Bone metastasis prediction in non-small-cell lung cancer: primary CT-based radiomics signature and clinical feature.
BMC Med Imaging
; 24(1): 203, 2024 Aug 05.
Article
em En
| MEDLINE
| ID: mdl-39103775
ABSTRACT
BACKGROUND:
Radiomics provided opportunities to quantify the tumor phenotype non-invasively. This study extracted contrast-enhanced computed tomography (CECT) radiomic signatures and evaluated clinical features of bone metastasis in non-small-cell lung cancer (NSCLC). With the combination of the revealed radiomics and clinical features, the predictive modeling on bone metastasis in NSCLC was established.METHODS:
A total of 318 patients with NSCLC at the Tianjin Medical University Cancer Institute & Hospital was enrolled between January 2009 and December 2019, which included a feature-learning cohort (n = 223) and a validation cohort (n = 95). We trained a radiomics model in 318 CECT images from feature-learning cohort to extract the radiomics features of bone metastasis in NSCLC. The Kruskal-Wallis and the least absolute shrinkage and selection operator regression (LASSO) were used to select bone metastasis-related features and construct the CT radiomics score (Rad-score). Multivariate logistic regression was performed with the combination of the Rad-score and clinical data. A predictive nomogram was subsequently developed.RESULTS:
Radiomics models using CECT scans were significant on bone metastasis prediction in NSCLC. Model performance was enhanced with each information into the model. The radiomics nomogram achieved an AUC of 0.745 (95% confidence interval [CI] 0.68,0.80) on predicting bone metastasis in the training set and an AUC of 0.808(95% confidence interval [CI] 0.71,0.88) in the validation set.CONCLUSION:
The revealed invisible image features were of significance on guiding bone metastasis prediction in NSCLC. Based on the combination of the image features and clinical characteristics, the predictive nomogram was established. Such nomogram can be used for the auxiliary screening of bone metastasis in NSCLC.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Ósseas
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Tomografia Computadorizada por Raios X
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Carcinoma Pulmonar de Células não Pequenas
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Neoplasias Pulmonares
Limite:
Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
BMC Med Imaging
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BMC med. imaging
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BMC medical imaging
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China
País de publicação:
Reino Unido