Development and evaluation of two open-source nnU-Net models for automatic segmentation of lung tumors on PET and CT images with and without respiratory motion compensation.

Autor: Carles M; La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infra-structures (ICTS), Valencia, Spain. montserrat_carles@iislafe.es., Kuhn D; Department of Radiation Oncology, Division of Medical Physics, University Medical Center Freiburg, Faculty of Medicine, Freiburg, Germany.; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany., Fechter T; Department of Radiation Oncology, Division of Medical Physics, University Medical Center Freiburg, Faculty of Medicine, Freiburg, Germany.; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany., Baltas D; Department of Radiation Oncology, Division of Medical Physics, University Medical Center Freiburg, Faculty of Medicine, Freiburg, Germany.; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany., Mix M; Department of Nuclear Medicine, Faculty of Medicine, University Medical Center Freiburg, Freiburg, Germany., Nestle U; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany.; Department of Radiation Oncology, Faculty of Medicine, University Medical Center Freiburg, Freiburg, Germany.; Department of Radiation Oncology, Kliniken Maria Hilf GmbH Moenchengladbach, Moechengladbach, Germany., Grosu AL; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany.; Department of Radiation Oncology, Faculty of Medicine, University Medical Center Freiburg, Freiburg, Germany., Martí-Bonmatí L; La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infra-structures (ICTS), Valencia, Spain., Radicioni G; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany.; Department of Radiation Oncology, Faculty of Medicine, University Medical Center Freiburg, Freiburg, Germany., Gkika E; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany.; Department of Radiation Oncology, Faculty of Medicine, University Medical Center Freiburg, Freiburg, Germany.
Jazyk: angličtina
Zdroj: European radiology [Eur Radiol] 2024 Oct; Vol. 34 (10), pp. 6701-6711. Date of Electronic Publication: 2024 Apr 25.
DOI: 10.1007/s00330-024-10751-2
Abstrakt: Objectives: In lung cancer, one of the main limitations for the optimal integration of the biological and anatomical information derived from Positron Emission Tomography (PET) and Computed Tomography (CT) is the time and expertise required for the evaluation of the different respiratory phases. In this study, we present two open-source models able to automatically segment lung tumors on PET and CT, with and without motion compensation.
Materials and Methods: This study involved time-bin gated (4D) and non-gated (3D) PET/CT images from two prospective lung cancer cohorts (Trials 108237 and 108472) and one retrospective. For model construction, the ground truth (GT) was defined by consensus of two experts, and the nnU-Net with 5-fold cross-validation was applied to 560 4D-images for PET and 100 3D-images for CT. The test sets included 270 4D- images and 19 3D-images for PET and 80 4D-images and 27 3D-images for CT, recruited at 10 different centres.
Results: In the performance evaluation with the multicentre test sets, the Dice Similarity Coefficients (DSC) obtained for our PET model were DSC(4D-PET) = 0.74 ± 0.06, improving 19% relative to the DSC between experts and DSC(3D-PET) = 0.82 ± 0.11. The performance for CT was DSC(4D-CT) = 0.61 ± 0.28 and DSC(3D-CT) = 0.63 ± 0.34, improving 4% and 15% relative to DSC between experts.
Conclusions: Performance evaluation demonstrated that the automatic segmentation models have the potential to achieve accuracy comparable to manual segmentation and thus hold promise for clinical application. The resulting models can be freely downloaded and employed to support the integration of 3D- or 4D- PET/CT and to facilitate the evaluation of its impact on lung cancer clinical practice.
Clinical Relevance Statement: We provide two open-source nnU-Net models for the automatic segmentation of lung tumors on PET/CT to facilitate the optimal integration of biological and anatomical information in clinical practice. The models have superior performance compared to the variability observed in manual segmentations by the different experts for images with and without motion compensation, allowing to take advantage in the clinical practice of the more accurate and robust 4D-quantification.
Key Points: Lung tumor segmentation on PET/CT imaging is limited by respiratory motion and manual delineation is time consuming and suffer from inter- and intra-variability. Our segmentation models had superior performance compared to the manual segmentations by different experts. Automating PET image segmentation allows for easier clinical implementation of biological information.
(© 2024. The Author(s).)
Databáze: MEDLINE
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