Mr. Ahmed A. Rashid Profile

Ahmed A. Rashid

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SPIE Journal Paper | 19 April 2021

Radiomics-based assessment of idiopathic pulmonary fibrosis is associated with genetic mutations and patient survival

Jorie Budzikowski, Joseph Foy, Ahmed Rashid, Jonathan Chung, Imre Noth, Samuel Armato

JMI, Vol. 8, Issue 03, 031903, (April 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.3.031903

KEYWORDS: Genetics, Lung, Computed tomography, Pulmonary fibrosis, Feature extraction, Fractal analysis, Genomics, Databases, Data modeling, Statistical analysis

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Purpose: The purpose of our study was to combine differences in radiomic features extracted from lung regions in the computed tomography (CT) scans of patients diagnosed with idiopathic pulmonary fibrosis (IPF) to identify associations with genetic variations and patient survival. Approach: A database of CT scans and genomic data from 169 patients diagnosed with IPF was collected retrospectively. Six region-of-interest pairs (three per lung, positioned posteriorly, anteriorly, and laterally) were placed in each of three axial CT sections for each patient. Thirty-one features were used in logistic regression to classify patients’ genetic mutation status; classification performance was evaluated through the area under the receiver operating characteristic (ROC) curve [average area under the ROC curve (AUC)]. Kaplan–Meier (KM) survival curve models quantified the ability of each feature to differentiate between survival curves based on feature-specific thresholds. Results: Nine first-order texture features and one fractal feature were correlated with TOLLIP-1 (rs4963062) mutations (AUC: 0.54 to 0.74), and five Laws’ filter features were correlated with TOLLIP-2 (rs5743905) mutations (AUC: 0.53 to 0.70). None of the features analyzed were found to be correlated with MUC5B mutations. First-order and fractal features demonstrated the greatest discrimination between KM curves. Conclusions: A radiomics approach for the correlation of patient genetic mutations with image texture features has potential as a biomarker. These features also may serve as prognostic indicators using a survival curve modeling approach in which the combination of radiomic features and genetic mutations provides an enhanced understanding of the interaction between imaging phenotype and patient genotype on the progression and treatment of IPF.

Proceedings Article | 16 March 2020 Paper

Radiomics-based texture analysis of idiopathic pulmonary fibrosis for genetic and survival predictions

Jorie Budzikowski, Ahmed Rashid, Joseph Foy, Jonathan Chung, Imre Noth, Samuel Armato

Proceedings Volume 11314, 113142K (2020) https://doi.org/10.1117/12.2551172

KEYWORDS: Genetics, Lung, Computed tomography, Pulmonary fibrosis, Fractal analysis, Databases, Feature extraction, Genomics, Data modeling, Receivers

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This study aims to combine differences in radiomic features between internal and peripheral portions of lungs diagnosed with idiopathic pulmonary fibrosis (IPF) and with TOLLIP and MUC5B genetic mutations to predict patient prognosis. A database of computed tomography (CT) scans from 169 IPF patients was selected from the INSPIRE study along with the corresponding genomic and demographic datasets. Three CT sections per patient were chosen to represent the superior, middle, and inferior portions of the lungs. Twelve regions of interest (ROIs) were placed in central and peripheral portions at each level of the lungs, and 142 radiomics features were calculated within each ROI. Based on feature reproducibility, 30 features were used with logistic regression and receiver operating characteristic (ROC) analysis to classify patients with various genetic mutations. Kaplan-Meier survival curve models quantified the ability of each feature to differentiate between survival curves based on a feature-specific threshold. Nine first-order features and one fractal feature were found to be predictive of TOLLIP-1 (rs4963062) mutation (AUC 0.54-0.74). Five Laws’ filter features were predictive of TOLLIP-2 (rs5743905) mutation (AUC 0.53-0.70), while no feature was found to be predictive for MUC5B mutations. First-order and fractal features reflected the greatest discrimination between Kaplan-Meier curves. A radiogenomic approach for predicting patient genetic mutations based on radiomics features extracted from thoracic CT images of patients with IPF has potential as a biomarker. These same features can also serve as predictors of patient prognosis using a survival curve modeling approach.

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