Radiomics is reshaping quantitative imaging by converting biomedical images into high-dimensional biomarkers that capture biological, molecular, and functional processes. While its clinical adoption—particularly in oncology—has accelerated, preclinical applications are rapidly expanding, driven by high-resolution imaging technologies and the need for robust quantitative frameworks to support translational research. Preclinical imaging spans micro-computed tomography (micro-CT), high-field magnetic resonance imaging (MRI), micro-positron emission tomography (micro-PET), micro-single-photon emission computed tomography (SPECT), and emerging modalities such as optical and photoacoustic imaging, applied across diverse experimental models from murine systems and patient-derived xenografts (PDX) to zebrafish and 3D cellular platforms. Despite its promise, preclinical radiomics faces challenges including variability in acquisition and reconstruction, limited sample sizes, lack of standardized workflows, and difficulties in biological interpretation. This review provides a systematic overview of current literature, organized by imaging modality and biological model, highlighting methodological patterns, shared limitations, and emerging opportunities. Special attention is given to harmonization strategies, publicly available datasets, and integrated software platforms that enable reproducible pipelines and lower technical barriers. Ultimately, this work delineates key directions to enhance reproducibility and accelerate the translation of preclinical radiomics into clinically meaningful applications.

Melanoma is, nowadays, the most aggressive kind of skin tumor. Even if advanced therapies and diagnosis techniques have been developed, metastatic cases still have a poor prognosis. Diagnosis of melanoma is complicated and confounded by the histopathological heterogeneity in samples and the absence of affordable biomarkers. The advent of innovative analytical techniques in medical imaging, such as radiomics and pathomics, poses hope in providing the needed assistance in diagnostic accuracy. In this paper, a preliminary radiomic workflow has been proposed for a subset of 100 annotated Regions of Interest (ROIs) from the PUMA dataset, comprising primary and metastatic melanoma samples. The goal was to differentiate tumor nuclei from non-tumor nuclei using radiomic features extracted from Whole Slide Images (WSIs). Feature extraction was performed using PyRadiomics, and LASSO regression was applied to carry out feature selection and reduce the features count from 102 to 57. The selected features were used to train a Random Forest classifier, whose output was assessed in terms of accuracy, precision, recall, specificity, F1-score, and area under the ROC curve (AUC). The model exhibited 74.56

Background To develop, validate, and externally test a radiomics-based machine learning model for differentiating metastatic from non-metastatic bone uptake on fluorine-18 prostate-specific membrane antigen-1007 positron emission tomography/computed tomography (PET/CT) in prostate cancer. Methods In this retrospective multicenter study, 285 patients with PCa from three tertiary referral centres were included. A total of 435 focal bone uptakes were segmented using a semi-automatic SUVmax-based approach. Radiomics features were extracted from PET and CT images using an Image Biomarker Standardisation Initiative-compliant software. Feature selection was performed using elastic net penalised regression, and selected features were used to train a multilayer perceptron classifier. Data were split at the patient level into training, validation, and independent external test sets. Model performance was evaluated using balanced accuracy, area under the receiver operating characteristic curve, precision, sensitivity, and F1 score. Results Of 3562 extracted radiomics features, 46 were retained after feature selection. On the validation set, the ensemble model achieved a balanced accuracy of 83.16% and an area under the curve of 90.19%. On the independent external test set, model performance remained robust, with a balanced accuracy of 78.42% and an area under the curve of 84.82%. Conclusion Radiomics-based machine learning enabled accurate differentiation of metastatic and non-metastatic bone uptake on fluorine-18 prostate-specific membrane antigen–1007 PET/CT in a multicenter cohort with independent external validation.

Breast cancer remains the second leading cause of cancer-related mortality among women worldwide. Early detection and accurate classification of breast lesions, including masses and microcalcifications, are therefore essential to improving diagnostic precision and patient outcomes. This study investigates the performance of three supervised machine learning (ML) classifiers—Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and K-Nearest Neighbours (KNN)—in the context of a radiomics-based approach to mammographic image classification. The analysis was conducted using the matRadiomics toolbox on 1,219 mammograms obtained from the publicly available CBIS-DDSM dataset. All three classifiers—LDA, SVM, and KNN—were tested on the same mammographic subsets of masses and microcalcifications. LDA achieved the highest overall performance in terms of AUC (69.7

We investigate a quantum-inspired approach to supervised multi-class classification based on the Pretty Good Measurement (PGM), viewed as an operator-valued decision rule derived from quantum state discrimination. The method associates each class with an encoded mixed state and performs classification through a single POVM construction, thus providing a genuinely multi-class strategy without reduction to pairwise or one-vs-rest schemes. In this perspective, classification is reformulated as the discrimination of a finite ensemble of class-dependent density operators, with performance governed by the geometry induced by the encoding map and by the overlap structure among classes. To assess the practical scope of this framework, we apply the PGM-based classifier to two biomedical radiomics case studies: histopathological subtyping of non-small-cell lung carcinoma (NSCLC) and prostate cancer (PCa) risk stratification. The evaluation is conducted under protocols aligned with previously reported radiomics studies, enabling direct comparison with established classical baselines. The results show that the PGM-based classifier is consistently competitive and, in several settings, improves upon standard methods. In particular, the method performs especially well in the NSCLC binary and three-class tasks, while remaining competitive in the four-class case, where increased class overlap yields a more demanding discrimination geometry. In the PCa study, the PGM classifier remains close to the strongest ensemble baseline and exhibits clinically relevant sensitivity–specificity trade-offs across feature-selection scenarios. These findings support the relevance of PGM-based quantum-inspired decision rules as a mathematically well-motivated and practically viable extension of quantum state discrimination techniques to genuinely multi-class learning problems and provide further evidence of their applicability in high-dimensional biomedical settings.

Quantitative preclinical imaging enables non-invasive characterization of physiological, molecular, and functional processes providing measurable biomarkers for longitudinal and translational studies. This review systematically analyzes 60 studies published between 2015 and 2025, covering major imaging modalities including Positron emission tomography (PET), Single-Photon Emission Computed Tomography (SPECT), Magnetic resonance imaging (MRI), Computed Tomography (CT), optical imaging, and hybrid systems across murine and zebrafish models. We examine methodological frameworks for parameter extraction, reproducibility, and validation against biological reference standards, evaluating each modality through a cross-cutting analytical framework that distinguishes technical, biological, and computational sources of quantitative variance and identifies the current metrological maturity of harmonization infrastructure across platforms. Key comparative findings indicate that variability sources can be broadly categorized into technical (instrumentation, reconstruction, calibration) and biological (physiological heterogeneity, model-specific factors), with their interaction governing overall measurement uncertainty. Emerging computational approaches, including parametric modeling and artificial intelligence–assisted pipelines, show potential in reducing variance and improving parameter stability, although they introduce additional dependencies requiring validation. Collectively, this review frames quantitative preclinical imaging as a metrological discipline, emphasizing that reproducibility, bias control, and cross-modality harmonization are critical for generating robust and translationally relevant imaging biomarkers.

Background/Objectives: Prostate cancer (PCa) frequently metastasizes to bone, leading to severe clinical complications and reduced quality of life. Accurate and robust imaging-based characterization of bone lesions is therefore critical for diagnosis and treatment planning. Radiomics has emerged as a powerful tool for extracting quantitative information from medical images; however, classical radiomics features are often affected by inter-scanner variability, segmentation dependence, and limited ability to describe lesions with complex biological heterogeneity. This study aims to introduce a translational graph-based radiomics approach designed to extract novel quantitative descriptors with improved robustness and clinical reliability. Methods: A graph representation was derived from segmented Positron Emission Tomography/Computed Tomography (PET/CT) bone lesions by generating a point cloud followed by Delaunay triangulation to preserve geometric information. Graph signal processing techniques were applied to extract three classes of features: orientation, connectivity, and transform-based descriptors. The dataset included PET/CT scans from 50 PCa patients acquired using two different scanners, comprising 92 bone lesions classified as benign or malignant. Correlation analysis with classical radiomics features was performed to assess information redundancy. Robustness against batch effects and segmentation variability was evaluated. Classification performance was tested using Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) models based on proposed features, classical features, and their combination. Results: The proposed features captured non-redundant information compared to classical radiomics and demonstrated superior robustness to scanner-related batch effects and segmentation variability. In classification tasks, models using the proposed features consistently outperformed those based on classical radiomics. Using LDA, the proposed features achieved a mean balanced accuracy of 69.68% and a mean Area Under the Curve (AUC) of 72.14%. With SVM, they achieved a mean balanced accuracy of 65.16% and a mean AUC of 66.49%, exceeding the performance of classical and combined feature sets. Conclusions: This study presents a translational graph-based radiomics framework that extends beyond conventional methodologies, improving robustness and diagnostic performance. The proposed approach shows promise as an integrative tool for more reliable PET/CT-based characterization of bone lesions in prostate cancer.

Quantitative preclinical imaging enables non-invasive characterization of physiological, molecular, and functional processes across a variety of experimental models, providing metrics that inform longitudinal studies and translational research. This review synthesizes current strategies for quantitative imaging across modalities including Positron emission tomography (PET), Single Photon Emission Computed Tomography (SPECT), Magnetic resonance imaging (MRI), Computed Tomography (CT), optical imaging, and hybrid systems. We examine methodological frameworks for parameter extraction, reproducibility, and validation against biological reference standards, evaluating each modality through a cross-cutting analytical framework that distinguishes technical, biological, and computational sources of quantitative variance and identifies the current metrological maturity of harmonization infrastructure across platforms. Key challenges, such as protocol harmonization, cross-platform comparability, and integration across species, are analyzed, alongside computational advances including parametric mapping, and artificial intelligence–assisted pipelines. Emerging approaches that combine multimodal acquisition with standardized reconstruction and calibration strategies are also discussed, emphasizing their potential to enhance precision, reduce bias, and support biologically meaningful interpretation. Collectively, this review provides a comprehensive perspective on the design, implementation, and validation of quantitative preclinical imaging studies, offering practical guidance for generating reproducible, interpretable, and translationally relevant imaging biomarkers. By integrating methodological insights with advances in technology and analytics, it underscores the role of quantitative frameworks in bridging preclinical discovery with translational applications.

This study proposes a fully automatic, patch-based pipeline for the classification and localization of masses and microcalcifications in mammographic images. Leveraging the EfficientNetB6 convolutional neural network, the method classifies image patches into one of three categories, healthy tissue, mass, or calcification, based on their lesion content. The CBIS-DDSM dataset was used, containing over 3500 annotated images. Preprocessing included removing irrelevant content, enhancing image quality through normalization, filtering, and contrast enhancement. Patches (528 × 528) were extracted with 50

BackgroundThe analysis of histopathological characteristics from biopsy whole slide images (WSI) is a standard procedure in current diagnostic workflows. For instance, malignancies such as melanoma often require the execution of biopsy to be accurately identified. However, diagnosis can be difficult because of variability in clinical scenarios and in microscopic pictures, as well as the lack of biomarkers availability. In this context, the extraction of shape, texture, and intensity-based features from medical images has proven to be a very promising strategy to uncover latent patterns that may be helpful for diagnosis and prediction of several pathologies.MethodsThis study proposes radiomics as a powerful tool for extracting nuclei features and enabling nuclei classification of PUMa dataset melanoma WSIs. More specifically, it evaluates the extraction of radiomics features through PyRadiomics, in comparison with the pathomics tool, namely HistomicsTK, in terms of classification performance. To systematically compare these approaches, three supervised classifiers were trained and tested using the same training/testing splits and usual classification metrics: one on radiomics features, one on histomic features, and one on the merged feature set.ResultsThe results illustrate an improved performance of the radiomics model compared with both the histomic model and the hybrid radiomics and histomics model, suggesting that radiomics can extract valuable phenotypic information from histological images.ConclusionsRadiomics-based feature extraction, as implemented in PyRadiomics, may be a valid and robust alternative to histomics/pathomics descriptors implemented in HistomicsTK in computational pathology pipelines for melanoma analysis.

BACKGROUND AND OBJECTIVE:Genomic characterization of metastatic prostate cancer (mPCa) plays a pivotal role in guiding precision oncology. This study aimed to evaluate the feasibility of combining radiomics and clinical data within a machine learning (ML) framework to non-invasively predict key genomic mutations in patients with mPCa undergoing PSMA PET imaging. METHODS:A retrospective cohort of 14 mPCa patients who underwent [18 F]PSMA-1007 PET/CT was analysed. Prostate and metastatic lesions were segmented, and radiomics features were extracted. Somatic genomic alterations were obtained from formalin-fixed paraffin-embedded tissue samples using FoundationOne CDx testing. Six ML algorithms - Discriminant Analysis, Support Vector Machines, K-Nearest Neighbours, Neural Networks, Random Forest, and Boosting - were trained using a 5-times repeated pipeline with 80/20 train/test split, LASSO feature selection, and 5-fold cross-validation. Model performance was assessed using accuracy, AUC, sensitivity, specificity, precision, and F-score. KEY FINDINGS:Fourteen patients with mPCa were included, and 46 lesions were analysed. Genomic alterations included mutations in TP53, TMPRSS2, PTEN, BRCA1/2, ATM, and others. Owing to data limitations, mutations other than TP53, TMPRSS2, and PTEN were grouped into a composite "OTHER" category. The best-performing clinical-radiomics ML models achieved AUCs of 91.11% (TP53), 84.44% (TMPRSS2), 80.00% (PTEN), and 77.78% (OTHER). Selected feature stability was consistent across repeated runs. CONCLUSIONS AND CLINICAL IMPLICATIONS:Clinical-radiomics ML models based on PSMA PET imaging show promising accuracy in predicting actionable genomic alterations in mPCa. These findings support further investigation into radiogenomics modelling as a complementary, non-invasive tool to inform molecular profiling and treatment stratification.

This study investigates the effectiveness of machine learning (ML)-based radiomics in classifying mammographic lesions. Leveraging the publicly available CBIS-DDSM and the matRadiomics toolbox, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) models were tested across progressively larger training sets. LDA achieved the highest performance, demonstrating excellent discrimination between masses and calcifications (AUC of 97.08%, Accuracy of 95.63%). However, classification of benign versus malignant lesions yielded lower AUCs of 68.28% for microcalcifications and 61.53% for masses highlighting the limitations of traditional ML approaches. These findings point toward the need for more advanced methods, such as deep learning, in future research.

We hypothesised that applying radiomics to [18F]PSMA-1007 PET/CT images could help distinguish Unspecific Bone Uptakes (UBUs) from bone metastases in prostate cancer (PCa) patients. We compared the performance of radiomic features to human visual interpretation. We retrospectively analysed 102 hormone-sensitive PCa patients who underwent [18F]PSMA-1007 PET/CT and exhibited at least one focal bone uptake with known clinical follow-up (reference standard). Using matRadiomics, we extracted features from PET and CT images of each bone uptake and identified the best predictor model for bone metastases using a machine-learning approach to generate a radiomic score. Blinded PET readers with low (n = 2) and high (n = 2) experience rated each bone uptake as either UBU or bone metastasis. The same readers performed a second read three months later, with access to the radiomic score. Of the 178 [18F]PSMA-1007 bone uptakes, 74 (41.5

Introduction: As adequate perfusion has turned out to be a key determinant of adipose tissue (AT) remodeling, interest has grown regarding possible pharmacological interventions to promote this process and hence prevent the metabolic abnormalities associated with obesity and aging. Clinical trials have shown that mirabegron improves insulin sensitivity and glucose homeostasis in obese humans via stimulation of β 3 -adrenoceptors. Ligands of these receptors have also emerged as endothelium-dependent vasodilators in disparate human vascular beds. Hypothesis: We hypothesized, therefore, that mirabegron might exert vasodilator effect in human AT arteries and tried to characterize the underlying mechanism(s). Methods: Small arteries (116-734 μm) isolated from visceral AT were studied ex vivo in a wire myograph. After vessels had been contracted, changes in vascular tone in response to mirabegron were determined under different experimental conditions. Results: Mirabegron elicited vasorelaxation in vessels contracted with endothelin-1 (P<0001), but not in those contracted with U46619 or high-K + (both P>0.05). Notably, mirabegron markedly blunted the contractile effect of the α 1 -adrenergic receptor agonist phenylephrine (P<0.001). Also, vessels with removed endothelium contracted with phenylephrine had preserved vasorelaxing response to mirabegron. The anti-contractile action of mirabegron on phenylephrine-induced vasoconstriction was not influenced by the presence of the selective β 3 -adrenoceptor blocker L-748,337 (P<0.05); lack of involvement of β 3 -adrenoceptors was further supported by absent vascular staining for them at immunohistochemistry. Similar to the observed effect of mirabegron, preincubation with the α 1 -adrenoceptor antagonist doxazosin abolished the contractile response to phenylephrine. Conclusions: Mirabegron induces endothelium-independent vasorelaxation in arteries from visceral adipose tissue, likely through antagonism of α 1 -adrenoceptors. This action suggests that mirabegron might effectively improve visceral AT perfusion, thereby favoring a healthy AT remodeling and preventing, in turn, some of the unwanted cardiometabolic consequences of obesity and aging.

Background: Breast cancer is the second leading cause of cancer-related mortality among women, accounting for 12% of cases. Early diagnosis, based on the identification of radiological features, such as masses and microcalcifications in mammograms, is crucial for reducing mortality rates. However, manual interpretation by radiologists is complex and subject to variability, emphasizing the need for automated diagnostic tools to enhance accuracy and efficiency. This study compares a radiomics workflow based on machine learning (ML) with a deep learning (DL) approach for classifying breast lesions as benign or malignant. Methods: matRadiomics was used to extract radiomics features from mammographic images of 1219 patients from the CBIS-DDSM public database, including 581 cases of microcalcifications and 638 of masses. Among the ML models, a linear discriminant analysis (LDA) demonstrated the best performance for both lesion types. External validation was conducted on a private dataset of 222 images to evaluate generalizability to an independent cohort. Additionally, a deep learning approach based on the EfficientNetB6 model was employed for comparison. Results: The LDA model achieved a mean validation AUC of 68.28% for microcalcifications and 61.53% for masses. In the external validation, AUC values of 66.9% and 61.5% were obtained, respectively. In contrast, the EfficientNetB6 model demonstrated superior performance, achieving an AUC of 81.52% for microcalcifications and 76.24% for masses, highlighting the potential of DL for improved diagnostic accuracy. Conclusions: This study underscores the limitations of ML-based radiomics in breast cancer diagnosis. Deep learning proves to be a more effective approach, offering enhanced accuracy and supporting clinicians in improving patient management.

For decades, wavelet theory has attracted interest in several fields in dealing with signals. Nowadays, it is acknowledged that it is not very suitable to face aspects of multidimensional data like singularities and this has led to the development of other mathematical tools. A recent application of wavelet theory is in radiomics, an emerging field aiming to improve diagnostic, prognostic and predictive analysis of various cancer types through the analysis of features extracted from medical images. In this paper, for a radiomics study of prostate cancer with magnetic resonance (MR) images, we apply a similar but more sophisticated tool, namely the shearlet transform which, in contrast to the wavelet transform, allows us to examine variations along more orientations. In particular, we conduct a parallel radiomics analysis based on the two different transformations and highlight a better performance (evaluated in terms of statistical measures) in the use of the shearlet transform (in absolute value). The results achieved suggest taking the shearlet transform into consideration for radiomics studies in other contexts.

Purpose: To evaluate the role of radiomics in preoperative outcome prediction in cirrhotic patients who underwent transjugular intrahepatic portosystemic shunt (TIPS) using “controlled expansion covered stents”. Materials and Methods: This retrospective institutional review board-approved study included cirrhotic patients undergoing TIPS with controlled expansion covered stent placement. From preoperative CT images, the whole liver was segmented into Volumes of Interest (VOIs) at the unenhanced and portal venous phase. Radiomics features were extracted, collected, and analyzed. Subsequently, receiver operating characteristic (ROC) curves were drawn to assess which features could predict patients’ outcomes. The endpoints studied were 6-month overall survival (OS), development of hepatic encephalopathy (HE), grade II or higher HE according to West Haven Criteria, and clinical response, defined as the absence of rebleeding or ascites. A radiomic model for outcome prediction was then designed. Results: A total of 76 consecutive cirrhotic patients undergoing TIPS creation were enrolled. The highest performances in terms of the area under the receiver operating characteristic curve (AUROC) were observed for the “clinical response” and “survival at 6 months” outcome with 0.755 and 0.767, at the unenhanced and portal venous phase, respectively. Specifically, on basal scans, accuracy, specificity, and sensitivity were 66.42%, 63.93%, and 73.75%, respectively. At the portal venous phase, an accuracy of 65.34%, a specificity of 62.38%, and a sensitivity of 74.00% were demonstrated. Conclusions: A pre-interventional machine learning-based CT radiomics algorithm could be useful in predicting survival and clinical response after TIPS creation in cirrhotic patients.