Development of a model to predict Ki-67 expression status in non-Hodgkin’s lymphoma based on PET radiomics - Tian, Ke; Tong, Pengfei; Wu, Keliu; Azhar, Amina; Fang, Yuan; Xu, Nan; Wang, Ruihua (Frontiers in Oncology, 2025)
IntroductionThis study aims to evaluate the effectiveness of conventional metabolic parameters and radiomic features from 18F-deoxyglucose(FDG) PET in predicting Ki-67 expression status in patients with non-Hodgkin’s lymphoma.MethodsWe analyzed clinical, immunohistochemical(IHC), and 18F-FDG PET/CT data from 197 patients diagnosed with non-Hodgkin’s lymphoma at our institution between May 2018 and July 2023. Patients were randomly assigned to a training set (60%) and a validation set (40%) to develop PET image-based radiomics, clinical, and combined models. The models’ predictive abilities were evaluated using receiver operating characteristic (ROC) curves and a nomogram was created to estimate high Ki-67 expression probabilities.ResultsAmong the patients, 70 exhibited low Ki-67 expression while 127 had high Ki-67 expression (113 males, 84 females, aged 5–85 years). The high Ki-67 group showed a higher proportion of fever(75.9% vs. 24.1%, P < 0.05) and tumor SUV max value/mediastinal SUV max value (T/MB) (P < 0.01). Five radiomic features formed the radiomics score (AUC: training 0.827; validation 0.883). The combined model showed the highest AUC(training 0.921; validation 0.916), indicating strong predictive capability.ConclusionThe radiomics model derived from 18F-FDG PET demonstrates superior predictive performance for Ki-67 expression status compared to T/MB. The combined model further improves prediction accuracy, highlighting its potential clinical applicability.