  Lesion segmentation is a crucial step of the radiomic workflow. Manual
segmentation requires long execution time and is prone to variability,
impairing the realisation of radiomic studies and their robustness. In this
study, a deep-learning automatic segmentation method was applied on computed
tomography images of non-small-cell lung cancer patients. The use of manual vs
automatic segmentation in the performance of survival radiomic models was
assessed, as well. METHODS A total of 899 NSCLC patients were included (2
proprietary: A and B, 1 public datasets: C). Automatic segmentation of lung
lesions was performed by training a previously developed architecture, the
nnU-Net, including 2D, 3D and cascade approaches. The quality of automatic
segmentation was evaluated with DICE coefficient, considering manual contours
as reference. The impact of automatic segmentation on the performance of a
radiomic model for patient survival was explored by extracting radiomic
hand-crafted and deep-learning features from manual and automatic contours of
dataset A, and feeding different machine learning algorithms to classify
survival above/below median. Models' accuracies were assessed and compared.
RESULTS The best agreement between automatic and manual contours with DICE=0.78
+(0.12) was achieved by averaging predictions from 2D and 3D models, and
applying a post-processing technique to extract the maximum connected
component. No statistical differences were observed in the performances of
survival models when using manual or automatic contours, hand-crafted, or deep
features. The best classifier showed an accuracy between 0.65 and 0.78.
CONCLUSION The promising role of nnU-Net for automatic segmentation of lung
lesions was confirmed, dramatically reducing the time-consuming physicians'
workload without impairing the accuracy of survival predictive models based on
radiomics.
