Pretreatment MRI Descriptors Overview

• Pretreatment MRI descriptors are standardized imaging features that quantify tissue phenotype, tumor morphology, intensity, kinetic, and microstructural properties before therapy.
• They are extracted using rigorous, IBSI-compliant workflows combining radiomics, microstructural modeling, and kinetic analyses to ensure reproducible and reliable results.
• By integrating with statistical and machine learning models, these descriptors support personalized risk assessment and predictive modeling across oncology, neurology, and musculoskeletal applications.

Pretreatment MRI descriptors are quantitative or categorical imaging features, extracted from magnetic resonance imaging prior to therapeutic intervention, that characterize tissue phenotype, tumor morphology, intensity, spatial distribution, enhancement kinetics, or microstructural properties for the purposes of diagnosis, prognosis, or predictive modeling. They serve as standardized biomarkers for patient stratification, risk assessment, and as covariates or inputs for machine learning models in clinical research. Modern descriptor frameworks encompass both radiologist-assigned categorical gradings (e.g., BI-RADS, BPE) and high-dimensional radiomic or microstructural features derived from algorithmic pipelines, with mathematical rigor in extraction and normalization. These descriptors have demonstrated statistical and clinical utility across oncology, neurology, and musculoskeletal applications, forming a cornerstone of precision medicine studies.

1. Definition and Taxonomy of Pretreatment MRI Descriptors

Pretreatment MRI descriptors can be systematically grouped by their computational origin, biological target, and measurement type. Major categories include:

• Anatomical and Shape Descriptors: Quantify lesion or anatomical compartment volume, surface area, compactness, sphericity, axis lengths, and related morphological features. These are essential in tumor burden estimation and stratification (Banerjee et al., 2021, Hatamikia et al., 2023, Malhaire et al., 21 Nov 2025).
• Intensity (First-order) Features: Capture the histogram of voxel intensities within a region of interest; examples include mean, median, variance, skewness, kurtosis, percentiles, and entropy (Hatamikia et al., 2023, Amiri et al., 13 Sep 2025, Banerjee et al., 2016).
• Texture Features: Describe spatial patterns or heterogeneity of voxel intensities, often via gray-level co-occurrence matrices (GLCMs), run-length matrices (GLRLM), size zone matrices (GLSZM), and gray-level dependence matrices (GLDM) with defined mathematical formulas per the IBSI standard (Amiri et al., 13 Sep 2025, Banerjee et al., 2016).
• Functional/Kinetic Metrics: In dynamic contrast-enhanced protocols, descriptors such as percent enhancement (PE), signal enhancement ratio (SER), wash-in/wash-out rates, and area under the time-intensity curve are computed to characterize vascular permeability and tissue perfusion (Velden, 2018).
• Microstructural and Biophysical Descriptors: Derived from advanced models such as multi-compartment diffusion (e.g., intra-/extra-neurite signal fractions $f_I$, $f_E$; diffusivities $D_I$, $D_E$), T2 relaxometry (e.g., myelin water fraction), and free-water mapping (Fischi-Gomez et al., 2021).
• Location-Based and Atlas Features: Anatomically map lesion occurrence, intensity, or volumetric compartment within parcellation atlases or structural brain templates; include lobe-wise frequencies, hemisphere ratios, and per-structure occupancy (Ismail et al., 2020, Banerjee et al., 2021).
• Aggregated Disease Compositions: Voxel-wise machine learning assignments quantifying composition of tissue states (e.g., tumor, necrosis, edema) in a defined region, leading to “predicted disease composition” vectors (Diller et al., 2019).

This taxonomy underpins descriptor selection in disease-specific pipelines and multimodal statistical modeling workflows.

2. Mathematical Formulation and Extraction Pipelines

Extraction of pretreatment MRI descriptors follows rigorous computational protocols to ensure reproducibility, invariance to trivial transformations, and standardization across cohorts. Representative approaches include:

• Radiomics via IBSI-Compliant Workflows: Feature extraction as per the Image Biomarker Standardization Initiative (IBSI), with formal mathematical definitions (e.g., $\text{GLCM Contrast} = \sum_{i,j}(i-j)^2p_{ij}$), fixed intensity binning, isotropic resampling, and mesh-based shape computation (Amiri et al., 13 Sep 2025, Hatamikia et al., 2023).
• Microstructural Modelling: For multi-compartment diffusion, the signal model

$S(b) = \sum_{i=1}^N f_i e^{-b D_i}$

parameterizes signal fractions and apparent diffusivities; T2 relaxometry fits echo amplitudes as $S(TE) = \sum_{j=1}^M A_j e^{-TE/T_{2,j}}$ (Fischi-Gomez et al., 2021).

• Kinetic Feature Calculation: Percent enhancement $$PE = \frac{SI_{\text{post}} - SI_{\text{pre}}}{SI_{\text{pre}}} \times 100\%$$, SER, and slopes computed from time-resolved T1-weighted series for breast cancer risk and monitoring (Velden, 2018).
• Atlas and Habitat Features: Parcellation overlaps determined as fractions of volume $$\mathrm{Frac}_{p,X} = \frac{\mathrm{Vol}(X \cap \text{Parcel}_p)}{\mathrm{Vol}(\text{WT})}$$; habitats defined by k-means clustering of multiparametric signal vectors and volumetric quantification per habitat label (Banerjee et al., 2021).
• Location-Based Frequency Descriptors: For GBM, lobe-wise frequencies

$$F^c_k = \frac{1}{|\mathcal{L}_k|}\sum_{x \in \mathcal{L}_k}A^{\text{enh}/\text{fl}}_{\text{group}}(x)$$

and ratios of right/left lobar involvement quantify spatial disease bias (Ismail et al., 2020).

Most pipelines integrate preprocessing steps—bias correction, intensity normalization, skull-stripping, registration, and discretization—before feature computation. For radiomics, per-feature zero-mean unit-variance normalization is standard prior to model input (Banerjee et al., 2016, Hatamikia et al., 2023).

3. Statistical Modeling, Selection, and Validation

Pretreatment MRI descriptors serve as covariates for predictive and prognostic models, with typical workflows as follows:

• Univariate Association: Logistic regression, Cox proportional hazards, or discriminant analyses relate individual descriptors to endpoints such as pathological complete response (pCR), overall survival, or specific histopathological markers. Reported odds ratios quantify effect magnitude (Malhaire et al., 21 Nov 2025).
• Machine Learning Selection: High-dimensional radiomic sets are filtered using penalized regression (ElasticNet), random forests variable importance, mutual information, or wrapper/embedded methods. Selection criteria include maximizing AUC, C-index, or information gain, with cross-validation to prevent overfitting (Banerjee et al., 2021, Amiri et al., 13 Sep 2025, Banerjee et al., 2016).
• Multimodal Integration: Clinical, molecular, and imaging features are concatenated (e.g., MRI-based size, margin, and focality descriptors together with Ki67, TILs, and subtype) and entered into ensemble classifiers or survival forests, yielding improved predictive performance and calibrated risk quantification (Malhaire et al., 21 Nov 2025).
• Model Validation: Metrics include AUC (ROC), C-index (survival), sensitivity, specificity, precision, and held-out test performance, with significance assessed by Wilcoxon and DeLong tests as appropriate (Malhaire et al., 21 Nov 2025, Hatamikia et al., 2023).

Feature stability to segmentation uncertainty is assessed by quantifying intraclass correlation (ICC) across morphological perturbations, establishing robustness and guiding prioritized descriptor usage (Hatamikia et al., 2023).

4. Clinical Applications Across Domains

Pretreatment MRI descriptors enable a range of clinically relevant applications:

• Oncologic Response and Prognosis: In breast cancer, descriptors such as non-spiculated margins, unifocality, maximal lesion size, and radiomics signatures stratify likelihood of pCR after neoadjuvant chemotherapy, complementing established molecular predictors and informing individualized regimens (Malhaire et al., 21 Nov 2025, Hatamikia et al., 2023).
• Functional and Kinetic Tissue Assessment: Quantitative BPE metrics, kinetic curves, and parenchymal enhancement in breast MRI serve as independent predictors of cancer risk, response to therapy, and prognostic endpoints, beyond categorical BI-RADS assessment (Velden, 2018).
• Neuro-oncology Characterization: Voxel-wise composition vectors, habitat and atlas occupancy features, and microstructural markers (e.g., $f_I$, $f_E$, MWF) discriminate between normal tissue, peritumoral edema, necrosis, and active tumor, supporting survival modeling, grade prediction, and differential diagnosis (e.g., pseudo-progression vs recurrence in GBM) (Fischi-Gomez et al., 2021, Banerjee et al., 2021, Ismail et al., 2020, Diller et al., 2019).
• Prostate Cancer Aggressiveness: Integrated multiparametric descriptors—texture, shape, intensity—across T2, DWI, and ADC, selected by regularized logistic regression, distinguish aggressive (Gleason ≥ 7) from non-aggressive disease (AUC = 0.73), with DWI-derived roughness featuring highest discriminative power (Banerjee et al., 2016).
• Contrast-Independent Enhancement Prediction: Machine learning pipelines predict gadolinium contrast enhancement directly from non-contrast T1WI features, using robust radiomics and multicenter harmonization strategies to obviate the need for contrast agents and support treatment planning (Amiri et al., 13 Sep 2025).

5. Descriptor Robustness, Standardization, and Reproducibility

Ensuring descriptor stability under segmentation variability and across imaging protocols is essential for clinical translation:

• Segmentation Sensitivity: Small, systematic smoothing or morphological erosion (up to 1 mm) generally preserves radiomic feature values (ICC > 0.9 for shape, >90% for texture), whereas large dilations or ellipsoid approximations degrade both numerical robustness and model AUC (Hatamikia et al., 2023).
• Class-Specific Stability: Shape descriptors are the most robust under perturbation; texture features remain stable for mild variations; first-order features show intermediate sensitivity. Coarse approximations should be avoided unless shown to preserve signal (Hatamikia et al., 2023).
• Feature Standardization: Adoption of IBSI definitions and harmonized extraction (binning, resampling, normalization) allows reproducibility across centers and cohorts, facilitating multicenter studies and meta-analyses (Amiri et al., 13 Sep 2025).
• Preprocessing Protocols: Bias correction, co-registration, and intensity standardization are essential for stability; omitting these introduces site and scanner dependence and reduces generalizability (Banerjee et al., 2021, Amiri et al., 13 Sep 2025).

Results reinforce the necessity of validating not only predictive power but also feature robustness to segmentation and parameter uncertainty before deploying descriptors in decision-support tools.

6. Integration into Clinical and Research Workflows

Pretreatment MRI descriptors possess direct translational relevance within clinical workflows and multicenter trials:

• Reporting Standards: Routine breast MRI should include BI-RADS BPE grade and, when feasible, quantitative PE/SER metrics, documented with laterality and percentage thresholds for risk stratification (Velden, 2018).
• Decision Support and Risk Prediction: Descriptor sets are being integrated into machine-learning–based decision-support pipelines within PACS/RIS, auto-generating lesion probability maps (e.g., in GBM) or providing risk scores for expected treatment response (Ismail et al., 2020).
• Model-Based Triage: Rule-based logic derived from statistical differences in location descriptors (e.g., parietal FLAIR frequency > 0.75 suggests recurrence) can flag patients for early intervention or further diagnostic workup (Ismail et al., 2020).
• Research Standardization: The move toward multicenter, stability-aware modeling frameworks and feature harmonization promotes reproducible benchmarking and robust generalization of imaging markers (Amiri et al., 13 Sep 2025).

Emerging consensus emphasizes integrating standardized, robust pretreatment MRI descriptors with clinicopathological variables in both statistical and machine-learning models, providing a foundation for personalized therapy and refined prognostic stratification across disease domains.