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Imaging, radiomics, and genomics advances supported by artificial intelligence can improve risk stratification, prognosis estimation, and the choice of therapeutic and surveillance strategies. 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The article highlights over-diagnosis and over-treatment risks and notes non-diagnostic rates at re-biopsy can be significant.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What clinical value does radiogenomics aim to provide?",{"text":117,"@type":113},"Radiogenomics combines quantitative imaging data with individual genetic characteristics to support risk stratification, treatment selection, follow-up strategy, and prognosis assessment. The text positions it as a tool to improve RCC management decisions.",{"name":119,"@type":110,"acceptedAnswer":120},"What are the current limitations of radiogenomics evidence?",{"text":121,"@type":113},"Evidence is constrained by retrospective study designs and small patient numbers in clinical trials. As a result, previously obtained results need validation through larger, well-designed prospective studies.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},380934,1790783487,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},1099513958607,"https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399","Review  \nRadiogenomics in Renal Cancer Management—Current Evidence and Future Prospects  \nMatteo Ferro 1,2, *, Gennaro Musi 1,2, Michele Marchioni 3,4,5, Martina Maggi 6, Alessandro Veccia 7, Francesco Del Giudice 6, Biagio Barone 8, Felice Crocetto 8, Francesco Lasorsa 9, Alessandro Antonelli 7, Luigi Schips 3,4,5, Riccardo Autorino 10, Gian Maria Busetto 11, Daniela Terracciano 12, Giuseppe Lucarelli 9 and Octavian Sabin Tataru 13  \nCitation: Ferro, M.; Musi, G.;  \nMarchioni, M.; Maggi, M.; Veccia, A.; Del Giudice, F.; Barone, B.; Crocetto, F.; Lasorsa, F.; Antonelli, A.; et al. Radiogenomics in Renal Cancer Management—Current Evidence and Future Prospects. Int. J. Mol. Sci.  \n2023, 24, 4615. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/ijms24054615](10.3390/ijms24054615)  \nAcademic Editors: Peter J.K. Kuppen, Carmine Stolfi and Massimo Nabissi  \nReceived: 2 February 2023  \nRevised: 21 February 2023  \nAccepted: 22 February 2023  \nPublished: 27 February 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Urology, European Institute of Oncology (IEO) IRCCS, 20141 Milan, Italy  \n2 Department of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, Italy  \n3 Department of Medical, Oral and Biotechnological Sciences, G. d'Annunzio, University of Chieti,  \n66100 Chieti, Italy  \n4 Urology Unit, SS. Annunziata Hospital, 66100 Chieti, Italy  \n5 Department of Urology, ASL Abruzzo 2, 66100 Chieti, Italy  \n6 Department of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, University of Rome, 00161 Rome, Italy  \n7 Department of Urology, Azienda Ospedaliera Universitaria Integrata of Verona, University of Verona, 37126 Verona, Italy  \n8 Department of Neurosciences and Reproductive Sciences and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy  \n9 Urology, Andrology and Kidney Transplantation Unit, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari Aldo Moro, 70124 Bari, Italy  \n10 Department of Urology, Rush University, Chicago, IL 60612, USA  \n11 Department of Urology and Renal Transplantation, University of Foggia, 71122 Foggia, Italy  \n12 Department of Translational Medical Sciences, University of Naples Federico II, 80131 Naples, Italy  \n13 Department of Simulation Applied in Medicine, The Institution Organizing University Doctoral Studies (I.O.S.U.D.), George Emil Palade University of Medicine, Pharmacy, Sciences, and Technology of T¥rgu Mures,, 540142 T¥rgu Mures,, Romania  \n* [Correspondence: matteo.ferro@ieo.it](Correspondence: matteo.ferro@ieo.it)  \nAbstract: Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.  \nKeywords: renal cancer; radiomics; radiogenomics; genomics; artificial intelligence; machine learning  \n1. Introduction  \nRenal cell carcinoma (RCC) is one of the most common solid tumors in bo","cbCaihyWnPHTxMLh","https://ap.wps.com/l/cbCaihyWnPHTxMLh","pdf",757891,25,"English","# Introduction\n## Clinical challenges in renal cancer management\n## Limits of imaging and biopsy in small renal masses\n## Biomarkers, scoring systems, and prediction accuracy\n## Radiogenomics and AI-enabled risk stratification","[{\"question\":\"Why is diagnosis difficult in small renal masses and cystic lesions?\",\"answer\":\"Differentiating benign versus malignant tissue can be challenging, and relying on imaging—or even renal biopsy—may lead to diagnostic pitfalls. The article highlights over-diagnosis and over-treatment risks and notes non-diagnostic rates at re-biopsy can be significant.\"},{\"question\":\"What clinical value does radiogenomics aim to provide?\",\"answer\":\"Radiogenomics combines quantitative imaging data with individual genetic characteristics to support risk stratification, treatment selection, follow-up strategy, and prognosis assessment. The text positions it as a tool to improve RCC management decisions.\"},{\"question\":\"What are the current limitations of radiogenomics evidence?\",\"answer\":\"Evidence is constrained by retrospective study designs and small patient numbers in clinical trials. As a result, previously obtained results need validation through larger, well-designed prospective studies.\"}]","Radiogenomics in Renal Cancer Management—Current Evidence and Future Prospects | PDF",1790242879,63]