Upper Aero-digestive Tract (UADT) Neoplasm

This is a prospective observational clinical study designed to evaluate the performance of artificial intelligence (AI) algorithms applied to upper aerodigestive tract (UADT) video-endoscopy. The study assesses three main tasks: lesion detection (localization), classification (benign vs malignant), and segmentation of tumor margins. AI algorithms will be applied to endoscopic video data acquired during routine clinical practice without influencing clinical decision-making. The system will process images in real time and store data for subsequent analysis. AI outputs will be compared with physician assessment and reference standard histopathology to evaluate diagnostic performance.

Squamous cell carcinomas of the upper aero-digestive tract (SCC-UADT) represent the seventh cause of cancer and affect approximately 600,000 patients per year worldwide. The majority of UADT cancers are diagnosed at an advanced stage (70.3% at stage III and IV) and less than 60% of these patients are free of the disease at 3 years, despite aggressive multimodal local treatment by surgery and /or radiochemotherapy. The average progression-free survival (PFS) at 2 years varies between 45 and 60% depending on the studies. Tumor recurrence is most often incurable. To our knowledge, no study has demonstrated the benefit of early evaluation of the rate of decrease in ctDNA at 1 month after the end of radiotherapy alone or associated with concomitant treatment, as a predictive factor of PFS in UADT squamous cell carcinomas regardless of their HPV status. The main objective of this study is to evaluate the value of measuring the quantity of circulating tumor DNA (ctDNA) at 1 month post-treatment as a predictive factor for PFS at 24 months.