Classification Model to Estimate MIB-1 (Ki 67) Proliferation Index in NSCLC Patients Evaluated With 18F-FDG-PET/CT.
Anticancer Res
; 40(6): 3355-3360, 2020 Jun.
Article
em En
| MEDLINE
| ID: mdl-32487631
ABSTRACT
BACKGROUND/AIM:
Proliferation biomarkers such as MIB-1 are strong predictors of clinical outcome and response to therapy in patients with non-small-cell lung cancer, but they require histological examination. In this work, we present a classification model to predict MIB-1 expression based on clinical parameters from positron emission tomography. PATIENTS ANDMETHODS:
We retrospectively evaluated 78 patients with histology-proven non-small-cell lung cancer (NSCLC) who underwent 18F-FDG-PET/CT for clinical examination. We stratified the population into a low and high proliferation group using MIB-1=25% as cut-off value. We built a predictive model based on binary classification trees to estimate the group label from the maximum standardized uptake value (SUVmax) and lesion diameter.RESULTS:
The proposed model showed ability to predict the correct proliferation group with overall accuracy >82% (78% and 86% for the low- and high-proliferation group, respectively).CONCLUSION:
Our results indicate that radiotracer activity evaluated via SUVmax and lesion diameter are correlated with tumour proliferation index MIB-1.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Carcinoma Pulmonar de Células não Pequenas
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Antígeno Ki-67
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Fluordesoxiglucose F18
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Neoplasias Pulmonares
Tipo de estudo:
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Female
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Humans
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Male
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article