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- Title
An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients.
- Authors
Wallis, David; Soussan, Michaël; Lacroix, Maxime; Akl, Pia; Duboucher, Clément; Buvat, Irène
- Abstract
Purpose: The 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-labelled fluoro-2-deoxyglucose ([18F]FDG) positron emission tomography/computed tomography ([18F]FDG-PET/CT) scans (Philips Gemini TF) from 134 patients were retrospectively analysed. The thorax was automatically located, and 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 these predictions, pathological mediastinal nodes could be identified. A second cohort of 71 patients was then acquired from a different, newer scanner (GE Discovery MI), and the performance of the model on this dataset was tested with and without transfer learning. Results: On the test set from the first scanner, our model achieved a sensitivity of 0.87 (95% confidence intervals [0.74, 0.94]) with 0.41 [0.22, 0.71] false positives/patient. This was comparable to the performance of an expert. Without transfer learning, on the test set from the second scanner, the corresponding results were 0.53 [0.35, 0.70] and 0.24 [0.10, 0.49], respectively. With transfer learning, these metrics were 0.88 [0.73, 0.97] and 0.69 [0.43, 1.04], respectively. Conclusion: Model performance was comparable to that of an expert on data from the same scanner. With transfer learning, the model can be applied to data from a different scanner. To our knowledge it is the first study of its kind to go directly from whole-body [18F]FDG-PET/CT scans to pathological mediastinal lymph node localisation.
- Subjects
LYMPH node cancer; DEEP learning; POSITRON emission tomography computed tomography; CANCER patients; COMPUTED tomography; LYMPH nodes; LYMPHATIC disease diagnosis; COMPUTERS in medicine; CONFIDENCE intervals; LUNG tumors; RETROSPECTIVE studies; DIAGNOSTIC imaging; RADIOPHARMACEUTICALS; POSITRON emission tomography; DEOXY sugars; SENSITIVITY &; specificity (Statistics)
- Publication
European Journal of Nuclear Medicine & Molecular Imaging, 2022, Vol 49, Issue 3, p881
- ISSN
1619-7070
- Publication type
Article
- DOI
10.1007/s00259-021-05513-x