Utilizing Radiation Dose Maps to Predict Local Failure Following Stereotactic Radiation of Brain Metastases.

PURPOSE/OBJECTIVE(S) Radiologic images have long been used clinically to assess tumor response to therapies, with a significant thrust in recent years to develop methods for predicting tumor response to therapy from radiologic images. Magnetic resonance imaging (MRI) of the apparent diffusion coefficient of water (ADC) in the tumor has been mechanistically linked to tumor cell density and viability. Post-radiation tumor ADC can arguably serve as an objective metric of tumor response to radiation treatment (RT). Here we hypothesize that the post-RT tumor ADC map can be predicted from spatially co-registered pre-RT multiparameter MRI (mpMRI) images and the delivered RT dose map. The overarching goal of this work is to provide a method to predict the RT dose map that will optimize local control. MATERIALS/METHODS We retrospectively examined 19 patients with breast cancer metastases to the brain (BCMB) who were treated with stereotactic radiation from 2013 to 2019. All patients had confirmed local recurrence. Subjects received a median dose of 21 Gy (range 15-30 Gy) in 1-5 fractions. T1-weighted unenhanced (T1W), T1-weighted contrast-enhanced (T1WCE), T2-weighted (T2W), Fluid-Attenuated Inversion Recovery (FLAIR) scans and ADC maps were acquired on all subjects before SRS, an average of 78 (15-158) days after SRS, and an average of 303 (54-831) days after SRS. Intensity-calibrated images from 11 subjects were used to train a forward model of the general form: ADCpostRT = f(RT Dose Map, T1WpreRT, T1WCEpreRT, T2WpreRT, FLAIRpreRT, ADCpreRT). RESULTS A significant univariable correlation was observed between the pixelwise change in tumor ADC (first post-SRS scan vs. pre-SRS scan) and the pixelwise RT dose (Table). A multivariable forward model with 13 fitted parameters that related ADCpostRT to an exponential function of the RT dose map with additional exponential terms involving ADCpreRT, T1WpreRT, T1W-CEpreRT, T2WpreRT, and FLAIRpreRT could predict ADCpostRT maps that were visually similar to the corresponding measured ADCpostRT maps (mean structural similarity index measure = 0.96). CONCLUSION We have demonstrated the feasibility of predicting post-RT ADC maps from pre-RT mpMRI images and the delivered RT dose maps. Further studies are underway to improve the forward model as well as to train a conditional Generative Adversarial Network (cGAN)-based inverse model. The ultimate study objective is to investigate whether the inverse model can predict the RT dose map that will yield a target post-RT ADC map in BCMB.