A convolutional neural network for fully automated total metabolic tumor volume delineation in patients with aggressive Non-Hodgkin lymphoma
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PURPOSE: The [18F]FDG-PET-derived total metabolic tumor volume (TMTV) has a high prognostic value in patients with Hodgkin and Non-Hodgkin lymphoma. However, in order to enable TMTV as a biomarker for clinical use, an accurate and fast method of tumor delineation in lymphoma patients is needed. Deep-learning-based methods have shown promising results in this field and offer distinct advantages over classical approaches. Therefore, the goal of this work was to train a convolutional neural network (CNN) for delineation of all lymphoma lesions regardless of their size and uptake characteristics while performing the optimal contouring of each individual lesion. METHODS: A neural network was trained with the nnU-Net software package. A total of 1192 [18F]FDG-PET/CT scans from 716 patients with Non-Hodgkin lymphoma participating in the PETAL trial comprised the main dataset which was used for training. The ground truth delineation included all lesions that were clinically considered as lymphoma manifestations by an experienced observer and was developed iteratively with the assistance of intermediate CNN models. Performance of the trained network was assessed in the main dataset via 5-fold cross-validation as well as in external benchmark dataset (N = 60 scans). RESULTS: Comparing the manual and automated delineations in the main (external) dataset, the aggregated Dice coefficient reached 0.895 (0.715) and the corresponding TMTVs were highly correlated with R2 = 0.974 (0.767). The main (external) dataset contained a total of 8971 (713) manually delineated lesions, the detection sensitivity of which was 71.2% (77.6%) with the positive predictive value of 84.1% (63.1%). Univariate Cox regression analysis in the main dataset revealed both manually and automatically derived TMTVs as highly prognostic factors for progression-free survival with very similar hazard ratios (HR = 3.5; p < 0.001 and HR = 3.7; p < 0.001, respectively). CONCLUSION: In this study we presented a CNN model capable of accurate TMTV delineation in [18F]FDG-PET/CT images of lymphoma patients. It is trained to delineate all tumor lesions while accounting for typical caveats inherent in this task. The developed neural network allows for substantial acceleration of quantitative analysis of lymphoma imaging data and has the potential for supervised clinical use.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- A convolutional neural network for fully automated total metabolic tumor volume delineation in patients with aggressive Non-Hodgkin lymphoma
- Date Crossref
- 24/03/2026
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Helmholtz-Zentrum Dresden-Rossendorf pays non établi dans la noticeStructure de recherche
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University Hospital Carl Gustav Carus Department of Nuclear Medicine pays non établi dans la noticeÉtablissement de santé
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Technische Universität Dresden pays non établi dans la noticeUniversité ou école supérieure
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Universität Hamburg pays non établi dans la noticeUniversité ou école supérieure
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University Medical Center Hamburg-Eppendorf pays non établi dans la noticeÉtablissement de santé
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University of Duisburg-Essen pays non établi dans la noticeUniversité ou école supérieure
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Nationales Centrum für Tumorerkrankungen Dresden pays non établi dans la noticeÉtablissement de santé
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Deutsches Konsortium für Translationale Krebsforschung pays non établi dans la noticeStructure de recherche
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German Cancer Consortium (DKTK) Dresden pays non établi dans la noticeStructure de recherche
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Institute of Radiopharmaceutical Cancer Research Department of Positron Emission Tomography pays non établi dans la noticeStructure de recherche
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University Hospital Hamburg-Eppendorf Department of Diagnostic and Interventional Radiology and Nuclear Medicine pays non établi dans la noticeUniversité ou école supérieure
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University Hospital Essen Department of Hematology pays non établi dans la noticeUniversité ou école supérieure
Helmholtz-Zentrum Dresden-Rossendorf, Department of Nuclear Medicine — University Hospital Carl Gustav Carus et Technische Universität Dresden, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.