Investigating DWI changes in white matter of meningioma patients treated with proton therapy. (April 2021) - Buizza, Giulia; Zampini, Marco Andrea; Riva, Giulia; Molinelli, Silvia; Fontana, Giulia; Imparato, Sara; Ciocca, Mario; Iannalfi, Alberto; Orlandi, Ester; Baroni, Guido; Paganelli, Chiara
Highlights: Evaluation of DWI changes in white matter after proton therapy. Significant diffusion changes were found in high-dose regions. Linear-mixed models unveiled significant dependencies of ADC on dose and time. Perfusion-related parameters presented mixed results. Diffusion restriction may characterize radiation-induced cellular injury. Abstract: Purpose: To evaluate changes in diffusion and perfusion-related properties of white matter (WM) induced by proton therapy, which is capable of a greater dose sparing to organs at risk with respect to conventional X-ray radiotherapy, and to eventually expose early manifestations of delayed neuro-toxicities. Methods: Apparent diffusion coefficient (ADC) and IVIM parameters (D, D* and f) were estimated from diffusion-weighted MRI (DWI) in 46 patients affected by meningioma and treated with proton therapy. The impact on changes in diffusion and perfusion-related WM properties of dose and time, as well as the influence of demographic and pre-treatment clinical information, were investigated through linear mixed-effects models. Results: Decreasing trends in ADC and D were found for WM regions hit by medium–high (30–40 Gy(RBE)) and high ( > 40 Gy(RBE)) doses, which are compatible with diffusion restriction due to radiation-induced cellular injury. Significant influence of dose and time on median ADC changes were observed. Also, D* showed a significant dependency on dose, whereas f consistently showed no dependency on dose and time. Age, gender and surgery extent were also found to affect changes in ADC. Conclusions: These results overall agree with those from studies conducted on cohorts of mixed proton and X-ray radiotherapy patients. Future work should focus on relating our findings with clinical information of co-morbidities and thus exploiting such or more advanced imaging data to build normal tissue complication probability models to better integrate clinical and dose information.