||1R01CA249096-01A1 Interpret this number
||University Of Michigan At Ann Arbor
||New Statistical Methods for Modelling Cancer Outcomes
Lung cancer is one of the most common causes of mortality worldwide. Radiomic features have been shown to
provide prognostic values in predicting lung cancer outcomes. Quantitative imaging features, often in
dauntingly large numbers, are extracted from tumor regions. However, not all these extracted features are
useful for tumor characterization, and feature selection is key for best performance. We plan to develop
feasible statistical methods to select relevant features and conduct feature learning, i.e. discovery of
representations needed for feature detection from the raw data.
On the molecular level, expression and genetic variation of some known genes, such as KDM4 genes, have
been linked to lung cancer prognosis, though little is known about epigenetic modifications' roles. Even fewer
studies have investigated the impact of the interplay of DNA methylation and coexisting chronic obstructive
pulmonary disease (COPD; a major clinical risk factor) on lung cancer risks. Statistically, drawing inference
when the predictors (the clinical indicators and the methylation sites) outnumber the sample size in regression
settings, e.g. generalized linear models, Cox proportional hazards models and censored quantile regression
models, is very challenging. We plan to establish a new framework to draw inferences based on these
Growing evidence has suggested that cancer can be better understood through mutated or dysregulated
pathways or networks rather than individual DNA mutations and mechanism of lung cancer involves the
interplay of the cellular heterogeneity, the myriad of dysfunctional molecular and genetic networks. We plan to
develop new models to analyze those large scale network/pathway data and investigate how their dynamic
network structures can be predicted based on DNA mutations.
Leveraging the rich Boston Lung Cancer Survival Cohort database with 11,164 lung cancer cases, we expect
that our new statistical methods will help identify novel biomarkers linked to lung cancer. Our promising
preliminary results indicate the feasibility of the proposed work, which provides a solid radiomic and molecular
basis for prediction of lung cancer outcomes. Core methods will be distributed in open-source, freely available
software, naturally leading to implementable procedures for researchers and practitioners.
Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes.
, Sun Y.
, Morris E.L.
, He X.
, Li Y.
, Pan Z.
, Han P.
, Kang J.
, Sjoding M.W.
, Li Y.
PloS one, 2021; 16(10), p. e0258278.
Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach.
, Li Y.
Journal of machine learning research : JMLR, 2021; 22, .