Classification Model to Estimate MIB-1 (Ki 67) Proliferation Index in NSCLC Patients Evaluated With 18F-FDG-PET/CT

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 (SUV_max) 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 SUV_max and lesion diameter are correlated with tumour proliferation index MIB-1.

Keywords: 18F-FDG PET/CT; MIB-1; artificial intelligence; non-small-cell lung cancer.