Radiogenomics in Renal Cancer Management—Current Evidence and Future Prospects
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, imagi...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | International Journal of Molecular Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1422-0067/24/5/4615 |
| _version_ | 1797615164295479296 |
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| author | Matteo Ferro Gennaro Musi Michele Marchioni Martina Maggi Alessandro Veccia Francesco Del Giudice Biagio Barone Felice Crocetto Francesco Lasorsa Alessandro Antonelli Luigi Schips Riccardo Autorino Gian Maria Busetto Daniela Terracciano Giuseppe Lucarelli Octavian Sabin Tataru |
| author_facet | Matteo Ferro Gennaro Musi Michele Marchioni Martina Maggi Alessandro Veccia Francesco Del Giudice Biagio Barone Felice Crocetto Francesco Lasorsa Alessandro Antonelli Luigi Schips Riccardo Autorino Gian Maria Busetto Daniela Terracciano Giuseppe Lucarelli Octavian Sabin Tataru |
| author_sort | Matteo Ferro |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T07:22:37Z |
| format | Article |
| id | doaj.art-6cf8463046464cfda0b0f8c2169dab9a |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-11T07:22:37Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-6cf8463046464cfda0b0f8c2169dab9a2023-11-17T07:50:57ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-02-01245461510.3390/ijms24054615Radiogenomics in Renal Cancer Management—Current Evidence and Future ProspectsMatteo Ferro0Gennaro Musi1Michele Marchioni2Martina Maggi3Alessandro Veccia4Francesco Del Giudice5Biagio Barone6Felice Crocetto7Francesco Lasorsa8Alessandro Antonelli9Luigi Schips10Riccardo Autorino11Gian Maria Busetto12Daniela Terracciano13Giuseppe Lucarelli14Octavian Sabin Tataru15Department of Urology, European Institute of Oncology (IEO) IRCCS, 20141 Milan, ItalyDepartment of Urology, European Institute of Oncology (IEO) IRCCS, 20141 Milan, ItalyDepartment of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, 66100 Chieti, ItalyDepartment of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, University of Rome, 00161 Rome, ItalyDepartment of Urology, Azienda Ospedaliera Universitaria Integrata of Verona, University of Verona, 37126 Verona, ItalyDepartment of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, University of Rome, 00161 Rome, ItalyDepartment of Neurosciences and Reproductive Sciences and Odontostomatology, University of Naples Federico II, 80131 Naples, ItalyDepartment of Neurosciences and Reproductive Sciences and Odontostomatology, University of Naples Federico II, 80131 Naples, ItalyUrology, Andrology and Kidney Transplantation Unit, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari Aldo Moro, 70124 Bari, ItalyDepartment of Urology, Azienda Ospedaliera Universitaria Integrata of Verona, University of Verona, 37126 Verona, ItalyDepartment of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, 66100 Chieti, ItalyDepartment of Urology, Rush University, Chicago, IL 60612, USADepartment of Urology and Renal Transplantation, University of Foggia, 71122 Foggia, ItalyDepartment of Translational Medical Sciences, University of Naples Federico II, 80131 Naples, ItalyUrology, Andrology and Kidney Transplantation Unit, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari Aldo Moro, 70124 Bari, ItalyDepartment 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 Târgu Mureș, 540142 Târgu Mureș, RomaniaRenal 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.https://www.mdpi.com/1422-0067/24/5/4615renal cancerradiomicsradiogenomicsgenomicsartificial intelligencemachine learning |
| spellingShingle | Matteo Ferro Gennaro Musi Michele Marchioni Martina Maggi Alessandro Veccia Francesco Del Giudice Biagio Barone Felice Crocetto Francesco Lasorsa Alessandro Antonelli Luigi Schips Riccardo Autorino Gian Maria Busetto Daniela Terracciano Giuseppe Lucarelli Octavian Sabin Tataru Radiogenomics in Renal Cancer Management—Current Evidence and Future Prospects International Journal of Molecular Sciences renal cancer radiomics radiogenomics genomics artificial intelligence machine learning |
| title | Radiogenomics in Renal Cancer Management—Current Evidence and Future Prospects |
| title_full | Radiogenomics in Renal Cancer Management—Current Evidence and Future Prospects |
| title_fullStr | Radiogenomics in Renal Cancer Management—Current Evidence and Future Prospects |
| title_full_unstemmed | Radiogenomics in Renal Cancer Management—Current Evidence and Future Prospects |
| title_short | Radiogenomics in Renal Cancer Management—Current Evidence and Future Prospects |
| title_sort | radiogenomics in renal cancer management current evidence and future prospects |
| topic | renal cancer radiomics radiogenomics genomics artificial intelligence machine learning |
| url | https://www.mdpi.com/1422-0067/24/5/4615 |
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