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SU‐E‐J‐259; How Does CT Reconstruction Kernel Affect the Radiogenomic Features in Non‐Small Cell Lung Cancer?
Author(s) -
Jin H,
Ahn C,
Kuo M,
Kim J
Publication year - 2015
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4924345
Subject(s) - kernel (algebra) , radiogenomics , imaging phantom , medicine , feature (linguistics) , artificial intelligence , data set , nuclear medicine , histogram , lung cancer , iterative reconstruction , pattern recognition (psychology) , radiology , mathematics , image (mathematics) , computer science , pathology , radiomics , linguistics , philosophy , combinatorics
Purpose: Radiogenomics promises to discover quantitative imaging features which are associated with genomic profiles and of prognostic in cancer patients. However, the CT imaging features are known to be sensitive to noise characteristic and affected by CT parameters. We investigate the variability of CT imaging features which are previously reported as radiogenomic markers in non‐small cell lung cancer (NSCLC). Methods: Three NSCLC cases of CT exams with 2 reconstruction kernels (B30f, B60f) were selected. We set ten ROIs within tumor mask. Run length nonuniformity (RLN) and histogram energy, which were previously reported to be predictive of patient survival, were calculated for each ROI. For establishing the intrinsic variability of image feature depending on the reconstruction kernel, we carried out the same feature extraction procedure on the COPDGene phantom CT images. The coefficient of variation (CoV) was compared between imaging features from images of single reconstruction kernel and mixed kernel image set. Results: RLN and Energy of standard kernel for phantom data showed higher values up to 366% than that of sharp kernel. For patient cases, the image features of standard kernel was higher 196% than that of sharp kernel on an average. The CoV of RLN was 0.142±0.035 for B30f data set and 0.060±0.015 for B60f data set in patient case study. For the mixed kernel dataset, the CoV of RLN increased to 0.407±0.061. The CoV of Energy feature values showed similar trends with RLN. Conclusion: Out study revealed high variability of CT image features depending on reconstruction kernel. These characteristic should be considered in feature extraction procedure when different kernels are used in the patient dataset. Use of the same CT protocol is preferred. Otherwise, application of kernel normalization techniques is necessary in the radiogenomic study.