Shiga University of Medical Science

Multiple randomized trials comparing wide local excision (WLE) with different peripheral margins have established recommended peripheral margins according to Breslow thickness (BT) in the National Comprehensive Cancer Network (NCCN) Guidelines. However, these clinical trials included only a small number of patients with acral melanoma (AM). Therefore, we aimed to compare the prognosis of different WLE peripheral margin cohorts with invasive sole AM.

We conducted a multi-institutional retrospective study of patients with sole AM and a BT greater than 2 mm. Survival outcomes were compared between two groups: those excised with a peripheral margin of 2 cm, as recommended by the NCCN Guidelines, and those excised with a peripheral margin of 1 cm.

This study included 336 patients (2 cm margin: n = 226; 1 cm margin, n = 110) with a median follow-up period of 43.1 months. In multivariate analyses, a peripheral margin of 1 cm did not negatively affect survival (local recurrence-free survival [LRFS]: hazard ratio [HR] 1.19, P = 0.38; disease-free survival [DFS]: HR 1.05, P = 0.75). Survival after propensity score matching showed no significant differences between the matched groups (each group: n = 103; 5-year LRFS: 70.6 % vs. 59.3 %, P = 0.13; 5-year DFS: 47.4 % vs. 54.0 %, P = 0.63).

A peripheral margin of 1 cm did not negatively influence the prognosis of patients with a sole AM and a BT greater than 2 mm. Narrower surgical margins may be acceptable to minimize morbidity without compromising survival.

The larynx of infants and neonates is occasionally challenging to identify using video laryngoscope. This study aimed to develop an artificial intelligence (AI)-assisted model that can identify the larynx, including the opening vocal cord and the arytenoid portion, on images obtained using a video laryngoscope.

First, 1197 images were extracted by the author from the 653 videos for train and validation data. An AI-assisted model for identifying the larynx was developed using YOLOv8n. Then, 399 images were selected from the additional 63 videos for the test data at every 150 frames.

The sensitivity, specificity, and area under the curve of the AI-assisted model for identifying the larynx were 0.74, 0.99, and 0.91, respectively. The AI model correctly identified the opening vocal cord and arytenoid portion in cases without obstacles. Esophageal misidentification of the larynx and undetected cases of the larynx caused by obstacles were observed.

The AI-assisted model effectively identified the larynx, including the opening vocal cord and the arytenoid portion, during video laryngoscopy and potentially can enhance the safety of tracheal intubation of infants and neonates. However, esophageal misidentification remains critical. Further studies are needed to refine the model and ensure its reliability in clinical practice.

Owing to improvements in anticancer treatment, patients' overall survival has been prolonged, and late complications occur more frequently than previously. We successfully managed a case of ureterorectal-arterial fistula (URAF) and external iliac artery (EIA) stenosis due to endometrial cancer recurrence using an endovascular approach, including arterial embolization and angioplasty. The patient was a 48-year-old woman undergoing chemoradiotherapy for recurrent endometrial cancer following surgery and adjuvant chemotherapy. Tumor involvement and its response to treatment led to the development of URAF and EIA stenosis. Catastrophic bleeding occurred from URAF, and hemostasis was successfully achieved by left internal iliac artery embolization using a mixture of N-butyl cyanoacrylate and ethiodized oil. Nine days after embolization, angioplasty was performed using a stent graft implantation for EIA stenosis. Endovascular treatment is considered a crucial approach for managing both URAF and arterial stenosis, which is surgically untreatable.