Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy.
Sci Rep
; 8(1): 4003, 2018 03 05.
Article
em En
| MEDLINE
| ID: mdl-29507399
ABSTRACT
We sought to quantify contribution of radiomics and SUVmax at PET/CT to predict clinical outcome in lung cancer patients treated with stereotactic body radiotherapy (SBRT). 150 patients with 172 lung cancers, who underwent SBRT were retrospectively included. Radiomics were applied on PET/CT. Principal components (PC) for 42 CT and PET-derived features were examined to determine which ones accounted for most of variability. Survival analysis quantified ability of radiomics and SUVmax to predict outcome. PCs including homogeneity, size, maximum intensity, mean and median gray level, standard deviation, entropy, kurtosis, skewness, morphology and asymmetry were included in prediction models for regional control (RC) [PC4-HR0.38, p = 0.02], distant control (DC) [PC4-HR0.51, p = 0.02 and PC1-HR1.12, p = 0.01], recurrence free probability (RFP) [PC1-HR1.08, p = 0.04], disease specific survival (DSS) [PC2-HR1.34, p = 0.03 and PC3-HR0.64, p = 0.02] and overall survival (OS) [PC4-HR0.45, p = 0.004 and PC3-HR0.74, p = 0.02]. In combined analysis with SUVmax, PC1 lost predictive ability over SUVmax for RFP [HR1.1, p = 0.04] and DC [HR1.13, p = 0.002], while PC4 remained predictive of DC independent of SUVmax [HR0.5, p = 0.02]. Radiomics remained the only predictors of OS, DSS and RC. Neither SUVmax nor radiomics predicted recurrence free survival. Radiomics on PET/CT provided complementary information for prediction of control and survival in SBRT-treated lung cancer patients.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Radiocirurgia
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Carcinoma Pulmonar de Células não Pequenas
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Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada
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Neoplasias Pulmonares
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Recidiva Local de Neoplasia
Tipo de estudo:
Prognostic_studies
Limite:
Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article