Deep learning-based auto-segmentation of paraganglioma for growth
  monitoring
E. M. C. Sijben J. C. Jansen M. de Ridder P. A. N. Bosman T. Alderliesten
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.