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. 2021 May;48(5):1362-1370.
doi: 10.1007/s00259-020-05080-7. Epub 2020 Oct 24.

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

Paul Blanc-Durand  1   2   3   4   5 , Simon Jégou  6 , Salim Kanoun  7   8 , Alina Berriolo-Riedinger  7   9 , Caroline Bodet-Milin  7   10   11 , Françoise Kraeber-Bodéré  7   10   11 , Thomas Carlier  7   10   11 , Steven Le Gouill  7   12 , René-Olivier Casasnovas  7   13 , Michel Meignan  7 , Emmanuel Itti  14   7   15
Affiliations

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

Paul Blanc-Durand et al. Eur J Nucl Med Mol Imaging. 2021 May.

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.

Trial registration: ClinicalTrials.gov NCT00498043 NCT01659099.

Keywords: Convolutional neural network; Deep learning; Lymphoma; Positron emission tomography; Segmentation; Total metabolic tumour volume; U-net.

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References

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