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• W4312127612 endingPage "7334" @default.
• W4312127612 startingPage "7334" @default.
• W4312127612 abstract "Radiomics investigates the predictive role of quantitative parameters calculated from radiological images. In oncology, tumour segmentation constitutes a crucial step of the radiomic workflow. Manual segmentation is time-consuming and prone to inter-observer variability. In this study, a state-of-the-art deep-learning network for automatic segmentation (nnU-Net) was applied to computed tomography images of lung tumour patients, and its impact on the performance of survival radiomic models was assessed. In total, 899 patients were included, from two proprietary and one public datasets. Different network architectures (2D, 3D) were trained and tested on different combinations of the datasets. Automatic segmentations were compared to reference manual segmentations performed by physicians using the DICE similarity coefficient. Subsequently, the accuracy of radiomic models for survival classification based on either manual or automatic segmentations were compared, considering both hand-crafted and deep-learning features. The best agreement between automatic and manual contours (DICE = 0.78 ± 0.12) was achieved averaging 2D and 3D predictions and applying customised post-processing. The accuracy of the survival classifier (ranging between 0.65 and 0.78) was not statistically different when using manual versus automatic contours, both with hand-crafted and deep features. These results support the promising role nnU-Net can play in automatic segmentation, accelerating the radiomic workflow without impairing the models' accuracy. Further investigations on different clinical endpoints and populations are encouraged to confirm and generalise these findings." @default.
• W4312127612 created "2023-01-04" @default.
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• W4312127612 date "2022-12-09" @default.
• W4312127612 modified "2023-10-15" @default.
• W4312127612 title "Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models" @default.
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• W4312127612 cites W2903150666 @default.
• W4312127612 cites W2907772920 @default.
• W4312127612 cites W2940487144 @default.
• W4312127612 cites W2954296981 @default.
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• W4312127612 doi "https://doi.org/10.3390/jcm11247334" @default.
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• W4312127612 hasPublicationYear "2022" @default.
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