Deep Learning Approach to Automatize TMTV Calculations Regardless of Segmentation Methodology for Major FDG-Avid Lymphomas.
Fiche publication
Date publication
février 2022
Journal
Diagnostics (Basel, Switzerland)
Auteurs
Membres identifiés du Cancéropôle Est :
Dr CASASNOVAS Olivier, Dr ROSSI Cédric
Tous les auteurs :
Revailler W, Cottereau AS, Rossi C, Noyelle R, Trouillard T, Morschhauser F, Casasnovas O, Thieblemont C, Gouill SL, André M, Ghesquieres H, Ricci R, Meignan M, Kanoun S
Lien Pubmed
Résumé
The total metabolic tumor volume (TMTV) is a new prognostic factor in lymphomas that could benefit from automation with deep learning convolutional neural networks (CNN). Manual TMTV segmentations of 1218 baseline 18FDG-PET/CT have been used for training. A 3D V-NET model has been trained to generate segmentations with soft dice loss. Ground truth segmentation has been generated using a combination of different thresholds (TMTVprob), applied to the manual region of interest (Otsu, relative 41% and SUV 2.5 and 4 cutoffs). In total, 407 and 405 PET/CT were used for test and validation datasets, respectively. The training was completed in 93 h. In comparison with the TMTVprob, mean dice reached 0.84 in the training set, 0.84 in the validation set and 0.76 in the test set. The median dice scores for each TMTV methodology were 0.77, 0.70 and 0.90 for 41%, 2.5 and 4 cutoff, respectively. Differences in the median TMTV between manual and predicted TMTV were 32, 147 and 5 mL. Spearman's correlations between manual and predicted TMTV were 0.92, 0.95 and 0.98. This generic deep learning model to compute TMTV in lymphomas can drastically reduce computation time of TMTV.
Mots clés
convolutional neural network, deep learning, lymphoma, total metabolic tumor volume
Référence
Diagnostics (Basel). 2022 Feb 6;12(2):