Abstract
PurposeThe 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.MethodsWhole-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.ResultsOn 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.ConclusionModel 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.
Highlights
IntroductionThis article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence)
Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer death worldwide, with non–smallcell lung cancer (NSCLC) comprising more than 85% ofThis article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence).Currently, a 18F-labelled fluoro-2-deoxyglucose ([18F] FDG) positron emission tomography/computed tomography ([18F]FDG-PET/CT) scan is acquired, and the nuclear medicine physician/radiologist examine all the slices
All scans were analysed under the supervision of an experienced dual board–certified radiologist (15 years’ experience in thoracic imaging), who used the LIFEx [27] software to mark the positions of mediastinal nodes they would consider positive in routine clinical examination
Summary
This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence). A 18F-labelled fluoro-2-deoxyglucose ([18F] FDG) positron emission tomography/computed tomography ([18F]FDG-PET/CT) scan is acquired, and the nuclear medicine physician/radiologist examine all the slices. The sensitivity and specificity of these lesion-based analyses have been shown to be 0.59 and 0.97, respectively, meaning many nodes remain undetected [4]. European Journal of Nuclear Medicine and Molecular Imaging between observers is limited. Inter-observer agreement, defined by the kappa score (κ), has been shown to range from 0.48 to 0.88, depending on the type of node (agreement was lower for aortopulmonary nodes (κ = 0.48–0.55) but higher for inferior and superior nodes (κ = 0.71–0.88)) [5]. We hypothesise that an artificial intelligence–based system could improve the sensitivity and reproducibility of mediastinal lymph node staging while saving radiologists time
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