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CT-based radiomics for differentiating renal tumours: a systematic review.
Bhandari, Abhishta; Ibrahim, Muhammad; Sharma, Chinmay; Liong, Rebecca; Gustafson, Sonja; Prior, Marita.
Afiliação
  • Bhandari A; Townsville University Hospital, 100 Angus Smith Drive, Douglas, QLD, 4814, Australia. Abhishta.bhandari@my.jcu.edu.au.
  • Ibrahim M; Townsville University Hospital, 100 Angus Smith Drive, Douglas, QLD, 4814, Australia.
  • Sharma C; Townsville University Hospital, 100 Angus Smith Drive, Douglas, QLD, 4814, Australia.
  • Liong R; Department of Medical Imaging Research Office, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia.
  • Gustafson S; Department of Medical Imaging Research Office, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia.
  • Prior M; Department of Medical Imaging Research Office, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia.
Abdom Radiol (NY) ; 46(5): 2052-2063, 2021 05.
Article em En | MEDLINE | ID: mdl-33136182
PURPOSE: Differentiating renal tumours into grades and tumour subtype from medical imaging is important for patient management; however, there is an element of subjectivity when performed qualitatively. Quantitative analysis such as radiomics may provide a more objective approach. The purpose of this article is to systematically review the literature on computed tomography (CT) radiomics for grading and differentiating renal tumour subtypes. An educational perspective will also be provided. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist was followed. PubMed, Scopus and Web of Science were searched for relevant articles. The quality of each study was assessed using the Radiomic Quality Score (RQS). RESULTS: 13 studies were found. The main outcomes were prediction of pathological grade and differentiating between renal tumour types, measured as area under the curve (AUC) for either the receiver operator curve or precision recall curve. Features extracted to predict pathological grade or tumour subtype included shape, intensity, texture and wavelet (a type of higher order feature). Four studies differentiated between low-grade and high-grade clear cell renal cell cancer (RCC) with good performance (AUC = 0.82-0.978). One other study differentiated low- and high-grade chromophobe with AUC = 0.84. Finally, eight studies used radiomics to differentiate between tumour types such as clear cell RCC, fat-poor angiomyolipoma, papillary RCC, chromophobe RCC and renal oncocytoma with high levels of performance (AUC 0.82-0.96). CONCLUSION: Renal tumours can be pathologically classified using CT-based radiomics with good performance. The main radiomic feature used for tumour differentiation was texture. Fuhrman was the most common pathologic grading system used in the reviewed studies. Renal tumour grading studies should be extended beyond clear cell RCC and chromophobe RCC. Further research with larger prospective studies, performed in the clinical setting, across multiple institutions would help with clinical translation to the radiologist's workstation.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Carcinoma de Células Renais / Neoplasias Renais Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Carcinoma de Células Renais / Neoplasias Renais Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article