Biological and Radiological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer; Dictionary Version PM1.0

• URL: http://arxiv.org/abs/2412.10967v2
• Date: Wed, 18 Dec 2024 02:26:46 GMT
• Title: Biological and Radiological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer; Dictionary Version PM1.0
• Authors: Mohammad R. Salmanpour, Sajad Amiri, Sara Gharibi, Ahmad Shariftabrizi, Yixi Xu, William B Weeks, Arman Rahmim, Ilker Hacihaliloglu,
• Abstract summary: We created a standardized dictionary of biological/radiologicalRFs for PI-RADS and associated risk factors.<n>We then utilized the dictionary to interpret the best-predictive models.<n>This approach achieved the highest average accuracy of 0.78, significantly outperforming single-sequence methods.
• Score: 1.2200133485912512
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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