The risk factors for each recurrence pattern and timing of gastric cancer can be predicted by the clinicopathological features of the primary tumour. Since the results of treatment remain dismal, studies of perioperative adjuvant therapy in an attempt to reduce recurrence are warranted.

This study demonstrated that RAG with lymphadenectomy can be applied safely and effectively for patients with gastric cancer.

We observe the formation of optical precursors while propagating 540 fs pulses through 700 mm of deionized water. The launched pulses were strongly chirped to give them a bandwidth of approximately 60 nm to more readily excite the precursors. The precursors attenuated nonexponentially with distance.

In this context, it could be assumed that experienced laparoscopic surgeons could perform robotic gastrectomy with a certain level of skill, even in initial series.

In early gastric cancer (EGC), tumor invasion depth is an important factor for determining the treatment method. However, as endoscopic ultrasonography has limitations when measuring the exact depth in a clinical setting as endoscopists often depend on gross findings and personal experience. The present study aimed to develop a model optimized for EGC detection and depth prediction, and we investigated factors affecting artificial intelligence (AI) diagnosis. We employed a visual geometry group(VGG)-16 model for the classification of endoscopic images as EGC (T1a or T1b) or non-EGC. To induce the model to activate EGC regions during training, we proposed a novel loss function that simultaneously measured classification and localization errors. We experimented with 11,539 endoscopic images (896 T1a-EGC, 809 T1b-EGC, and 9834 non-EGC). The areas under the curves of receiver operating characteristic curves for EGC detection and depth prediction were 0.981 and 0.851, respectively. Among the factors affecting AI prediction of tumor depth, only histologic differentiation was significantly associated, where undifferentiated-type histology exhibited a lower AI accuracy. Thus, the lesion-based model is an appropriate training method for AI in EGC. However, further improvements and validation are required, especially for undifferentiated-type histology.