Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer.
Can Assoc Radiol J
; 72(1): 109-119, 2021 Feb.
Article
em En
| MEDLINE
| ID: mdl-32063026
ABSTRACT
BACKGROUND:
The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and 18F-FDG PET-CT (FDG PET-CT) images of non-small cell lung cancer (NSCLC) with and without epidermal growth factor receptor (EGFR) mutations.METHODS:
Fifty patients diagnosed with NSCLC between 2011 and 2015 and with known EGFR mutation status were retrospectively identified. Texture features extracted from pretreatment CT and FDG PET-CT images by manual contouring of the primary tumor were used to develop multivariate logistic regression (LR) models to predict EGFR mutations in exon 19 and exon 20.RESULTS:
An LR model evaluating FDG PET-texture features was able to differentiate EGFR mutant from wild type with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.87, 0.76, 0.66, and 0.71, respectively. The model derived from CT texture features had an AUC, sensitivity, specificity, and accuracy of 0.83, 0.84, 0.73, and 0.78, respectively. FDG PET-texture features that could discriminate between mutations in EGFR exon 19 and 21 demonstrated AUC, sensitivity, specificity, and accuracy of 0.86, 0.84, 0.73, and 0.78, respectively. Based on CT texture features, the AUC, sensitivity, specificity, and accuracy were 0.75, 0.81, 0.69, and 0.75, respectively.CONCLUSION:
Non-small cell lung cancer texture analysis using FGD-PET and CT images can identify tumors with mutations in EGFR. Imaging signatures could be valuable for pretreatment assessment and prognosis in precision therapy.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Interpretação de Imagem Assistida por Computador
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Carcinoma Pulmonar de Células não Pequenas
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Aprendizado de Máquina
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Genômica por Imageamento
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Neoplasias Pulmonares
/
Mutação
Tipo de estudo:
Diagnostic_studies
/
Guideline
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Observational_studies
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Prognostic_studies
/
Risk_factors_studies
Limite:
Aged
/
Female
/
Humans
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Male
Idioma:
En
Revista:
Can Assoc Radiol J
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Canadá