| Title: |
Classification model to estimate MIB-1 (Ki 67) proliferation index in NSCLC patients evaluated with 18F-FDG-PET/CT |
| Authors: |
Palumbo B.; Capozzi R.; Bianconi F.; Fravolini M. L.; Cascianelli S.; Messina S. G.; Bellezza G.; Sidoni A.; Puma F.; Ragusa M. |
| Contributors: |
Palumbo, B.; Capozzi, R.; Bianconi, F.; Fravolini, M. L.; Cascianelli, S.; Messina, S. G.; Bellezza, G.; Sidoni, A.; Puma, F.; Ragusa, M. |
| Publication Year: |
2020 |
| Collection: |
Archivio della ricerca dell'Università di Modena e Reggio Emilia (Unimore: IRIS) |
| Subject Terms: |
F-FDG PET/CT; Artificial intelligence; MIB-1; Non-small-cell lung cancer |
| Time: |
18 |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/pmid/32487631; info:eu-repo/semantics/altIdentifier/wos/WOS:000538104800037; volume:40; issue:6; firstpage:3355; lastpage:3360; journal:ANTICANCER RESEARCH; https://hdl.handle.net/11380/1207070 |
| DOI: |
10.2196/10.21873/anticanres.14318 |
| Availability: |
https://hdl.handle.net/11380/1207070; https://doi.org/10.2196/10.21873/anticanres.14318 |
| Rights: |
info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.E61EC6D |
| Database: |
BASE |