Development of machine learning models for the prediction of long-term feeding tube dependence after oral and oropharyngeal cancer surgery.

Purpose: To predict the necessity of enteral nutrition at 28 days after surgery in patients undergoing major head and neck oncologic procedures for oral and oropharyngeal cancers.

Material And Methods: Data from 193 patients with oral cavity and oropharyngeal squamous cell carcinoma were retrospectively collected at two tertiary referral centers to train (n = 135) and validate (n = 58) six supervised machine learning (ML) models for binary prediction employing 29 clinical variables available pre-operatively.

Results: The accuracy of the six ML models ranged between 0.

Videomics of the Upper Aero-Digestive Tract Cancer: Deep Learning Applied to White Light and Narrow Band Imaging for Automatic Segmentation of Endoscopic Images.

Introduction: Narrow Band Imaging (NBI) is an endoscopic visualization technique useful for upper aero-digestive tract (UADT) cancer detection and margins evaluation. However, NBI analysis is strongly operator-dependent and requires high expertise, thus limiting its wider implementation. Recently, artificial intelligence (AI) has demonstrated potential for applications in UADT videoendoscopy.

Comprehensive Evaluation of Vocal Outcomes and Quality of Life after Total Laryngectomy and Voice Restoration with J-Flap and Tracheoesophageal Puncture.

Background: Tracheoesophageal puncture with a voice prosthesis is the gold standard for speech rehabilitation in patients that receive a laryngopharyngectomy. However, a novel surgical technique, using a tubularized anterolateral tight flap, named "J-flap," has been demonstrated to produce adequate voice restoration. We aimed to compare the outcomes and the quality of life of patients who underwent voice rehabilitation with both techniques.