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.

Radiogenomics in Renal Cancer Management—Current Evidence and Future Prospects / M. Ferro, G. Musi, M. Marchioni, M. Maggi, A. Veccia, F. Del Giudice, B. Barone, F. Crocetto, F. Lasorsa, A. Antonelli, L. Schips, R. Autorino, G.M. Busetto, D. Terracciano, G. Lucarelli, O.S. Tataru. - In: INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES. - ISSN 1422-0067. - 24:5(2023 Feb 27), pp. 4615.1-4615.25. [10.3390/ijms24054615]

Radiogenomics in Renal Cancer Management—Current Evidence and Future Prospects

M. Ferro
Primo
;
G. Musi
Secondo
;
G. Lucarelli
Penultimo
;
2023

Abstract

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.
artificial intelligence; genomics; machine learning; radiogenomics; radiomics; renal cancer
Settore MEDS-14/C - Urologia
27-feb-2023
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1127124
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