Anatomical location-guided deep learning-based genetic cluster identification of pheochromocytomas and paragangliomas from CT images

FARE Award Winner

Authors

• JM Madrigal
• CN Pruitt
• JA Fisher
• BI Graubard
• MH Ward
• RR Jones

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. Genetic testing for PPGLs is expensive and time-consuming. Contrast-enhanced CT (CE-CT) scans of PPGL patients are 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 the 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 and balanced accuracy (BA) of 0.63 ± 0.06 on five-fold cross-validation and outperformed competing methods by 2-29% on accuracy and 3-18% on BA. 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). The proposed method may lead to faster and more widely available PPGLs’ genetic characterization offering PPGLs’ timely personalized management.

Scientific Focus Area: Clinical Research

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