AISTAT LYMPHOMA combines a pre-trained U-Net deep learning model with a statistical contour-based framework, enabling semi-automated segmentation that is both interpretable and clinically actionable. Unlike standard pixel-based methods, it provides contour-level outputs suitable for radiotherapy (RT) including intensity modulated (IMRT) and includes a traffic light-style uncertainty measure, supporting more informed clinical decisions.The tool has already been trained on publicly available AUTOPET dataset and currently being tested on 1,400 non-small cell lung cancer and 60 lymphoma patients. In this GKEF project we will focus exclusively on H&N cancers, using both the publicly available HECKTOR dataset and a real-world set of 200 patients from NHSGGC, including radiotherapy plans to provide a demonstration of clinical utility on real-world tumours that will in turn provide a platform for further investment and development.Video showing how contours are drawn on the PET-CT imagesAssociated Probabilities of representative lesions

AISTAT H&N integrates a pre-trained U-Net deep learning model with a statistical contour-based framework to deliver semi-automated, interpretable segmentation tailored for Head & Neck cancer radiotherapy planning. Unlike conventional voxel-level approaches, it produces clinically actionable contour-level outputs compatible with IMRT and VMAT workflows, enabling consistent, reproducible delineations across complex anatomical regions.The tool incorporates built-in uncertainty quantification, allowing clinicians to assess segmentation reliability and make confident adjustments during treatment planning.Building on prior validation in lung and lymphoma datasets, this product focuses on H&N cancers, using both the publicly available HECKTOR dataset and a real-world cohort of 200 patients from NHSGGC. The goal is to demonstrate clinical utility in authentic radiotherapy environments — providing a robust foundation for future commercial development and investment.