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Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy.
Davey, Angela; van Herk, Marcel; Faivre-Finn, Corinne; Brown, Sean; McWilliam, Alan.
Afiliação
  • Davey A; Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
  • van Herk M; Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
  • Faivre-Finn C; Department of Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, United Kingdom.
  • Brown S; Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
  • McWilliam A; Department of Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, United Kingdom.
Phys Med Biol ; 66(11)2021 05 24.
Article em En | MEDLINE | ID: mdl-33882470
ABSTRACT
Purpose. 4D-CT is routine imaging for lung cancer patients treated with stereotactic body radiotherapy. No studies have investigated optimal 4D phase selection for radiomics. We aim to determine how phase data should be used to identify prognostic biomarkers for distant failure, and test whether stability assessment is required. A phase selection approach will be developed to aid studies with different 4D protocols and account for patient differences.Methods. 186 features were extracted from the tumour and peritumour on all phases for 258 patients. Feature values were selected from phase features using four

methods:

(A) mean across phases, (B) median across phases, (C) 50% phase, and (D) the most stable phase (closest in value to two neighbours), coined personalised selection. Four levels of stability assessment were also analysed, with inclusion of (1) all features, (2) stable features across all phases, (3) stable features across phase and neighbour phases, and (4) features averaged over neighbour phases. Clinical-radiomics models were built for twelve combinations of feature type and assessment method. Model performance was assessed by concordance index (c-index) and fraction of new information from radiomic features.Results. The most stable phase spanned the whole range but was most often near exhale. All radiomic signatures provided new information for distant failure prediction. The personalised model had the highest c-index (0.77), and 58% of new information was provided by radiomic features when no stability assessment was performed.Conclusion. The most stable phase varies per-patient and selecting this improves model performance compared to standard methods. We advise the single most stable phase should be determined by minimising feature differences to neighbour phases. Stability assessment over all phases decreases performance by excessively removing features. Instead, averaging of neighbour phases should be used when stability is of concern. The models suggest that higher peritumoural intensity predicts distant failure.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiocirurgia / Carcinoma Pulmonar de Células não Pequenas / Neoplasias Pulmonares Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiocirurgia / Carcinoma Pulmonar de Células não Pequenas / Neoplasias Pulmonares Idioma: En Ano de publicação: 2021 Tipo de documento: Article