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Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform.
Fornacon-Wood, Isabella; Mistry, Hitesh; Ackermann, Christoph J; Blackhall, Fiona; McPartlin, Andrew; Faivre-Finn, Corinne; Price, Gareth J; O'Connor, James P B.
Afiliação
  • Fornacon-Wood I; Division of Cancer Sciences, University of Manchester, Manchester, UK. Isabella.fornacon-wood@postgrad.manchester.ac.uk.
  • Mistry H; Division of Cancer Sciences, University of Manchester, Manchester, UK.
  • Ackermann CJ; Department of Medical Oncology, Spital STS AG, Thun, Switzerland.
  • Blackhall F; Division of Cancer Sciences, University of Manchester, Manchester, UK.
  • McPartlin A; Department of Medical Oncology, The Christie Hospital NHS Foundation Trust, Manchester, UK.
  • Faivre-Finn C; Department of Clinical Oncology, The Christie Hospital NHS Foundation Trust, Manchester, UK.
  • Price GJ; Division of Cancer Sciences, University of Manchester, Manchester, UK.
  • O'Connor JPB; Department of Clinical Oncology, The Christie Hospital NHS Foundation Trust, Manchester, UK.
Eur Radiol ; 30(11): 6241-6250, 2020 Nov.
Article em En | MEDLINE | ID: mdl-32483644
OBJECTIVE: To investigate the effects of Image Biomarker Standardisation Initiative (IBSI) compliance, harmonisation of calculation settings and platform version on the statistical reliability of radiomic features and their corresponding ability to predict clinical outcome. METHODS: The statistical reliability of radiomic features was assessed retrospectively in three clinical datasets (patient numbers: 108 head and neck cancer, 37 small-cell lung cancer, 47 non-small-cell lung cancer). Features were calculated using four platforms (PyRadiomics, LIFEx, CERR and IBEX). PyRadiomics, LIFEx and CERR are IBSI-compliant, whereas IBEX is not. The effects of IBSI compliance, user-defined calculation settings and platform version were assessed by calculating intraclass correlation coefficients and confidence intervals. The influence of platform choice on the relationship between radiomic biomarkers and survival was evaluated using univariable cox regression in the largest dataset. RESULTS: The reliability of radiomic features calculated by the different software platforms was only excellent (ICC > 0.9) for 4/17 radiomic features when comparing all four platforms. Reliability improved to ICC > 0.9 for 15/17 radiomic features when analysis was restricted to the three IBSI-compliant platforms. Failure to harmonise calculation settings resulted in poor reliability, even across the IBSI-compliant platforms. Software platform version also had a marked effect on feature reliability in CERR and LIFEx. Features identified as having significant relationship to survival varied between platforms, as did the direction of hazard ratios. CONCLUSION: IBSI compliance, user-defined calculation settings and choice of platform version all influence the statistical reliability and corresponding performance of prognostic models in radiomics. KEY POINTS: • Reliability of radiomic features varies between feature calculation platforms and with choice of software version. • Image Biomarker Standardisation Initiative (IBSI) compliance improves reliability of radiomic features across platforms, but only when calculation settings are harmonised. • IBSI compliance, user-defined calculation settings and choice of platform version collectively affect the prognostic value of features.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Software / Carcinoma Pulmonar de Células não Pequenas / Carcinoma de Pequenas Células do Pulmão / Neoplasias de Cabeça e Pescoço / Neoplasias Pulmonares Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Eur Radiol Assunto da revista: RADIOLOGIA Ano de publicação: 2020 Tipo de documento: Article País de publicação: Alemanha

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Software / Carcinoma Pulmonar de Células não Pequenas / Carcinoma de Pequenas Células do Pulmão / Neoplasias de Cabeça e Pescoço / Neoplasias Pulmonares Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Eur Radiol Assunto da revista: RADIOLOGIA Ano de publicação: 2020 Tipo de documento: Article País de publicação: Alemanha