Assessment of Lung Cancer Development in Idiopathic Pulmonary Fibrosis Patients Using Quantitative High-Resolution Computed Tomography

2020 ◽  
Vol 35 (2) ◽  
pp. 115-122 ◽  
Author(s):  
Stefano Palmucci ◽  
Sebastiano E. Torrisi ◽  
Daniele Falsaperla ◽  
Alessandro Stefano ◽  
Alfredo G. Torcitto ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
High Resolution ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Cancer Development ◽  
High Resolution Computed Tomography

scholarly journals Domain Knowledge-Assisted Automatic Diagnosis of Idiopathic Pulmonary Fibrosis (IPF) Using High Resolution Computed Tomography (HRCT) (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13979-13980
Author(s):  
Wenxi Yu ◽  
Hua Zhou ◽  
Jonathan G. Goldin ◽  
Grace Hyun J. Kim
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Optimal Design ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Domain Knowledge ◽  
Population Level ◽  
Design Criterion ◽  
High Resolution Computed Tomography ◽  
Ct Slices

Domain knowledge acquired from pilot studies is important for medical diagnosis. This paper leverages the population-level domain knowledge based on the D-optimal design criterion to judiciously select CT slices that are meaningful for the disease diagnosis task. As an illustrative example, the diagnosis of idiopathic pulmonary fibrosis (IPF) among interstitial lung disease (ILD) patients is used for this work. IPF diagnosis is complicated and is subject to inter-observer variability. We aim to construct a time/memory-efficient IPF diagnosis model using high resolution computed tomography (HRCT) with domain knowledge-assisted data dimension reduction methods. Four two-dimensional convolutional neural network (2D-CNN) architectures (MobileNet, VGG16, ResNet, and DenseNet) are implemented for an automatic diagnosis of IPF among ILD patients. Axial lung CT images are acquired from five multi-center clinical trials, which sum up to 330 IPF patients and 650 non-IPF ILD patients. Model performance is evaluated using five-fold cross-validation. Depending on the model setup, MobileNet achieved satisfactory results with overall sensitivity, specificity, and accuracy greater than 90%. Further evaluation of independent datasets is underway. Based on our knowledge, this is the first work that (1) uses population-level domain knowledge with optimal design criterion in selecting CT slices and (2) focuses on patient-level IPF diagnosis.

High-Resolution Computed Tomography in Idiopathic Pulmonary Fibrosis

2005 ◽  
Vol 172 (4) ◽  
pp. 488-493 ◽  
Author(s):  
David A. Lynch ◽  
J. David Godwin ◽  
Sharon Safrin ◽  
Karen M. Starko ◽  
Phil Hormel ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
High Resolution Computed Tomography

scholarly journals Comparative analysis of density histograms and visual scores in incremental and volumetric high-resolution computed tomography of the chest in idiopathic pulmonary fibrosis patients

2020 ◽  
Author(s):  
Gaetano Rea ◽  
Marina De Martino ◽  
Annalisa Capaccio ◽  
Pasquale Dolce ◽  
Tullio Valente ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Lung Function ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Ct Scans ◽  
High Resolution Computed Tomography ◽  
Disease Extent ◽  
Single Breath ◽  
Volumetric Ct

Abstract Background Volumetric high-resolution computed tomography (HRCT) of the chest has recently replaced incremental CT in the diagnostic workup of idiopathic pulmonary fibrosis (IPF). Concomitantly, visual and quantitative scores have been proposed for disease extent assessment to ameliorate disease management. Purpose To compare the performance of density histograms (mean lung attenuation, skewness, and kurtosis) and visual scores, along with lung function correlations, in IPF patients submitted to incremental or volumetric thorax HRCT. Material and methods Clinical data and CT scans of 89 newly diagnosed and therapy-naive IPF patients were retrospectively evaluated. Results Forty-six incremental and 43 volumetric CT scans were reviewed. No differences of density histograms and visual scores estimates were found by comparing two HRCT techniques, with an optimal inter-operator agreement (concordance correlation coefficient >0.90 in all instances). Single-breath diffusing lung capacity for carbon monoxide (DLCOsb) was inversely related with the Best score (r = −00.416; p = 0.014), the Kazerooni fibrosis extent (r = −0.481; p = 0.004) and the mean lung attenuation (r = −0.382; p = 0.026), while a positive correlation was observed with skewness (r = 0.583; p = 0.001) and kurtosis (r = 0.543; p = 0.001) in the incremental HRCT sub-group. Similarly, in the volumetric CT sub-cohort, DLCOsb was significantly associated with skewness (r = 0.581; p = 0.007) and kurtosis (r = 0.549; p = 0.018). Correlations with visual scores were not confirmed. Forced vital capacity significantly related to all density indices independently on HRCT technique. Conclusions Density histograms and visual scores similarly perform in incremental and volumetric HRCT. Density quantification displays an optimal reproducibility and proves to be superior to visual scoring as more strongly correlated with lung function.

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