  Pheochromocytomas and Paragangliomas (PPGLs) are rare adrenal and
extra-adrenal tumors which have the potential to metastasize. For the
management of patients with PPGLs, CT is the preferred modality of choice for
precise localization and estimation of their progression. However, due to the
myriad variations in size, morphology, and appearance of the tumors in
different anatomical regions, radiologists are posed with the challenge of
accurate detection of PPGLs. Since clinicians also need to routinely measure
their size and track their changes over time across patient visits, manual
demarcation of PPGLs is quite a time-consuming and cumbersome process. To
ameliorate the manual effort spent for this task, we propose an automated
method to detect PPGLs in CT studies via a proxy segmentation task. As only
weak annotations for PPGLs in the form of prospectively marked 2D bounding
boxes on an axial slice were available, we extended these 2D boxes into weak 3D
annotations and trained a 3D full-resolution nnUNet model to directly segment
PPGLs. We evaluated our approach on a dataset consisting of
chest-abdomen-pelvis CTs of 255 patients with confirmed PPGLs. We obtained a
precision of 70% and sensitivity of 64.1% with our proposed approach when
tested on 53 CT studies. Our findings highlight the promising nature of
detecting PPGLs via segmentation, and furthers the state-of-the-art in this
exciting yet challenging area of rare cancer management.
