Biomarker Driven Studies
Biomarker Driven Studies
Matching Patients with Therapy. Biomarkers can be used as targets to devise treatment strategies that exploit current understanding of the biological mechanisms of the disease.
Wang Y, et. al, A functional model for classifying metastatic lesions integrating scans and biomarkers. Statistical Methods in Medical Research 29.1 (2020): 137-150.
Huang M & Hobbs BP. Estimating mean local posterior predictive benefit for biomarker-guided treatment strategies. Statistical Methods in Medical Research 28.9 (2019): 2820-2833.
Ma J, Stingo CF, & Hobbs BP. Bayesian personalized treatment selection strategies that integrate predictive with prognostic determinants. Biometrical Journal 61.4 (2019): 902-917.
Xiao L, et. al. Spatial Bayesian modeling of GLCM with application to malignantlesion characterization. Journal of Applied Statistics 46:2 (2019): 230-246.
Wang Y, et. al. An Efficient Nonparametric Estimate for Spatially Correlated Functional Data. Statistics in Biosciences 11 (2019): 162-183.
Ma J, Hobbs BP, & Stingo CF. Integrating genomic signatures for treatment selection with Bayesian predictive failure time models. Statistical Methods in Medical Research 27.7 (2018): 2093-2113.
Wen Y, et. al. Development and Validation of a Predictive Radiomics Model for Clinical Outcomes in Stage I Non-small Cell Lung Cancer. Radiation Oncology 102.4 (2018): 1090-1097.
Koay EJ, et. al. A Visually Apparent and Quantifiable CT Imaging Feature Identifies Biophysical Subtypes of Pancreatic Ductal Adenocarcinoma. Clinical Cancer Research 24.23 (2018): 5883-5894.
Chen N, Chaan N, & Hobbs BP. Bayesian classifiers of solid lesions with dynamic CT: Integrating enhancement density with washout density and delay interval. IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
Shoemaker K, et. al. Tree-based Methods for Characterizing Tumor Density Heterogeneity. Biocomputing (2018): 216-227.
Ma J, Stingo CF, & Hobbs BP. Bayesian Predictive Modeling for Genomic Based Personalized Treatment Selection. Biometrics 72.2 (2016): 575-583.
Azadeb, et. al. Integrative Bayesian analysis of neuroimaging-genetic data with application to cocaine dependence. NeuroImage 125 (2016): 813-824.
Ma J, Hobbs BP, & Stingo CF. Statistical Methods for Establishing Personalized Treatment Rules in Oncology. BioMed Research International (2015).
Wang Y, et. al. Predictive classification of correlated targets with application to detection of metastatic cancer using functional CT imaging. Biometrics 71.3 (2015): 792-802.
Tang C, Hobbs BP, Amer A, et. al. Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer. Scientific Report 8, 1922 (2018).
A BAYESIAN NONPARAMETRIC APPROACH FOR CANCER RADIOMICS: ELUCIDATING TEXTURAL PATTERN HETEROGENEITY OF SOLID LESIONS. COMING SOON…