Biological and Radiological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer; Dictionary Version PM1.0
Journal:
arXiv
Published Date:
Dec 14, 2024
Abstract
We investigate the connection between visual semantic features defined in
PI-RADS and associated risk factors, moving beyond abnormal imaging findings,
establishing a shared framework between medical and AI professionals by
creating a standardized dictionary of biological/radiological RFs.
Subsequently, 6 interpretable and seven complex classifiers, linked with nine
interpretable feature selection algorithms (FSA) applied to risk factors, were
extracted from segmented lesions in T2-weighted imaging (T2WI),
diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC)
multiparametric-prostate MRI sequences to predict the UCLA scores. We then
utilized the created dictionary to interpret the best-predictive models.
Combining T2WI, DWI, and ADC with FSAs including ANOVA F-test, Correlation
Coefficient, and Fisher Score, and utilizing logistic regression, identified
key features: The 90th percentile from T2WI, which captures hypo-intensity
related to prostate cancer risk; Variance from T2WI, indicating lesion
heterogeneity; shape metrics including Least Axis Length and Surface Area to
Volume ratio from ADC, describing lesion shape and compactness; and Run Entropy
from ADC, reflecting texture consistency. This approach achieved the highest
average accuracy of 0.78, significantly outperforming single-sequence methods
(p-value<0.05). The developed dictionary for Prostate-MRI (PM1.0) serves as a
common language, fosters collaboration between clinical professionals and AI
developers to advance trustworthy AI solutions that support
reliable/interpretable clinical decisions.
