Abstract
BACKGROUND: Metabolic positron emission tomography/computed tomography (PET/CT) parameters describing tumour activity contain valuable prognostic information, but to perform the measurements manually leads to both intra- and inter-reader variability and is too time-consuming in clinical practice. The use of modern artificial intelligence-based methods offers new possibilities for automated and objective image analysis of PET/CT data. PURPOSE: We aimed to train a convolutional neural network (CNN) to segment and quantify tumour burden in [(18)F]-fluorodeoxyglucose (FDG) PET/CT images and to evaluate the association between CNN-based measurements and overall survival (OS) in patients with lung cancer. A secondary aim was to make the method available to other researchers. METHODS: A total of 320 consecutive patients referred for FDG PET/CT due to suspected lung cancer were retrospectively selected for this study. Two nuclear medicine specialists manually segmented abnormal FDG uptake in all of the PET/CT studies. One-third of the patients were assigned to a test group. Survival data were collected for this group. The CNN was trained to segment lung tumours and thoracic lymph nodes. Total lesion glycolysis (TLG) was calculated from the CNN-based and manual segmentations. Associations between TLG and OS were investigated using a univariate Cox proportional hazards regression model. RESULTS: The test group comprised 106 patients (median age, 76 years (IQR 61-79); n = 59 female). Both CNN-based TLG (hazard ratio 1.64, 95% confidence interval 1.21-2.21; p = 0.001) and manual TLG (hazard ratio 1.54, 95% confidence interval 1.14-2.07; p = 0.004) estimations were significantly associated with OS. CONCLUSION: Fully automated CNN-based TLG measurements of PET/CT data showed were significantly associated with OS in patients with lung cancer. This type of measurement may be of value for the management of future patients with lung cancer. The CNN is publicly available for research purposes.