1. Classification Model to Estimate MIB-1 (Ki 67) Proliferation Index in NSCLC Patients Evaluated With 18 F-FDG-PET/CT.
- Author
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Palumbo B, Capozzi R, Bianconi F, Fravolini ML, Cascianelli S, Messina SG, Bellezza G, Sidoni A, Puma F, and Ragusa M
- Subjects
- Carcinoma, Non-Small-Cell Lung metabolism, Carcinoma, Non-Small-Cell Lung pathology, Cell Proliferation physiology, Female, Humans, Immunohistochemistry, Lung Neoplasms metabolism, Lung Neoplasms pathology, Male, Positron Emission Tomography Computed Tomography methods, Radiopharmaceuticals, Retrospective Studies, Carcinoma, Non-Small-Cell Lung classification, Carcinoma, Non-Small-Cell Lung diagnostic imaging, Fluorodeoxyglucose F18, Ki-67 Antigen biosynthesis, Lung Neoplasms classification, Lung Neoplasms diagnostic imaging
- 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 and Methods: We retrospectively evaluated 78 patients with histology-proven non-small-cell lung cancer (NSCLC) who underwent
18 F-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., (Copyright© 2020, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)- Published
- 2020
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