (LR), K-nearest neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB), and Neural Network (NN). The models predicted DSL occurrence preoperatively and on POD 1, 2, 3, 5, and 7. Performance was assessed using the Area Under the Receiver Operating Characteristic Curve (AUROC) and Recall@K.Results: Among the 1,107 patients, 29 developed DSL. XGB demonstrated the highest AUROC score (0.880), followed by RF (0.858), LR (0.823), SVM (0.819), NN (0.753), and KNN (0.726). The RF achieved the best Recall@K score of 0.643. Including additional POD features improved the predictive performance, with the AUROC value increasing to 0.879 on POD 7. The confidence scores of the model indicated that the DSL predictions became more reliable over time.Conclusion: The study concluded that ML models, notably the XGB algorithm, can effectively predict DSL in real-time using comprehensive clinical data, enhancing the clinical decision-making process for GC patients.