Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma-a systematic review and meta-analysis.
Objectives: (1) To assess the methodological quality of radiomics studies investigating histological subtypes, therapy response, and survival in patients with renal cell carcinoma (RCC) and (2) to determine the risk of bias in these radiomics studies.Methods: In this systematic review, literature pu...
| Publicado en: | European Radiology Vol. 30; no. 6; pp. 3558 - 3567 |
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| Autores principales: | , , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143397294&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143397294 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jun2020 vid: 30 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143397294 143397294 NLM32060715 143397294 10.1007/s00330-020-06666-3 NLM32060715 143397294 ppf: 3558 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma-a systematic review and meta-analysis. aug: au: Ursprung, Stephan Beer, Lucian Bruining, Annemarie Woitek, Ramona Stewart, Grant D Gallagher, Ferdia A Sala, Evis affil: Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge, UK sug: subj: Informatics Magnetic Resonance Imaging Methods Carcinoma, Renal Cell Neoplasms, Adipose Tissue Tomography, X-Ray Computed Methods Kidney Neoplasms Neoplasms, Adipose Tissue Pathology Diagnosis, Differential Carcinoma, Renal Cell Pathology Kidney Neoplasms Pathology Human Algorithms Validation Studies Comparative Studies Evaluation Research Multicenter Studies Systematic Review Meta Analysis ab: Objectives: (1) To assess the methodological quality of radiomics studies investigating histological subtypes, therapy response, and survival in patients with renal cell carcinoma (RCC) and (2) to determine the risk of bias in these radiomics studies.Methods: In this systematic review, literature published since 2000 on radiomics in RCC was included and assessed for methodological quality using the Radiomics Quality Score. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool and a meta-analysis of radiomics studies focusing on differentiating between angiomyolipoma without visible fat and RCC was performed.Results: Fifty-seven studies investigating the use of radiomics in renal cancer were identified, including 4590 patients in total. The average Radiomics Quality Score was 3.41 (9.4% of total) with good inter-rater agreement (ICC 0.96, 95% CI 0.93-0.98). Three studies validated results with an independent dataset, one used a publically available validation dataset. None of the studies shared the code, images, or regions of interest. The meta-analysis showed moderate heterogeneity among the included studies and an odds ratio of 6.24 (95% CI 4.27-9.12; p < 0.001) for the differentiation of angiomyolipoma without visible fat from RCC.Conclusions: Radiomics algorithms show promise for answering clinical questions where subjective interpretation is challenging or not established. However, the generalizability of findings to prospective cohorts needs to be demonstrated in future trials for progression towards clinical translation. Improved sharing of methods including code and images could facilitate independent validation of radiomics signatures.Key Points: • Studies achieved an average Radiomics Quality Score of 10.8%. Common reasons for low Radiomics Quality Scores were unvalidated results, retrospective study design, absence of open science, and insufficient control for multiple comparisons. • A previous training phase allowed reaching almost perfect inter-rater agreement in the application of the Radiomics Quality Score. • Meta-analysis of radiomics studies distinguishing angiomyolipoma without visible fat from renal cell carcinoma show moderate diagnostic odds ratios of 6.24 and moderate methodological diversity. pubtype: Academic Journal doctype: meta analysis research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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