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Radiomics to better characterize small renal masses.
Kuusk, Teele; Neves, Joana B; Tran, Maxine; Bex, Axel.
Afiliación
  • Kuusk T; Urology Department, Darent Valley Hospital, Dartford and Gravesham NHS Trust, Dartford, UK.
  • Neves JB; Specialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK.
  • Tran M; Specialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK.
  • Bex A; Specialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK.
World J Urol ; 39(8): 2861-2868, 2021 Aug.
Article en En | MEDLINE | ID: mdl-33495866
ABSTRACT

PURPOSE:

Radiomics is a specific field of medical research that uses programmable recognition tools to extract objective information from standard images to combine with clinical data, with the aim of improving diagnostic, prognostic, and predictive accuracy beyond standard visual interpretation. We performed a narrative review of radiomic applications that may support improved characterization of small renal masses (SRM). The main focus of the review was to identify and discuss methods which may accurately differentiate benign from malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat (fat-poor AML) and oncocytoma. Furthermore, prediction of grade, sarcomatoid features, and gene mutations would be of importance in terms of potential clinical utility in prognostic stratification and selecting personalised patient management strategies.

METHODS:

A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 September 2020 to identify the English literature relevant to radiomics applications in renal tumour assessment. In total, 42 articles were included in the analysis in 3 main categories related to SRM prediction of benign versus malignant SRM, subtypes, and nuclear grade, and other features of aggressiveness.

CONCLUSION:

Overall, studies reported the superiority of radiomics over expert radiological assessment, but were mainly of retrospective design and therefore of low-quality evidence. However, it is clear that radiomics is an attractive modality that has the potential to improve the non-invasive diagnostic accuracy of SRM imaging and prediction of its natural behaviour. Further prospective validation studies of radiomics are needed to augment management algorithms of SRM.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Radiología / Inteligencia Artificial / Medicina de Precisión / Neoplasias Renales Tipo de estudio: Diagnostic_studies / Prognostic_studies / Systematic_reviews Límite: Humans Idioma: En Revista: World J Urol Año: 2021 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Radiología / Inteligencia Artificial / Medicina de Precisión / Neoplasias Renales Tipo de estudio: Diagnostic_studies / Prognostic_studies / Systematic_reviews Límite: Humans Idioma: En Revista: World J Urol Año: 2021 Tipo del documento: Article País de afiliación: Reino Unido
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