An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients

International audienceThe identification of pathological mediastinal lymph nodes is an important step in the staging of lung cancer, with the presence of metastases significantly affecting survival rates. Nodes are currently identified by a physician, but this process is time-consuming and prone to errors. In this paper we investigate the use of artificial intelligence-based methods to increase the accuracy and consistency of this process. Methods: Whole-body [18F]FDG-PET/CT scans (Philips Gemini TF) from 134 patients were retrospectively analysed. The thorax was automatically located, then slices were fed into a U-Net to identify candidate regions. These regions were split into overlapping 3D cubes, which were individually predicted as positive or negative using a 3D CNN. From thes