Assessment of CT image reconstruction parameters on radiomic features in a lung cancer screening cohort: the PROSPR study

Babak Haghighi; Peter B. Noël; Eric Cohen; Lauren Pantalone; Anil Vachani; Katharine A. Rendle; Jocelyn Wainwright; Chelsea Saia; Eduardo Mortani Barbosa Jr.; Despina Kontos

doi:10.1117/12.2550161

16 March 2020 Assessment of CT image reconstruction parameters on radiomic features in a lung cancer screening cohort: the PROSPR study

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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Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113142G (2020) https://doi.org/10.1117/12.2550161
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

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Babak Haghighi, Peter B. Noël, Eric Cohen, Lauren Pantalone, Anil Vachani, Katharine A. Rendle, Jocelyn Wainwright, Chelsea Saia, Eduardo Mortani Barbosa Jr., and Despina Kontos "Assessment of CT image reconstruction parameters on radiomic features in a lung cancer screening cohort: the PROSPR study", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142G (16 March 2020); https://doi.org/10.1117/12.2550161

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KEYWORDS

Lung

Lung cancer

Computed tomography

Feature extraction

CT reconstruction

Scanners

Cancer

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