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 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.

Classification model to estimate MIB-1 (Ki 67) proliferation index in NSCLC patients evaluated with 18F-FDG-PET/CT / Palumbo, B.; Capozzi, R.; Bianconi, F.; Fravolini, M. L.; Cascianelli, S.; Messina, S. G.; Bellezza, G.; Sidoni, A.; Puma, F.; Ragusa, M.. - In: ANTICANCER RESEARCH. - ISSN 0250-7005. - 40:6(2020), pp. 3355-3360. [10.2196/10.21873/anticanres.14318]

Classification model to estimate MIB-1 (Ki 67) proliferation index in NSCLC patients evaluated with 18F-FDG-PET/CT

Cascianelli S.;
2020

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 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.
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Classification model to estimate MIB-1 (Ki 67) proliferation index in NSCLC patients evaluated with 18F-FDG-PET/CT / Palumbo, B.; Capozzi, R.; Bianconi, F.; Fravolini, M. L.; Cascianelli, S.; Messina, S. G.; Bellezza, G.; Sidoni, A.; Puma, F.; Ragusa, M.. - In: ANTICANCER RESEARCH. - ISSN 0250-7005. - 40:6(2020), pp. 3355-3360. [10.2196/10.21873/anticanres.14318]
Palumbo, B.; Capozzi, R.; Bianconi, F.; Fravolini, M. L.; Cascianelli, S.; Messina, S. G.; Bellezza, G.; Sidoni, A.; Puma, F.; Ragusa, M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1207070
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