10.1371/journal.pone.0196910.s001
Tobias Peikert
Tobias
Peikert
Fenghai Duan
Fenghai
Duan
Srinivasan Rajagopalan
Srinivasan
Rajagopalan
Ronald A. Karwoski
Ronald
A. Karwoski
Ryan Clay
Ryan
Clay
Richard A. Robb
Richard
A. Robb
Ziling Qin
Ziling
Qin
JoRean Sicks
JoRean
Sicks
Brian J. Bartholmai
Brian
J. Bartholmai
Fabien Maldonado
Fabien
Maldonado
S1 File -
Public Library of Science
2018
screen-detected lung nodules
NLST
novel radiomics-based approach
Minimum
method
8 features
SILA
curvature
novel radiomic LDCT-based approach
LASSO multivariate modeling
screen-detected nodule characterization
surface
National Lung Screening Trial Purpose Optimization
flatness
radiologic nodule features
optimism-corrected AUC
Average
variable
Maximum shape index
analysis
tomography-based radiomic classifier
validation
2018-05-14 17:45:32
Journal contribution
https://plos.figshare.com/articles/journal_contribution/Novel_high-resolution_computed_tomography-based_radiomic_classifier_for_screen-identified_pulmonary_nodules_in_the_National_Lung_Screening_Trial/6266501
<p><b>Figure A.</b> Analysis of the CALIPER texture features within the lung nodules. The texture features within the shaded region do not appear within the lung nodules.</p> <p><b>Figure B</b> Three dimensional scatter plot showing the pairwise Dice Similarity Coefficient (DSC) between the nodules segmented by the Radiologist (Rx), Pulmonologist (Px) and Image Analyst (IA).</p> <p><b>Table A</b> Algorithmic components of nodule surface characterization and the strategy used during the pilot study and current improvements.</p> <p><b>Table B.</b> List of quantitative metrics used in the discrimination of benign and malignant nodules. The pval, 95% CI and the probability plot correlation coefficient (PPCC) are given in the last column for benign (N = 319) and malignant (N = 338) nodules.</p> <p><b>Figure C</b> Mosaic showing the glyphs (<b>A</b>, <b>D</b>), the nodule distribution within the upper, middle, lower left and right lung (<b>B</b>, <b>E</b>) and the Score Indicative of Lesion Abnormality (SILA) for the NLST malignant and benign nodules used in this study. The glyphs are ordered in Panels <b>A</b> and <b>D</b> based on the nodule-specific SILA values; the SILA values in Panels <b>C</b> and <b>F</b> are color coded in green, yellow and red based on the previously developed CANARY categorization.</p> <p><b>Figure D</b> Three dimensional scatter plot showing the variations in the SILA (Score Indicative of Lung Abnormality) between the nodules segmented by 3 operators. Panels A and B respectively show the SILA values for the nodule texture and surface. The nodules (N = 266) described in section 2.2.1 were used for this analysis.</p> <p>(DOCX)</p>