Normalization (sociology)

Cancer imaging

Lung cancer

Computer science

Anthropology

Radiology

Radiomics

Imaging phantom

Voxel

Correlation

Pattern recognition (psychology)

Mathematics

Internal medicine

Sociology

Artificial intelligence

Geometry

Medicine

Nuclear medicine

Cancer

Pathology

Scientific Reports

Radiomic features are potential imaging biomarkers for therapy response assessment in oncology. However, the robustness of features with respect to imaging parameters is not well established. Previously identified potential imaging biomarkers were found to be intrinsically dependent on voxel size and number of gray levels (GLs) in a recent texture phantom investigation. Here, we validate the voxel size and GL in-phantom normalizations in lung tumors. Eighteen patients with non-small cell lung cancer of varying tumor volumes were analyzed. To compare with patient data, phantom scans were acquired on eight different scanners. Twenty four previously identified features were extracted from lung tumors. The Spearman rank (rs) and interclass correlation coefficient (ICC) were used as metrics. Eight out of 10 features showed high (rs > 0.9) and low (rs < 0.5) correlations with number of voxels before and after normalizations, respectively. Likewise, texture features were unstable (ICC < 0.6) and highly stable (ICC > 0.8) before and after GL normalizations, respectively. We conclude that voxel size and GL normalizations derived from a texture phantom study also apply to lung tumors. This study highlights the importance and utility of investigating the robustness of radiomic features with respect to CT imaging parameters in radiomic phantoms.

Voxel size and gray level normalization of CT radiomic features in lung cancer