Background: Pathological regression grade after chemotherapy evaluated by surgically resected specimens is closely related with prognosis. Since usefulness of measuring the area of the residual tumor (ART) has been reported, this study aimed to evaluate the utility of ART in predicting the prognosis of patients with gastric cancer (GC) who received preoperative chemotherapy.
Methods: This single-center retrospective study examined the relationship between ART and survival outcomes.
Purpose: The incidence of esophagogastric junction (EGJ) adenocarcinoma has increased worldwide. As the EGJ is located at the boundary between the thoracic and abdominal cavities, the optimal surgical approach is a subject of debate and estimation of the esophageal invasion length (EIL) is an important factor in its selection.
Methods: Data from our in-house database were extracted for consecutive patients with Siewert type I, II and III EGJ adenocarcinoma (EIL ≤ 4 cm), who underwent transhiatal or transthoracic surgical resection between 2010 and 2016.
Background: Robotic-assisted surgery has become increasingly popular worldwide in recent years. This study aimed to compare the surgical outcomes of robotic total gastrectomy (RTG) and laparoscopic total gastrectomy (LTG) to figure out the advantages of RTG.
Methods: The eligible cases in this study were patients who underwent RTG or LTG for gastric adenocarcinoma at our hospital from January 2014 to December 2022.
Background: The oncological efficacy of laparoscopic surgery for advanced gastric cancer (AGC) has been evaluated by several randomized trials. However, the inclusion of earlier-stage disease was a limitation in previous studies.
Methods: Patients with cT3-4 gastric cancer, determined by surgical staging to minimize migration of earlier stages, treated at a tertiary cancer center from 2009 to 2018 were included.
Purpose: Laparoscopic distal gastrectomy (LDG) is a difficult procedure for early career surgeons. Artificial intelligence (AI)-based surgical step recognition is crucial for establishing context-aware computer-aided surgery systems. In this study, we aimed to develop an automatic recognition model for LDG using AI and evaluate its performance.
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