Radiomic Feature Normalization, Selection, and Modeling in Non-Small Cell Lung Cancer: A Retrospective Workflow Analysis (Preprint)
Authors: Carver Eric Nathan , Marks Julia M 
Published: January 2025

BACKGROUND

Radiomics is a field that extracts additional information from a patient’s medical images through quantitative analysis in search of clinically useful patterns and relationships. Using current standard-of-care medical images, we can obtain patient-specific information through noninvasive measures which can give us in-sight into disease progression and treatment response that may not be visually apparent. However, there are barriers that must be overcome before widespread clinical implementation. This instability of radiomic feature values hampers radiomic research progression. Researchers are currently addressing this through the establishment of imaging and feature standardizations and research reporting requirements. However, it has been shown that each aspect of the hand-crafted radiomics workflow influences other aspects. Understanding the impact and importance of each detail in every aspect not only in its respective role, but also in combination with the rest of the workflow.

OBJECTIVE

This study aims to aid in the characterization of commonly used techniques for feature normalization, selection, and modeling by generating and investigating unique radiomics workflows. The overarching goal is to explore how each component uniquely impacts the overall workflow and model performance.

METHODS

Computed Tomography 302 patients with non-small cell lung cancer were investigated through radiomics features extraction from gross tumor volume by Pyradiomics. Each patient’s resultant radiomic features were separated into 80%:20% training &:testing cohorts and programmatically investigated through varying either the feature normalization, feature selection method, number of features, or model. Thusly, over 1900 unique workflows were created and resultant radiomic signature sets (RSSs) ability differences were judged to characterize each investigated techniques capabilities via area under the curve (ROC-AUC) and statistically by log-rank. Relative abilities for the eleven feature normalizations were individually assessed by sub-grouping each RSS by normalization employed and determining average, standard deviation and median ROC-AUC. We similarly investigated six feature selections, nine models , and varying number of selected features. We determined best capabilities of each through their ability to consistently generate RSSs with relatively higher performance in both the training and testing datasets along with their relative ability to consistently generate statistically significant RSSs.

RESULTS

Each of the workflows possesses unique ability. However, it is important to note that none of the methods demonstrated clear dominance. Best performing techniques only moderately increased training/testing performance.

CONCLUSIONS

: Results indicate importance of thorough experimental determination of key work-flow aspects and provides essential insights into the interplay between normalization, selection, and modeling techniques in radiomics workflows.