Alessandro Stefano

Tumour hypoxia is a major cause of radiotherapy (RT) failure and is associated with increased tumour biological aggressiveness, an increased risk of recurrence, and reduced patient survival. Oxygen plays a fundamental role in RT effectiveness. Ionizing radiation exerts its cytotoxic effect both directly, by inducing DNA damage, and indirectly, through the production of reactive oxygen species (ROS). Under normoxic conditions, oxygen reacts with radiation-generated free radicals, stabilizing the DNA damage and making it irreversible. Conversely, in hypoxic cells, some of the radiation-induced damage can be repaired, resulting in a significant reduction in therapeutic efficacy (Oxygen Enhancement Ratio—OER). In addition to its direct effect on radiation sensitivity, hypoxia induces profound molecular changes driven by the hypoxia-inducible factor 1α (HIF-1α), thus promoting glycolytic metabolism and contributing to the acquisition of more invasive and metastatic characteristics. Moreover, hypoxia increases DNA repair capacity, cell cycle disruption, the selection of more aggressive cell clones and the induction of an immunosuppressive microenvironment, causing radioresistance phenotype. In recent years, several strategies, such as the use of high linear energy transfer (LET) radiation, radiosensitizers, as well as drugs directed against hypoxia-associated molecular target have been developed to counteract tumour hypoxia and improve its radiosensitivity. At the same time, advanced molecular imaging techniques now allow us to identify and quantify hypoxic regions within the tumour, promoting personalized therapeutic approaches. Hypoxia represents a crucial determinant of tumour radioresistance. Understanding the biological mechanisms that regulate it not only explains the reduced efficacy of RT in many solid tumours but also offers new opportunities to develop targeted therapeutic strategies capable of improving local disease control and patient clinical outcomes.

Tumour hypoxia is a major cause of radiotherapy (RT) failure and is associated with increased tumour biological aggressiveness, an increased risk of recurrence, and reduced patient survival. Oxygen plays a fundamental role in the effectiveness of RT. Ionising radiation exerts its cytotoxic effect both directly, by inducing DNA damage, and indirectly, through the production of reactive oxygen species (ROS). Under normoxic conditions, oxygen reacts with radiation-generated free radicals, stabilising the DNA damage and making it irreversible. Conversely, in hypoxic cells, some of the radiation-induced damage can be repaired, resulting in a significant reduction in therapeutic efficacy (Oxygen Enhancement Ratio - OER). In addition to its direct effect on radiation sensitivity, hypoxia induces profound molecular changes driven by the hypoxia-inducible factor 1α (HIF-1α), thus promoting glycolytic metabolism and contributing to the acquisition of more invasive and metastatic characteristics. Moreover, hypoxia increases DNA repair capacity, cell cycle disruption, the selection of more aggressive cell clones and the induction of an immunosuppressive microenvironment, causing radioresistance phenotype. In recent years, several strategies, such as the use of high linear energy transfer (LET) radiation, radiosensitizers, as well as drugs directed against hypoxia-associated molecular target have been developed to counteract tumour hypoxia and improve its radiosensitivity. At the same time, advanced molecular imaging techniques now allow us to identify and quantify hypoxic regions within the tumour, promoting personalized therapeutic approaches. Hypoxia represents a crucial determinant of tumour radioresistance. Understanding the biological mechanisms that regulate it not only explains the reduced efficacy of RT in many solid tumours but also offers new opportunities to develop targeted therapeutic strategies capable of improving local disease control and patient clinical outcomes.

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.

Background: 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 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 with 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 accuracy of 80.28% and a mean AUC of 71.95%. With SVM, they reached a mean accuracy of 79.04% and a mean AUC of 69.41%, 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 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.

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 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.

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.