Prof. Anil Vachani Profile

Prof. Anil Vachani

at Univ of Pennsylvania

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Proceedings Article | 16 March 2020 Paper

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

Babak Haghighi, Peter Noël, Eric Cohen, Lauren Pantalone, Anil Vachani, Katharine Rendle, Jocelyn Wainwright, Chelsea Saia, Eduardo Mortani Barbosa , Despina Kontos

Proceedings Volume 11314, 113142G (2020) https://doi.org/10.1117/12.2550161

KEYWORDS: Lung cancer, Lung, Computed tomography, Feature extraction, CT reconstruction, Scanners, Image restoration, Cancer, Medicine, Statistical analysis

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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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