Area of research
Pulmonary and Respiratory Medicine · Radiology, Nuclear Medicine and Imaging
Research interest
Research interests include Lung Cancer Diagnosis and Treatment, Lung Cancer Treatments and Mutations, Radiomics and Machine Learning in Medical Imaging, and Global Cancer Incidence and Screening.
Impact of Race and Acculturation on Lung Cancer Screening Eligibility: A Multi-Center Lung Cancer Cohort Study.
Design and Creation of a Racially Diverse Lung Cancer Registry with Detailed Genomic and Environmental Annotation.
Monitoring the Harms of Lung Cancer Screening: Why the Nonmalignant Resection Rate Is Our Best Bet.
Characterizing the Design of and Emerging Evidence for Health Care Organization-Based Lung Cancer Screening Interventions: A Systematic Review.
Components Necessary for High-Quality Lung Cancer Screening
Gaps in care across the cancer screening continuum for cervical, colorectal, and lung cancers.
Supplementary Table from Evaluating and Improving Cancer Screening Process Quality in a Multilevel Context: The PROSPR II Consortium Design and Research Agenda
Data from Estimating Cancer Screening Sensitivity and Specificity Using Healthcare Utilization Data: Defining the Accuracy Assessment Interval
Supplementary Table from Evaluating and Improving Cancer Screening Process Quality in a Multilevel Context: The PROSPR II Consortium Design and Research Agenda
Supplementary Table 1 from Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care
Supplementary Table from Evaluating and Improving Cancer Screening Process Quality in a Multilevel Context: The PROSPR II Consortium Design and Research Agenda
Supplementary Table 3 from Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care
Supplementary Figure from Estimating Cancer Screening Sensitivity and Specificity Using Healthcare Utilization Data: Defining the Accuracy Assessment Interval
Supplementary Figure 3 from Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care
Data from Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care
Supplementary Table from Evaluating and Improving Cancer Screening Process Quality in a Multilevel Context: The PROSPR II Consortium Design and Research Agenda
Supplementary Table from Evaluating and Improving Cancer Screening Process Quality in a Multilevel Context: The PROSPR II Consortium Design and Research Agenda
Data from Evaluating and Improving Cancer Screening Process Quality in a Multilevel Context: The PROSPR II Consortium Design and Research Agenda
Supplementary Table from Evaluating and Improving Cancer Screening Process Quality in a Multilevel Context: The PROSPR II Consortium Design and Research Agenda
Supplementary Table 2 from Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care
Supplementary Figure 1 from Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care
Supplementary Table from Evaluating and Improving Cancer Screening Process Quality in a Multilevel Context: The PROSPR II Consortium Design and Research Agenda
Supplementary Figure 2 from Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care
Clinical Validation of a Cell-Free DNA Fragmentome Assay for Augmentation of Lung Cancer Early Detection.
Rates of Downstream Procedures and Complications Associated With Lung Cancer Screening in Routine Clinical Practice : A Retrospective Cohort Study.
A Framework for Integrating Telehealth Equitably across the cancer care continuum
A Framework for Integrating Telehealth Equitably across the cancer care continuum.
Innovations in Early Lung Cancer Detection: Tracing the Evolution and Advancements in Screening.
Clinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules.
University of Pennsylvania Telehealth Research Center of Excellence.