2026
article
OpenAlex
Pavel Nikulin, Sebastian Hoberück, F Hofheinz, A Hüttmann et autres
Ziel/Aim: Delineation of all tumor lesions in PET images of lymphoma patients is required for extraction of prognostic biomarkers. Since this process is time-consuming and non-trivial with classical approaches, the deep-learning-based delineation represents a promising alternative. While the majority of deep-learning models …
de
(code pays fourni par la source)
2026
article
OpenAlex
C-A Voltin, Dirk Hasenclever, U Dührsen, A Hüttmann et autres
Ziel/Aim: Positron emission tomography (PET) with 18 F-fluorodeoxyglucose ( 18 F-FDG) plays an important role in response evaluation of lymphoma, which is commonly based on the Deauville scale. Approaches using tumor-to-liver standardized uptake value (SUV) ratios may yield more objective and reproducible …
de, us
(code pays fourni par la source)
2026
article
OpenAlex
Frank Hofheinz, Pavel Nikulin, Jens Maus, A Hüttmann et autres
Ziel/Aim: It was shown in several investigations that the total metabolic tumor volume (TMTV), determined in [F-18]FDG PET, has a high prognostic value in patients with diffuse large B-cell lymphoma (DLBCL). However, in most of these studies, small lesions (<3ml) were excluded …
de
(code pays fourni par la source)
Accès ouvert
2025
software
OpenAlex
Pavel Nikulin, Sebastian Hoberück, Ivayla Apostolova, Jens Maus et autres
Collection of neural network models for metabolic tumor volume delineation in (Non-Hodgkin) lymphoma patients in FDG-PET/CT images. Intended to use within nnU-Net deep learning framework. Trained with a total of 1192 [18F]FDG-PET/CT scans from 716 patients with Non-Hodgkin lymphoma participating in the …
de
(code pays fourni par la source)