Radiomic Fingerprints: Imaging Biomarkers

• Radiomic fingerprints are rigorously defined, task-specific, high-dimensional imaging signatures that compactly encode quantitative tissue phenotypes for clinical applications.
• They integrate hand-crafted, deep learning, and patient-specific methods to extract reproducible features and optimize diagnostic and prognostic accuracy.
• Empirical studies in CT, MRI, and multiparametric imaging demonstrate that these fingerprints improve reproducibility, interpretability, and clinical performance while addressing standardization challenges.

A radiomic fingerprint is a rigorously defined, high-dimensional, and task-specific vector signature that compactly encodes the quantitative imaging phenotype of a biological entity—such as a tumor or anatomical region—using features extracted from medical images. Unlike generic radiomic feature sets, which may be large, redundant, and agnostic to context, radiomic fingerprints are distilled through selection, modeling, or learned feature discovery to maximize discriminative, prognostic, or predictive power for a specific clinical endpoint, and are validated for robustness and interpretability. The radiomic fingerprint paradigm underlies contemporary approaches to quantitative imaging biomarker development in oncology, neurology, and musculoskeletal medicine (Shafiee et al., 2015, Afshar et al., 2018, Chen et al., 13 Jan 2026, Chen et al., 25 Jun 2025, Parekh et al., 2018, Chaddad et al., 2019).

1. Conceptual Foundations and Definitions

Radiomic fingerprints originate from the broader field of radiomics, which refers to the quantitative extraction of engineered or learned features from medical images for use in predictive models (Afshar et al., 2018). A radiomic fingerprint is not simply a predefined list of radiomic features; rather, it is a compact, reproducible, and often minimal set of quantitative descriptors—either hand-crafted (first-order, shape, texture) or deep-learned—that are distilled and validated for a specific diagnostic, prognostic, or predictive task (Afshar et al., 2018). The fingerprint uniquely characterizes salient aspects of tissue phenotype such as heterogeneity, spatial complexity, and intensity distribution in a form suitable for robust biomarker analysis.

Mathematically, for a radiomic feature pool $F = \{f_1, ..., f_N\}$ and an imaging object $I$, the full feature vector is $r = [f_1(I), ..., f_N(I)]^T \in \mathbb{R}^N$, and a fingerprint is formed by selecting or weighting a subset (possibly per-task or per-patient) through statistical modeling or feature relevance estimation (Chen et al., 25 Jun 2025, Chen et al., 13 Jan 2026).

2. Radiomic Fingerprint Construction: Classical, Deep, and Hybrid Pipelines

2.1. Hand-Crafted Feature Extraction and Selection

Traditional workflows extract first-order statistics ($\mu$, $\sigma^2$, entropy), shape metrics (volume, surface area, sphericity), and texture features (GLCM, GLRLM, GLSZM, NGTDM) from ROIs defined on segmented medical images (Afshar et al., 2018, Bobholz et al., 2019, Na et al., 11 Jul 2025). These features are then filtered by reproducibility (e.g., Intra-Class Correlation, ICC), stability, and relevance using LASSO, MRMR, or PCA to form the final fingerprint (Afshar et al., 2018).

2.2. Deep Radiomic Fingerprints

Discovery radiomics replaces hand-crafted features with high-dimensional embeddings learned by deep neural networks trained directly on imaging-and-outcome pairs (Shafiee et al., 2015, Kumar et al., 2015, Chaddad et al., 2019). StochasticNet radiomic sequencers are paradigmatic: convolutional networks with stochastic connectivity (e.g., Bernoulli($p=0.5$) masks over weights) extract lesion-specific representations $\phi(x) \in \mathbb{R}^D$ (often $D\gg 100$), mapping lesion patches to feature vectors with enhanced linear separability of clinical labels (Shafiee et al., 2015). Deep radiomic fingerprints capture joint intensity–texture–shape patterns inaccessible to traditional feature banks and can be visualized as response maps of the last-layer filters.

2.3. Per-Patient and Dynamic Fingerprints

Recent advances in individualization predict feature relevance per patient using neural selectors, yielding patient-specific fingerprints $r^s = m \odot r$ where $m$ is a binary or soft mask from a trained selector network (Chen et al., 25 Jun 2025, Chen et al., 13 Jan 2026). This adaptive paradigm allows tailored representation of heterogeneous pathology, increasing both expressiveness and interpretability.

2.4. Multiparametric and Multi-Flavour Fingerprints

Multiparametric radiomics (e.g., MPRAD) and tensor radiomics (TR) extend the feature space across multiple modalities, preprocessing regimes, or parameter “flavours” (Parekh et al., 2018, Rahmim et al., 2022). The tensor representation $T \in \mathbb{R}^{F \times N^f}$ captures feature values under multiple discretizations or perturbations, with downstream ML or DL fusion models learning the optimal combination (Rahmim et al., 2022). This approach improves robustness and reproducibility over single-parameter fingerprints.

Approach	Fingerprint Dimensionality	Selection/Adaptation
Hand-crafted	Dozens–hundreds	Population-level, fixed
Deep-learned (CNN)	Hundreds–thousands	End-to-end learned, fixed
Patient-specific	Dozens–hundreds (sparse)	Dynamic per patient
Multiparametric/Tensor	Many hundreds–thousands	Multi-flavour fusion

3. Algorithmic Formulations and Training

Radiomic fingerprint pipelines typically comprise the following algorithmic modules:

1. Preprocessing: Intensity normalization, resampling, denoising, bias correction (Afshar et al., 2018, Parekh et al., 2018).
2. Segmentation: Manual, semi-automatic, or neural network-based ROI/VOI determination (e.g., U-Net, promptable SAM) (Na et al., 11 Jul 2025).
3. Feature Extraction: Handcrafted (PyRadiomics) or neural network embedding (CNN, CAE); in tensor/multiparametric approaches, extract features for each flavour (Parekh et al., 2018, Rahmim et al., 2022).
4. Selection/Fingerprint Formation: Reproducibility filtering, relevance scoring via sparsity (e.g., $l_1$ penalties), classifier-driven selection, or per-patient relevance network (Chen et al., 25 Jun 2025, Chen et al., 13 Jan 2026).
5. Modeling: Linear/logistic regression, SVM, Random Forest, deep fusion networks (e.g., TR-Net), Cox proportional hazards, or contrastive embedding (Afshar et al., 2018, Chaddad et al., 2019).
6. Validation: Stratified cross-validation, ROC/AUC, sensitivity, specificity, and interpretability analyses (Shafiee et al., 2015, Chen et al., 25 Jun 2025).

Patient-specific fingerprinting uses neural selectors $G_\omega(x)$ to assign feature relevance scores $s \in [0,1]^D$, typically followed by thresholding to form individualized fingerprints (Chen et al., 25 Jun 2025). In contrast, multiparametric fingerprints require tensor aggregation $T \in \mathbb{R}^{F \times N^f}$ with downstream ML/DL fusion (TR-Net or SVM) (Rahmim et al., 2022).

4. Applications and Empirical Performance

Radiomic fingerprints have been validated across a spectrum of clinical imaging tasks including cancer detection, prognosis, lesion retrieval, and treatment response prediction:

• Lung CT (malignancy prediction): StochasticNet fingerprints achieved sensitivity 91.07%, specificity 75.98%, and accuracy 84.49% on LIDC-IDRI, surpassing both autoencoder (DARS) and belief decision tree baselines (Shafiee et al., 2015).
• Knee MRI (diagnosis): Patient-specific fingerprints outperformed or matched end-to-end DL models for abnormality, ACL tear, and meniscus tear detection, with AUCs up to 0.92 (Chen et al., 25 Jun 2025, Chen et al., 13 Jan 2026).
• Glioblastoma MRI (survival): Deep radiomic fingerprints achieved AUC 89.15% vs. 78.07% with hand-crafted features, and stratified predicted survival groups (Chaddad et al., 2019).
• Breast MRI/Cerebrovascular Imaging: Multiparametric fingerprints (MPRAD) raised AUC by 9–28% over single-parameter radiomics, demonstrating tissue specificity and lesion-type invariance (Parekh et al., 2018).
• Image Retrieval: Fingerprints combining handcrafted features and deep embeddings, aligned through contrastive learning and anatomical position embedding, support volume- or attribute-specific retrieval in CT/MRI repositories (Na et al., 11 Jul 2025).

5. Interpretability, Biological Validation, and Clinical Relevance

Interpretability of radiomic fingerprints is addressed via:

• Visualization of deep network filters and activation maps, which may highlight features corresponding to biologically relevant structures (e.g., spiculations, necrosis, tissue heterogeneity) (Shafiee et al., 2015, Chen et al., 13 Jan 2026).
• Feature selection transparency in patient-specific models, where top-ranked features can be traced anatomically and associated with plausible lesion characteristics (e.g., entropy, compactness, uniformity) (Chen et al., 25 Jun 2025).
• Biological validation, such as correlation with histopathology or analogous histomic features, demonstrating that subsets of radiomic features stably reflect underlying tissue architecture (e.g., FLAIR/T1+C first-order features with $\rho > 0.65$ to histomics) (Bobholz et al., 2019).
• Use of healthy-persona residuals (difference between pathological and reconstructed “healthy” features) for intuitive, case-specific explanations of disease (Chen et al., 13 Jan 2026).

For clinical impact, the integration of fingerprints into radiology workflows is feasible via PACS-ready software modules, and prospective cohort validation is essential for generalizability across acquisition protocols and populations (Shafiee et al., 2015).

6. Methodological Challenges and Opportunities

Challenges include:

• Reproducibility and Harmonization: Radiomic fingerprint stability depends on acquisition protocol, reconstruction kernel, and segmentation; harmonization methods (e.g., RadiomicGAN with dynamic window-based training) greatly improve inter-protocol agreement of feature distributions, enabling multi-center studies (Selim et al., 2021).
• High-dimensionality versus Sample Size: Overfitting is a risk when the dimension of the fingerprint exceeds the training cohort size; dimensionality reduction, sparsity regularization, or multi-flavour aggregation (with per-flavour feature selection) is essential (Afshar et al., 2018, Rahmim et al., 2022).
• Standardization: Consistent quantization, feature nomenclature, ROI definition, and open pipelines are critical for reproducibility and deployment (Afshar et al., 2018, Parekh et al., 2018).
• Interpretability: Transparent models and feature-level explanations are required for clinical acceptance; the field is moving toward patient-tailored, interpretable embeddings backed by biological plausibility (Chen et al., 25 Jun 2025, Chen et al., 13 Jan 2026).
• Extensibility: Radiomic fingerprints are increasingly integrated with genomics, clinical data, and multimodal imaging to construct composite biomarkers (“radiogenomics”) (Afshar et al., 2018).

Opportunities encompass weak/semi-supervised learning, transfer learning, robust multi-modal fusion, and prospective evaluation in population-level studies.

7. Summary Table: Radiomic Fingerprints Across Paradigms

Paradigm	Definition of Fingerprint	Selection	Interpretability	Key Performance Example
Hand-crafted, fixed	Subset (via LASSO, stability) of raw features	Population-level	High	78% AUC rGBM survival (Chaddad et al., 2019)
Deep-learned (discovery)	Embedding from CNN/CAE	End-to-end learned	Filter-level (low)	84.5% accuracy lung CT (Shafiee et al., 2015)
Patient-specific selector	Sparse, per-patient weighted/selected subset	Neural net-guided	Anatomical-feature	AUC up to 0.92 in knee MRI (Chen et al., 25 Jun 2025)
Multiparametric/tensor radiomics	Multi-flavour feature tensor	ML/DL fusion	Variable	+5% absolute accuracy HN survival (Rahmim et al., 2022)
Retrieval-aligned embedding	Unified latent space for image & features	Contrastive DL	Attribute-level	P@5=0.91 for MRI tumor region search (Na et al., 11 Jul 2025)

Radiomic fingerprints constitute a principled, high-information imaging signature that bridges statistical rigor, interpretability, and clinical validity, enabling robust, individualized, and generalizable quantitative analysis of medical images across modalities and applications.