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

文献类型： 会议论文

第一作者： Bikash Santra

作者： Bikash Santra 1 ; Abhishek Jha 2 ; Pritam Mukherjee 1 ; Mayank Patel 2 ; Karel Pacak 2 ; Ronald M. Summers 1 ;

作者机构： 1.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA

2.National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA

关键词： Pheochromocytoma;Paraganglioma;Genetic mutations;Transformer;CT images;Deep learning;Radiogenomics;PPGL

会议名称： International workshop on applications of medical artificial intelligence

主办单位：

页码： 62-71

摘要： 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 Fl-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 Fl-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 Fl-score (0.74 ± 0.07).

分类号： tp18-53

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