  Background: The high dimensionality of radiomic feature sets, the variability
in radiomic feature types and potentially high computational requirements all
underscore the need for an effective method to identify the smallest set of
predictive features for a given clinical problem. Purpose: Develop a
methodology and tools to identify and explain the smallest set of predictive
radiomic features. Materials and Methods: 89,714 radiomic features were
extracted from five cancer datasets: low-grade glioma, meningioma, non-small
cell lung cancer (NSCLC), and two renal cell carcinoma cohorts (n=2104).
Features were categorized by computational complexity into morphological,
intensity, texture, linear filters, and nonlinear filters. Models were trained
and evaluated on each complexity level using the area under the curve (AUC).
The most informative features were identified, and their importance was
explained. The optimal complexity level and associated most informative
features were identified using systematic statistical significance analyses and
a false discovery avoidance procedure, respectively. Their predictive
importance was explained using a novel tree-based method. Results: MEDimage, a
new open-source tool, was developed to facilitate radiomic studies.
Morphological features were optimal for MRI-based meningioma (AUC: 0.65) and
low-grade glioma (AUC: 0.68). Intensity features were optimal for CECT-based
renal cell carcinoma (AUC: 0.82) and CT-based NSCLC (AUC: 0.76). Texture
features were optimal for MRI-based renal cell carcinoma (AUC: 0.72). Tuning
the Hounsfield unit range improved results for CECT-based renal cell carcinoma
(AUC: 0.86). Conclusion: Our proposed methodology and software can estimate the
optimal radiomics complexity level for specific medical outcomes, potentially
simplifying the use of radiomics in predictive modeling across various
contexts.
