Deep learning-based auto-segmentation of paraganglioma for growth monitoring
<|reference_start|>Deep learning-based auto-segmentation of paraganglioma for growth monitoring: Volume measurement of a paraganglioma (a rare neuroendocrine tumor that typically forms along major blood vessels and nerve pathways in the head and neck region) is crucial for monitoring and modeling tumor growth in the long term. However, in clinical practice, using available tools to do these measurements is time-consuming and suffers from tumor-shape assumptions and observer-to-observer variation. Growth modeling could play a significant role in solving a decades-old dilemma (stemming from uncertainty regarding how the tumor will develop over time). By giving paraganglioma patients treatment, severe symptoms can be prevented. However, treating patients who do not actually need it, comes at the cost of unnecessary possible side effects and complications. Improved measurement techniques could enable growth model studies with a large amount of tumor volume data, possibly giving valuable insights into how these tumors develop over time. Therefore, we propose an automated tumor volume measurement method based on a deep learning segmentation model using no-new-UNnet (nnUNet). We assess the performance of the model based on visual inspection by a senior otorhinolaryngologist and several quantitative metrics by comparing model outputs with manual delineations, including a comparison with variation in manual delineation by multiple observers. Our findings indicate that the automatic method performs (at least) equal to manual delineation. Finally, using the created model, and a linking procedure that we propose to track the tumor over time, we show how additional volume measurements affect the fit of known growth functions.<|reference_end|>
@article{sijben2024deep,
    title={Deep learning-based auto-segmentation of paraganglioma for growth
  monitoring},
    author={E.M.C. Sijben, J.C. Jansen, M. de Ridder, P.A.N. Bosman, T.
  Alderliesten},
    journal={arXiv preprint arXiv:2404.07952},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07952},
    primaryClass={cs.CV cs.LG}
}