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Full-Text Articles in Medicine and Health Sciences
Calculating Differences In Surrogate Lung Perfusion Measurements Between Smokers And Nonsmokers As A Biomarker To Assess Their Potentials For Developing Chronic Obstructive Pulmonary Disease, A Prakash, A Nowacki, Y K. Liu, J Cisneros-Paz, Girish Nair, E Castillo
Calculating Differences In Surrogate Lung Perfusion Measurements Between Smokers And Nonsmokers As A Biomarker To Assess Their Potentials For Developing Chronic Obstructive Pulmonary Disease, A Prakash, A Nowacki, Y K. Liu, J Cisneros-Paz, Girish Nair, E Castillo
Conference Presentation Abstracts
Purpose: COPD (chronic obstructive pulmonary disease) is a collection of lung diseases that complicates breathing and causes irreversible lung damage. Given that it is a progressive disease, early detection is crucial for managing its symptoms. We recently developed a method for calculating pulmonary blood mass change (PBMC) apparent on paired inhale/exhale non-contrast computed tomography (CT) scans as a quantitative surrogate for lung perfusion. Our long term objective is to develop a PBMC biomarker for predicting COPD progression. As a first step, we hypothesize that PBMC is significantly lower for non-COPD smokers versus non-COPD non-smokers. Methods: CT scans from 77 healthy …
Quantifying Disease Progression In Copd Patients By Forecasting Pft Scores Using Extracted Features From Non-Contrast Ct, A T. Luong, C J. Herrera, E Young, Y K. Liu, A Nowacki, J Cisneros-Paz, Girish Nair, E Castillo
Quantifying Disease Progression In Copd Patients By Forecasting Pft Scores Using Extracted Features From Non-Contrast Ct, A T. Luong, C J. Herrera, E Young, Y K. Liu, A Nowacki, J Cisneros-Paz, Girish Nair, E Castillo
Conference Presentation Abstracts
Purpose: Currently there is no validated method for quantifying risk of disease progression in chronic obstructive pulmonary disease (COPD). We aim to address this by predicting whether a patient will worsen using a machine learning model trained on basic patient information, current pulmonary function values, FEV1 and FVC, and extracted features from a non-contrast CT scan. Disease severity is characterized by these pulmonary function values, and by extension disease progression can be determined by the change in these values between future timepoints. Methods: XGBoost, a popular classification library that utilizes gradient boosted trees, was used to define an ensemble model …