Deep learning-based automatic delineation of anal cancer gross tumour volume: a multimodality comparison of CT, PET and MRI.

Autor: Groendahl AR; Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway., Moe YM; Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway., Kaushal CK; Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway., Huynh BN; Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway., Rusten E; Department of Medical Physics, Oslo University Hospital, Oslo, Norway., Tomic O; Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway., Hernes E; Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway., Hanekamp B; Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway., Undseth C; Department of Oncology, Oslo University Hospital, Oslo, Norway., Guren MG; Department of Oncology, Oslo University Hospital, Oslo, Norway.; Division of Cancer Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway., Malinen E; Department of Medical Physics, Oslo University Hospital, Oslo, Norway.; Department of Physics, University of Oslo, Oslo, Norway., Futsaether CM; Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.
Jazyk: angličtina
Zdroj: Acta oncologica (Stockholm, Sweden) [Acta Oncol] 2022 Jan; Vol. 61 (1), pp. 89-96. Date of Electronic Publication: 2021 Nov 16.
DOI: 10.1080/0284186X.2021.1994645
Abstrakt: Background: Accurate target volume delineation is a prerequisite for high-precision radiotherapy. However, manual delineation is resource-demanding and prone to interobserver variation. An automatic delineation approach could potentially save time and increase delineation consistency. In this study, the applicability of deep learning for fully automatic delineation of the gross tumour volume (GTV) in patients with anal squamous cell carcinoma (ASCC) was evaluated for the first time. An extensive comparison of the effects single modality and multimodality combinations of computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) have on automatic delineation quality was conducted.
Material and Methods: 18F-fluorodeoxyglucose PET/CT and contrast-enhanced CT (ceCT) images were collected for 86 patients with ASCC. A subset of 36 patients also underwent a study-specific 3T MRI examination including T2- and diffusion-weighted imaging. The resulting two datasets were analysed separately. A two-dimensional U-Net convolutional neural network (CNN) was trained to delineate the GTV in axial image slices based on single or multimodality image input. Manual GTV delineations constituted the ground truth for CNN model training and evaluation. Models were evaluated using the Dice similarity coefficient (Dice) and surface distance metrics computed from five-fold cross-validation.
Results: CNN-generated automatic delineations demonstrated good agreement with the ground truth, resulting in mean Dice scores of 0.65-0.76 and 0.74-0.83 for the 86 and 36-patient datasets, respectively. For both datasets, the highest mean Dice scores were obtained using a multimodal combination of PET and ceCT (0.76-0.83). However, models based on single modality ceCT performed comparably well (0.74-0.81). T2W-only models performed acceptably but were somewhat inferior to the PET/ceCT and ceCT-based models.
Conclusion: CNNs provided high-quality automatic GTV delineations for both single and multimodality image input, indicating that deep learning may prove a versatile tool for target volume delineation in future patients with ASCC.
Databáze: MEDLINE
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