Identification of LN-positive cases on the basis of PRLN and LPLN sizes was superior at a short-axis 5 mm cutoff. Size-based diagnosis of LN metastasis is simple and useful, but further investigation is needed to clarify whether it is superior to diagnosis based on morphology, such as shape, border, and signal intensity.

An LPLN-positive status with a short axis ≥5 mm on MRI is an important predictor of LPLN metastasis, but PRLN status is not a strong predictor of PRLN metastasis.

The present results suggested that neoadjuvant therapy with mFOLFOX6 is safe and effective, representing a reasonable treatment option for locally advanced rectal cancer.

Purpose Artificial intelligence-enabled techniques can process large amounts of surgical data and may be utilized for clinical decision support to recognize or forecast adverse events in an actual intraoperative scenario. To develop an image-guided navigation technology that will help in surgical education, we explored the performance of a convolutional neural network (CNN)-based computer vision system in detecting intraoperative objects. Methods The surgical videos used for annotation were recorded during surgeries conducted in the Department of Surgery of Tokyo Women's Medical University from 2019 to 2020. Abdominal endoscopic images were cut out from manually captured surgical videos. An open-source programming framework for CNN was used to design a model that could recognize and segment objects in real time through IBM Visual Insights. The model was used to detect the GI tract, blood, vessels, uterus, forceps, ports, gauze and clips in the surgical images. Results The accuracy, precision and recall of the model were 83%, 80% and 92%, respectively. The mean average precision (mAP), the calculated mean of the precision for each object, was 91%. Among surgical tools, the highest recall and precision of 96.3% and 97.9%, respectively, were achieved for forceps. Among the anatomical structures, the highest recall and precision of 92.9% and 91.3%, respectively, were achieved for the GI tract. ConclusionThe proposed model could detect objects in operative images with high accuracy, highlighting the possibility of using AI-based object recognition techniques for intraoperative navigation. Real-time object recognition will play a major role in navigation surgery and surgical education.

A logistic model including risk factors for LPLN metastasis and MRI findings had significantly better performance for prediction of LPLN metastasis compared with a model based on MRI findings alone.