Multiparametric Prostate MRI Radiomics

• The paper demonstrates that robust radiomic signatures, derived through systematic feature extraction and LASSO-based selection, can significantly improve prostate cancer diagnosis especially in PI-RADS 3 cases.
• Multiparametric prostate MRI radiomics is the extraction of high-dimensional features from multi-sequence MRI scans, enabling objective, quantitative risk stratification and enhanced lesion characterization.
• It integrates standardized MRI preprocessing, advanced texture and wavelet feature computation, and rigorous multi-cohort validation to complement traditional radiologist assessments.

Multiparametric prostate MRI radiomics refers to the quantitative extraction and analysis of high-dimensional features from pre- and post-processed prostate magnetic resonance imaging acquired with multiple imaging sequences, with the objective of improving automated detection, localization, grading, and risk stratification of prostate cancer. The radiomics paradigm emphasizes systematic feature engineering, selection, and statistical modeling to supplement or complement radiologist-driven guidelines such as PI-RADS. Recent work, especially in multi-center and protocol-agnostic environments, demonstrates that robust radiomic signatures can improve accuracy and generalizability of prostate cancer diagnosis, with particular value in equivocal cases (PI-RADS 3), while raising key questions regarding standardization, interpretability, and validation.

1. MRI Acquisition and Preprocessing

Multiparametric MRI (mpMRI) protocols for prostate cancer radiomics typically include high-resolution T2-weighted (T2w) imaging, diffusion-weighted imaging (DWI) with calculation of apparent diffusion coefficient (ADC) maps, and, in some settings, dynamic contrast-enhanced (DCE) MRI. In multi-center studies, harmonization is achieved by standardizing protocols across high-field 3 T systems (e.g., PROSTATEx, WH, GD cohorts) (Li et al., 2021). All images are resampled to isotropic voxel spacing (1×1×1 mm³), and sequence-specific normalization—z-scoring per volume for intensity, rigid registration of DWI/ADC to T2w—is uniformly enforced. Gaussian denoising (σ≈1 mm) is generally applied. Region-of-interest (ROI) delineation is performed manually on T2w by expert radiologists with adjudication for consensus segmentation, enabling robust downstream feature extraction.

2. Radiomic Feature Extraction

A comprehensive set of 1,576 radiomic features are extracted per patient volume, spanning multiple feature categories:

• First-order statistics: Mean, variance, skewness, kurtosis. Example: μ= (1/N)∑_{i=1}^N x_i.
• Shape descriptors: Volume, surface area, sphericity.
• Second-order and texture features: GLCM (24 features), GLRLM (16 features), GLSZM (16 features), NGTDM (5 features).
• Wavelet-decomposed features: 8 sub-bands × first-order/statistics/texture = 744 features (Li et al., 2021).

Mathematical definitions are standardized, e.g., GLCM contrast:

$$\mathrm{Contrast} = \sum_{i=1}^{G}\sum_{j=1}^{G}(i-j)^2P(i,j)$$

where P(i,j) is the normalized joint occurrence of gray levels i and j.

Feature calculation is performed on the entire ROI, as well as on wavelet-decomposed representations, to capture information at multiple spatial scales and frequency bands.

3. Feature Selection and Biomarker Construction

High-dimensionality is addressed by least absolute shrinkage and selection operator (LASSO) logistic regression with ten-fold cross-validation. The L1-penalized objective is:

$$\min_{\boldsymbol{\beta}}\left\{ -\ell(\boldsymbol{\beta}) +\lambda \sum_{j=1}^{p}|\beta_j| \right\}$$

where λ is tuned to minimize deviance (Li et al., 2021).

Radiomic signatures ("R-score") are defined as the LASSO-selected linear combination over the surviving K ≈ 10–20 features:

$$\text{R-score} = \beta_0 + \sum_{j=1}^{K} \beta_j f_j$$

where $f_j$ are normalized features. The resulting model is directly used for clinically significant prostate cancer (csPCa) probability estimation.

4. Multi-Cohort Validation and Robustness

Performance is validated internally (within-cohort 70/30 splits) and externally (cross-cohort deployment). The GD-biomarker, trained exclusively on PI-RADS 3 ("hard sample") cases, demonstrated AUC = 0.94 (train), 0.77 (internal val), 0.77 (WH), and 0.74 (PD) in external testing. In contrast, biomarkers built on mixed PI-RADS distributions (WH/PD) achieved high resubstitution performance but decreased generalizability on external cohorts (AUC as low as 0.62 for WH-biomarker tested on PD) (Li et al., 2021).

Statistical testing (DeLong test) confirmed that the PI-RADS 3–specific biomarker provided superior generalizability and robustness (p < 0.05). This suggests that PI-RADS 3–derived models are less sensitive to inter-institutional differences and technical heterogeneity.

5. Integration with PI-RADS and Clinical Implications

Radiomics augments traditional PI-RADS assessment by providing a continuous, objective R-score capable of stratifying risk within the equivocal (PI-RADS 3) cohort, a group in whom biopsy recommendations are controversial. While precise decision thresholds were not specified, the observed AUC improvements suggest that a combined PI-RADS 3 + R-score strategy could improve biopsy specificity—potentially reducing unnecessary interventions while maintaining sensitivity for csPCa (Li et al., 2021).

6. Methodological Best Practices and Reporting Standards

Standardization of preprocessing steps, feature extraction routines, and model development protocols is critical for reproducibility. The multi-center evaluation enforces:

• Consistent imaging protocols and normalization
• Transparent reporting of segmentation, denoising, and registration steps
• Cross-validated model selection and hyperparameter tuning within each cohort
• External validation emphasizing performance robustness across diverse data distributions

Explicit documentation of all parameters and representative code release is advocated to facilitate interpretability and reproducibility, as outlined in related studies (Schwier et al., 2018).

7. Limitations, Future Prospects, and Recommendations

Reported models did not include DCE features or higher-order clinical or genomic markers. Only area under the curve (AUC) was systematically reported; sensitivity, specificity, and threshold optimization require further paper to enable direct clinical translation. Multi-institutional prospective validation and harmonization frameworks are needed to address remaining sources of technical and population heterogeneity. Nonetheless, robust radiomics biomarkers derived from standardized mpMRI and trained on equivocal/PI-RADS 3 cohorts offer a promising adjunct to classical diagnostic heuristics and may advance individualized risk stratification and biopsy decision-making in prostate cancer (Li et al., 2021).