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- Title
Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network.
- Authors
Blanc-Durand, Paul; Jégou, Simon; Kanoun, Salim; Berriolo-Riedinger, Alina; Bodet-Milin, Caroline; Kraeber-Bodéré, Françoise; Carlier, Thomas; Le Gouill, Steven; Casasnovas, René-Olivier; Meignan, Michel; Itti, Emmanuel
- Abstract
Purpose: Lymphoma lesion detection and segmentation on whole-body FDG-PET/CT are a challenging task because of the diversity of involved nodes, organs or physiological uptakes. We sought to investigate the performances of a three-dimensional (3D) convolutional neural network (CNN) to automatically segment total metabolic tumour volume (TMTV) in large datasets of patients with diffuse large B cell lymphoma (DLBCL). Methods: The dataset contained pre-therapy FDG-PET/CT from 733 DLBCL patients of 2 prospective LYmphoma Study Association (LYSA) trials. The first cohort (n = 639) was used for training using a 5-fold cross validation scheme. The second cohort (n = 94) was used for external validation of TMTV predictions. Ground truth masks were manually obtained after a 41% SUVmax adaptive thresholding of lymphoma lesions. A 3D U-net architecture with 2 input channels for PET and CT was trained on patches randomly sampled within PET/CTs with a summed cross entropy and Dice similarity coefficient (DSC) loss. Segmentation performance was assessed by the DSC and Jaccard coefficients. Finally, TMTV predictions were validated on the second independent cohort. Results: Mean DSC and Jaccard coefficients (± standard deviation) in the validations set were 0.73 ± 0.20 and 0.68 ± 0.21, respectively. An underestimation of mean TMTV by − 12 mL (2.8%) ± 263 was found in the validation sets of the first cohort (P = 0.27). In the second cohort, an underestimation of mean TMTV by − 116 mL (20.8%) ± 425 was statistically significant (P = 0.01). Conclusion: Our CNN is a promising tool for automatic detection and segmentation of lymphoma lesions, despite slight underestimation of TMTV. The fully automatic and open-source features of this CNN will allow to increase both dissemination in routine practice and reproducibility of TMTV assessment in lymphoma patients.
- Subjects
CONVOLUTIONAL neural networks; B cells; LYMPHOMAS; EOSINOPHILIC granuloma; TUMORS; POSITRON emission tomography computed tomography
- Publication
European Journal of Nuclear Medicine & Molecular Imaging, 2021, Vol 48, Issue 5, p1362
- ISSN
1619-7070
- Publication type
Article
- DOI
10.1007/s00259-020-05080-7