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Abstract:
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The phenotypic and genetic heterogeneity have been significantly shown across subjects and subpopulations in many cancers and neuro-related diseases. Characterizing such heterogeneity could help transform our understanding of the etiology of these conditions and enlighten new approaches to urgently needed prevention, diagnosis, treatment, and prognosis. However, most existing statistical methods face major challenges in delineating such heterogeneity at both group and individual levels. The aim of this paper is to provide a novel statistical diseased region detection (SDRD) framework to address these challenges. We also develop an efficient estimation method to estimate unknown parameters in SDRD and delineate individual and group disease maps. Both simulation studies and real data analysis on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) hippocampal surface dataset show that our SDRD not only effectively detects diseased regions in each patient, but also provides a group disease detection analysis of Alzheimer subgroups.
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