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

This study investigates the connection between visual semantic features in PI-RADS and associated risk factors, moving beyond abnormal imaging findings by creating a standardized dictionary of biological/radiological radiomics features (RFs). Using multiparametric prostate MRI sequences (T2-weighted imaging [T2WI], diffusion-weighted imaging [DWI], and apparent diffusion coefficient [ADC]), six interpretable and seven complex classifiers, combined with nine feature selection algorithms (FSAs), were applied to segmented lesions to predict UCLA scores. Combining T2WI, DWI, and ADC with FSAs such as ANOVA F-test, Correlation Coefficient, and Fisher Score, and utilizing logistic regression, identified key features: the 90th percentile from T2WI (hypo-intensity linked to cancer risk), variance from T2WI (lesion heterogeneity), shape metrics like Least Axis Length and Surface Area to Volume ratio from ADC (lesion compactness), and Run Entropy from ADC (texture consistency). This approach achieved an average accuracy of 0.78, outperforming single-sequence methods (p < 0.05). The developed dictionary provides a common language, fostering collaboration between clinical professionals and AI developers to enable trustworthy, interpretable AI for reliable clinical decisions.

Machine Learning Evaluation Metric Discrepancies across Programming Languages and Their Components: Need for Standardization

This study evaluates metrics for tasks such as classification, regression, clustering, correlation analysis, statistical tests, segmentation, and image-to-image (I2I) translation. Metrics were compared across Python libraries, R packages, and Matlab functions to assess their consistency and highlight discrepancies. The findings underscore the need for a unified roadmap to standardize metrics, ensuring reliable and reproducible ML evaluations across platforms. This study examined a wide range of evaluation metrics across various tasks and found only some to be consistent across platforms, such as (i) Accuracy, Balanced Accuracy, Cohens Kappa, F-beta Score, MCC, Geometric Mean, AUC, and Log Loss in binary classification; (ii) Accuracy, Cohens Kappa, and F-beta Score in multi-class classification; (iii) MAE, MSE, RMSE, MAPE, Explained Variance, Median AE, MSLE, and Huber in regression; (iv) Davies-Bouldin Index and Calinski-Harabasz Index in clustering; (v) Pearson, Spearman, Kendall's Tau, Mutual Information, Distance Correlation, Percbend, Shepherd, and Partial Correlation in correlation analysis; (vi) Paired t-test, Chi-Square Test, ANOVA, Kruskal-Wallis Test, Shapiro-Wilk Test, Welchs t-test, and Bartlett's test in statistical tests; (vii) Accuracy, Precision, and Recall in 2D segmentation; (viii) Accuracy in 3D segmentation; (ix) MAE, MSE, RMSE, and R-Squared in 2D-I2I translation; and (x) MAE, MSE, and RMSE in 3D-I2I translation. Given observation of discrepancies in a number of metrics (e.g. precision, recall and F1 score in binary classification, WCSS in clustering, multiple statistical tests, and IoU in segmentation, amongst multiple metrics), this study concludes that ML evaluation metrics require standardization and recommends that future research use consistent metrics for different tasks to effectively compare ML techniques and solutions.