Radiomics Model Building from Multiparametric MRI to Predict Ki-67 Expression in Patients with Primary Central Nervous System Lymphomas: A Multicenter Study

Abstract

Abstract Objectives To examine the correlation of apparent diffusion coefficient (ADC), diffusion weighted imaging (DWI), and T1 contrast enhanced (T1-CE) with Ki-67 in primary central nervous system lymphomas (PCNSL). And to assess the diagnostic performance of MRI radiomics-based machine-learning algorithms in differentiating the high-proliferation and low-proliferation group of PCNSL.Methods 83 patients with PCNSL were included in this retrospective study. ADC, DWI and T1-CE sequences were collected and their correlation with Ki-67 was examined using Spearman’s correlation analyses. The radiomics features were extracted respectively, and the features were screened by machine learning algorithm and statistical method. Radiomics models of nine different sequence permutations were constructed. The area under the receiver operating characteristic curve (ROC AUC) was used to evaluate the predictive performance of all models. Delong test was utilised to compare the differences of models.Results Relative mean apparent diffusion coefficient (rADCmean) (ρ=-0.354, p = 0.019), relative mean diffusion weighted imaging (rDWImean) (b = 1000) (ρ = 0.273, p = 0.013) and relative mean T1 contrast enhancement (rT1-CEmean) (ρ = 0.385, p = 0.001) was significantly correlated with Ki-67. Interobserver agreements between the two radiologists were almost perfect for all parameters (rADCmean ICC = 0.978, 95%CI 0.966–0.986; rDWImean (b = 1000) ICC = 0.931, 95% CI 0.895–0.955; rT1-CEmean ICC = 0.969, 95% CI 0.953–0.980). The best prediction model in our study used a combination of ADC, DWI, and T1-CE achieving the highest AUC of 0.869, while the second ranked model used ADC and DWI, achieving AUC of 0.828.Conclusion rDWImean, rADCmean and rT1-CEmean was correlated with Ki-67. The radiomics model based on MRI sequences combined is promising to distinguish low proliferation PCNSL from high proliferation PCNSL.