{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T15:08:05Z","timestamp":1785424085859,"version":"3.56.0"},"reference-count":52,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute for Nuclear Physics"},{"id":[{"id":"https:\/\/ror.org\/005ta0471","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/diagnostics16142284","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T15:17:52Z","timestamp":1784647072000},"page":"2284","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing PET\/CT Radiomics Robustness Through Graph Signal Processing"],"prefix":"10.3390","volume":"16","author":[{"given":"Tommaso","family":"Latino","sequence":"first","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7189-1731","authenticated-orcid":false,"given":"Alessandro","family":"Stefano","sequence":"additional","affiliation":[{"name":"Institute of Bioimaging and Complex Biological Systems, National Research Council, 90015 Cefal\u00f9, Italy"},{"name":"National Laboratory of South, National Institute for Nuclear Physics (LNS-INFN), 95123 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8750-0731","authenticated-orcid":false,"given":"Giovanni","family":"Pasini","sequence":"additional","affiliation":[{"name":"Institute of Bioimaging and Complex Biological Systems, National Research Council, 90015 Cefal\u00f9, Italy"},{"name":"National Laboratory of South, National Institute for Nuclear Physics (LNS-INFN), 95123 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4872-2980","authenticated-orcid":false,"given":"Franco","family":"Marinozzi","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1493-1087","authenticated-orcid":false,"given":"Giorgio","family":"Russo","sequence":"additional","affiliation":[{"name":"Institute of Bioimaging and Complex Biological Systems, National Research Council, 90015 Cefal\u00f9, Italy"},{"name":"National Laboratory of South, National Institute for Nuclear Physics (LNS-INFN), 95123 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5641-1189","authenticated-orcid":false,"given":"Fabiano","family":"Bini","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2076","DOI":"10.1007\/s00259-025-07085-6","article-title":"[18F]PSMA-1007 PET\/CT-Based Radiomics May Help Enhance the Interpretation of Bone Focal Uptakes in Hormone-Sensitive Prostate Cancer Patients","volume":"52","author":"Bauckneht","year":"2025","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.3349\/ymj.2013.54.5.1202","article-title":"Role of Prostate Volume in the Early Detection of Prostate Cancer in a Cohort with Slowly Increasing Prostate Specific Antigen","volume":"54","author":"Kim","year":"2013","journal-title":"Yonsei Med. J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Laudicella, R., Spataro, A., Croc\u00e8, L., Giacoppo, G., Romano, D., Dav\u00ec, V., Lopes, M., Librando, M., Nicocia, A., and Rappazzo, A. (2023). Preliminary Findings of the Role of FAPi in Prostate Cancer Theranostics. Diagnostics, 13.","DOI":"10.3390\/diagnostics13061175"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1016\/j.eururo.2016.08.002","article-title":"EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part II: Treatment of Relapsing, Metastatic, and Castration-Resistant Prostate Cancer","volume":"71","author":"Cornford","year":"2017","journal-title":"Eur. Urol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lauciello, N., Russo, G., and Stefano, A. (2026). Radiomics in Preclinical Imaging: Current Trends and Future Directions. Clin. Transl. Imaging.","DOI":"10.1007\/s40336-026-00763-x"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"044501","DOI":"10.1117\/1.JMI.5.4.044501","article-title":"PROSTATEx Challenges for Computerized Classification of Prostate Lesions from Multiparametric Magnetic Resonance Images","volume":"5","author":"Armato","year":"2018","journal-title":"J. Med. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.urology.2018.02.035","article-title":"The Correlation of Prostate Volume and Prostate-Specific Antigen Levels With Positive Bacterial Prostate Tissue Cultures","volume":"115","author":"Heidler","year":"2018","journal-title":"Urology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.1007\/s00259-021-05599-3","article-title":"Hot Needles Can Confirm Accurate Lesion Sampling Intraoperatively Using [18F]PSMA-1007 PET\/CT-Guided Biopsy in Patients with Suspected Prostate Cancer","volume":"49","author":"Ferraro","year":"2022","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.euo.2022.03.004","article-title":"[68Ga]Ga-PSMA Versus [18F]PSMA Positron Emission Tomography\/Computed Tomography in the Staging of Primary and Recurrent Prostate Cancer. A Systematic Review of the Literature","volume":"5","author":"Evangelista","year":"2022","journal-title":"Eur. Urol. Oncol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images Are More than Pictures, They Are Data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"365","DOI":"10.2967\/jnumed.116.184655","article-title":"Radiomics in PET\/CT: More Than Meets the Eye?","volume":"58","author":"Hatt","year":"2017","journal-title":"J. Nucl. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur. J. Cancer"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s41747-022-00282-0","article-title":"Radiomics and Artificial Intelligence in Prostate Cancer: New Tools for Molecular Hybrid Imaging and Theragnostics","volume":"6","author":"Liberini","year":"2022","journal-title":"Eur. Radiol. Exp."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"31695","DOI":"10.1007\/s11042-024-20388-4","article-title":"Insights into Radiomics: Impact of Feature Selection and Classification","volume":"84","author":"Perniciano","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pasini, G., Stefano, A., Russo, G., Comelli, A., Marinozzi, F., and Bini, F. (2023). Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics?. Diagnostics, 13.","DOI":"10.3390\/diagnostics13061167"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1002\/ima.22154","article-title":"A Fully Automatic Method for Biological Target Volume Segmentation of Brain Metastases","volume":"26","author":"Stefano","year":"2016","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compbiomed.2018.09.002","article-title":"A Smart and Operator Independent System to Delineate Tumours in Positron Emission Tomography Scans","volume":"102","author":"Comelli","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.artmed.2015.07.002","article-title":"Robust Feature Selection to Predict Tumor Treatment Outcome","volume":"64","author":"Mi","year":"2015","journal-title":"Artif. Intell. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.compbiomed.2019.02.017","article-title":"Radiological Images and Machine Learning: Trends, Perspectives, and Prospects","volume":"108","author":"Zhang","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1016\/j.ijrobp.2017.12.268","article-title":"Challenges and Promises of PET Radiomics","volume":"102","author":"Cook","year":"2018","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108827","DOI":"10.1016\/j.compbiomed.2024.108827","article-title":"Challenges and Limitations in Applying Radiomics to PET Imaging: Possible Opportunities and Avenues for Research","volume":"179","author":"Stefano","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4493","DOI":"10.1038\/s41598-022-08412-9","article-title":"Generalized ComBat Harmonization Methods for Radiomic Features with Multi-Modal Distributions and Multiple Batch Effects","volume":"12","author":"Horng","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.2967\/jnumed.121.263102","article-title":"Impact of ComBat Harmonization on PET Radiomics-Based Tissue Classification: A Dual-Center PET\/MRI and PET\/CT Study","volume":"63","author":"Leithner","year":"2022","journal-title":"J. Nucl. Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational Radiomics System to Decode the Radiographic Phenotype","volume":"77","author":"Fedorov","year":"2017","journal-title":"Cancer Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1038\/ncomms5006","article-title":"Decoding Tumour Phenotype by Noninvasive Imaging Using a Quantitative Radiomics Approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., and Cousins, S. (2011). 3D Is Here: Point Cloud Library (PCL). Proceedings of the 2011 IEEE International Conference on Robotics and Automation, IEEE.","DOI":"10.1109\/ICRA.2011.5980567"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Di Salvo, E., Latino, T., Sanzone, M., Trozzo, A., and Colonnese, S. (2025). Topological Signal Processing from Stereo Visual SLAM. Sensors, 25.","DOI":"10.3390\/s25196103"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MSP.2012.2235192","article-title":"The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains","volume":"30","author":"Shuman","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-Based Phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"11044","DOI":"10.1038\/srep11044","article-title":"Radiomic Feature Clusters and Prognostic Signatures Specific for Lung and Head & Neck Cancer","volume":"5","author":"Parmar","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01621459.1952.10483441","article-title":"Use of Ranks in One-Criterion Variance Analysis","volume":"47","author":"Kruskal","year":"1952","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.1007\/s00259-010-1423-3","article-title":"PET-Guided Delineation of Radiation Therapy Treatment Volumes: A Survey of Image Segmentation Techniques","volume":"37","author":"Zaidi","year":"2010","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103701","DOI":"10.1016\/j.compbiomed.2020.103701","article-title":"Development of a New Fully Three-Dimensional Methodology for Tumours Delineation in Functional Images","volume":"120","author":"Comelli","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10766-008-0082-5","article-title":"MATLAB\u00ae: A Language for Parallel Computing","volume":"37","author":"Sharma","year":"2009","journal-title":"Int. J. Parallel Program."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/BF02187695","article-title":"Generalized Delaunay Triangulation for Planar Graphs","volume":"1","author":"Lee","year":"1986","journal-title":"Discret. Comput. Geom."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chung, F. (1996). Spectral Graph Theory, American Mathematical Society.","DOI":"10.1090\/cbms\/092"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1109\/TSP.2013.2238935","article-title":"Discrete Signal Processing on Graphs","volume":"61","author":"Sandryhaila","year":"2013","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1109\/JPROC.2018.2820126","article-title":"Graph Signal Processing: Overview, Challenges, and Applications","volume":"106","author":"Ortega","year":"2018","journal-title":"Proc. IEEE"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.acha.2010.04.005","article-title":"Wavelets on Graphs via Spectral Graph Theory","volume":"30","author":"Hammond","year":"2011","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","article-title":"Individual Comparisons by Ranking Methods","volume":"1","author":"Wilcoxon","year":"1945","journal-title":"Biom. Bull."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"169","DOI":"10.3233\/AIC-170729","article-title":"Linear Discriminant Analysis: A Detailed Tutorial","volume":"30","author":"Tharwat","year":"2017","journal-title":"AI Commun."},{"key":"ref_43","first-page":"262","article-title":"A Kernel Support Vector Machine Based Technique for Crohn\u2019s Disease Classification in Human Patients","volume":"Volume 611","author":"Comelli","year":"2018","journal-title":"Advances in Intelligent Systems and Computing"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1213\/ANE.0000000000002864","article-title":"Correlation Coefficients: Appropriate Use and Interpretation","volume":"126","author":"Schober","year":"2018","journal-title":"Anesth. Analg."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.tjem.2018.08.001","article-title":"User\u2019s Guide to Correlation Coefficients","volume":"18","author":"Akoglu","year":"2018","journal-title":"Turk. J. Emerg. Med."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1080\/00401706.1964.10490181","article-title":"Multiple Comparisons Using Rank Sums","volume":"6","author":"Dunn","year":"1964","journal-title":"Technometrics"},{"key":"ref_47","first-page":"626","article-title":"Rectangular Confidence Regions for the Means of Multivariate Normal Distributions","volume":"62","year":"1967","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e763","DOI":"10.1016\/j.ijrobp.2025.06.3217","article-title":"AI-Driven Graph-Based Radiomics: Enhancing Imaging Biomarkers Reproducibility in Multi-Institutional Head and Neck Cancer Management","volume":"123","author":"Moradmand","year":"2025","journal-title":"Int. J. Radiat. Oncol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"125359","DOI":"10.1109\/ACCESS.2025.3588817","article-title":"Graph-Based Radiomics Feature Extraction From 2D Retina Images","volume":"13","author":"Jorreia","year":"2025","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A Comprehensive Survey on Graph Neural Networks","volume":"32","author":"Wu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph Neural Networks: A Review of Methods and Applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2992","DOI":"10.1109\/TSP.2020.2981920","article-title":"Topological Signal Processing Over Simplicial Complexes","volume":"68","author":"Barbarossa","year":"2020","journal-title":"IEEE Trans. Signal Process."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/14\/2284\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T04:34:05Z","timestamp":1784781245000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/14\/2284"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,21]]},"references-count":52,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["diagnostics16142284"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16142284","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,21]]}}}