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Proceedings Paper

Assessment of CT image reconstruction parameters on radiomic features in a lung cancer screening cohort: the PROSPR study
Author(s): Babak Haghighi; Peter B. Noël; Eric Cohen; Lauren Pantalone; Anil Vachani; Katharine A. Rendle; Jocelyn Wainwright; Chelsea Saia; Eduardo Mortani Barbosa Jr.; Despina Kontos
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Paper Abstract

Background: Imaging biomarkers derived from quantitative computed tomography (QCT) enable to quantify lung diseases and to distinguish their phenotypes. However, variability in radiomic features can have an impact on their diagnosis and prognosis significance. We aim to assess the effect of CT image reconstruction parameters on radiomic features in the PROSPR lung cancer screening cohort (1); thereby identifying more robust imaging features across heterogeneous CT images. Methods: CT feature extraction analysis was performed using a lattice-based texture estimation for data (n = 330) collected from a single CT scanner (Siemens Healthineers, Erlangen, Germany) with two different sets of image reconstruction kernels (medium (I30f), sharp (I50f)). A total of 26 features from three major statistical approaches, graylevel histogram, co-occurrence, and run-length, were computed. Features were calculated and averaged within a range of window sizes (W) from 4mm to 20mm. Furthermore, an unsupervised hierarchal clustering was applied to the features to identify distinct phenotypic patterns for the two kernels. The difference across phenotypes by age, sex, and Lung-Rads was assessed. Results: The results showed two distinct subtypes for two kernels across different window sizes. The heat map generated by radiomic features of the sharper kernel provided more distinct patterns compared to the medium kernel. The extracted features across the two kernels and their corresponding clusters were compared based on different clinical features. Conclusions: Our results suggest a set of radiomic features across different kernels can distinguish distinct phenotypes and can also help to assess the sensitivity of texture analysis to CT variabilities; helping for a better characterization of CT heterogeneity.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142G (16 March 2020); doi: 10.1117/12.2550161
Show Author Affiliations
Babak Haghighi, Univ. of Pennsylvania (United States)
Hospital of the Univ. of Pennsylvania (United States)
Peter B. Noël, Univ. of Pennsylvania (United States)
Hospital of the Univ. of Pennsylvania (United States)
Eric Cohen, Univ. of Pennsylvania (United States)
Hospital of the Univ. of Pennsylvania (United States)
Lauren Pantalone, Univ. of Pennsylvania (United States)
Hospital of the Univ. of Pennsylvania (United States)
Anil Vachani, Univ. of Pennsylvania (United States)
Hospital of the Univ. of Pennsylvania (United States)
Katharine A. Rendle, Univ. of Pennsylvania (United States)
Hospital of the Univ. of Pennsylvania (United States)
Jocelyn Wainwright, Univ. of Pennsylvania (United States)
Hospital of the Univ. of Pennsylvania (United States)
Chelsea Saia, Univ. of Pennsylvania (United States)
Hospital of the Univ. of Pennsylvania (United States)
Eduardo Mortani Barbosa Jr., Univ. of Pennsylvania (United States)
Hospital of the Univ. of Pennsylvania (United States)
Despina Kontos, Univ. of Pennsylvania (United States)
Hospital of the Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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