Safety and Efficacy of Self-Expandable Metallic Stent Placement Using Low Radial Force Stent for Malignant Dysphagia after Radiotherapy.

INTRODUCTION: We aimed to investigate the safety and efficacy of self-expandable metallic stent (SEMS) placement in patients with prior radiotherapy (RT) using the Niti-S stent, which is characterized by low radial force, in comparison to patients without prior RT. METHODS: A consecutive series of 83 patients who were treated by SEMS placement using Niti-S stent for severe malignant esophageal obstruction or fistula were enrolled. The adverse event rates and efficacy were retrospectively compared between patients with/without prior RT before SEMS placement (RT group [n = 32] versus non-RT group [n = 51]). RESULTS: The incidence rate of major adverse events in the RT group was 6.3% and was not significantly different from that in the non-RT group (5.9%, p = 0.95). Among the RT group, 84.4% were able to resume oral intake within a median of 2 days. Among the patients with fistula, 78.6% could resume oral intake and survive for 73 days after SEMS placement. Cox proportional hazard regression analysis identified significant factors affecting overall survival to be prior RT (hazard ratio [HR]: 1.96), low performance status (HR: 3.87), and subsequent anticancer treatment after SEMS placement (HR: 0.41). However, compared to the non-RT group, the RT group had received longer duration of anticancer treatment before SEMS placement and a lower rate of subsequent anticancer treatment after SEMS placement. CONCLUSIONS: With the Niti-S stent, the incidence of major adverse events was sufficiently low even for patients after RT. SEMS with low radial force would be an effective palliative treatment option for patients, regardless of prior RT.

Artificial intelligence-based detection of pharyngeal cancer using convolutional neural networks.

OBJECTIVES: The prognosis for pharyngeal cancer is relatively poor. It is usually diagnosed in an advanced stage. Although the recent development of narrow-band imaging (NBI) and increased awareness among endoscopists have enabled detection of superficial pharyngeal cancer, these techniques are still not prevalent worldwide. Nevertheless, artificial intelligence (AI)-based deep learning has led to significant advancements in various medical fields. Here, we demonstrate the diagnostic ability of AI-based detection of pharyngeal cancer from endoscopic images in esophagogastroduodenoscopy. METHODS: We retrospectively collected 5403 training images of pharyngeal cancer from 202 superficial cancers and 45 advanced cancers from the Cancer Institute Hospital, Tokyo, Japan. Using these images, we developed an AI-based diagnostic system with convolutional neural networks. We prepared 1912 validation images from 35 patients with 40 pharyngeal cancers and 40 patients without pharyngeal cancer to evaluate our system. RESULTS: Our AI-based diagnostic system correctly detected all pharyngeal cancer lesions (40/40) in the patients with cancer, including three small lesions smaller than 10 mm. For each image, the AI-based system correctly detected pharyngeal cancers in images obtained via NBI with a sensitivity of 85.6%, much higher sensitivity than that for images obtained via white light imaging (70.1%). The novel diagnostic system took only 28 s to analyze 1912 validation images. CONCLUSIONS: The novel AI-based diagnostic system detected pharyngeal cancer with high sensitivity. It could facilitate early detection, thereby leading to better prognosis and quality of life for patients with pharyngeal cancers in the near future.

[Endoscopic Diagnosis Using Artificial Intelligence].

Image recognition using artificial intelligence(AI)has developed dramatically with innovative technologies such as machine learning and deep learning. Currently, it is considered that AI has exceeded human ability in image recognition. In the field of endoscopic diagnosis, development of computer-aided diagnosis(CAD)systems using AI is progressing. The CAD is expected to help endoscopists improve detection and characterization of polyp, cancer, and inflamation in all digestive area. Some CAD systemes showing ability better than endoscopists have been reported. It may be well applicable to daily clinical practice as real time endoscopic diagnosis in the near future.