Anatomical Location-Guided Deep Learning-Based Genetic Cluster Identification of Pheochromocytomas and Paragangliomas From CT Images.

Abstract

Pheochromocytomas and paragangliomas (PPGLs) are respectively intra-adrenal and extra-adrenal neuroendocrine tumors whose pathogenesis and progression are greatly regulated by genetics. Identifying PPGL's genetic clusters (SDHx, VHL/EPAS1, kinase signaling, and sporadic) is essential as PPGL's management varies critically on its genotype. But, genetic testing for PPGLs is expensive and time-consuming. Contrast-enhanced CT (CE-CT) scans of PPGL patients are usually acquired at the beginning of patient management for PPGL staging and determining the next therapeutic steps. Given a CE-CT sub-image of the PPGL, this work demonstrates a two-branch vision transformer (PPGL-Transformer) to identify each tumor's genetic cluster. The standard of reference for each tumor included two items: its genetic cluster from clinical testing, and its anatomical location. One branch of our PPGL-Transformer identifies PPGL's anatomic location while the other one characterizes PPGL's genetic type. A supervised contrastive learning strategy was used to train the PPGL-Transformer by optimizing contrastive and classification losses for PPGLs' genetic group and anatomic location. Our method was evaluated on a dataset comprised of 1010 PPGLs extracted from the CE-CT images of 289 patients. PPGL-Transformer achieved an accuracy of 0.63±0.08, balanced accuracy (BA) of 0.63±0.06 and F1-score of 0.46±0.08 on five-fold cross-validation and outperformed competing methods by 2-29% on accuracy, 3-18% on BA and 3-14% on F1-score. The performance for the sporadic cluster was higher on BA (0.68 ± 0.13) while the performance for the SDHx cluster was higher on recall (0.83 ± 0.06) and F1-score (0.74 ± 0.07).