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Radiation necrosis is an uncommon but severe adverse effect of brain radiation therapy (RT). Current predictive models based on radiation dose have limited accuracy. We aimed to identify early individual response biomarkers based upon diffusion tensor (DT) imaging and incorporated them into a response model for prediction of radiation necrosis. Twenty-nine patients with glioblastoma received six weeks of intensity modulated RT and concurrent temozolomide. Patients underwent DT-MRI scans before treatment, at three weeks during RT, and one, three, and six months after RT. Cases with radiation necrosis were classified based on generalized equivalent uniform dose (gEUD) of whole brain and DT index early changes in the corpus callosum and its substructures. Significant covariates were used to develop normal tissue complication probability models using binary logistic regression. Seven patients developed radiation necrosis. Percentage changes of radial diffusivity (RD) in the splenium at three weeks during RT and at six months after RT differed significantly between the patients with and without necrosis (p = 0.05 and p = 0.01). Percentage change of RD at three weeks during RT in the 30 Gy dose–volume of the splenium and brain gEUD combined yielded the best-fit logistic regression model. Our findings indicate that early individual response during the course of RT, assessed by radial diffusivity, has the potential to aid the prediction of delayed radiation necrosis, which could provide guidance in dose-escalation trials. [ABSTRACT FROM AUTHOR]

Purpose: To develop a pharmacokinetic modelfree framework to analyze the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data for assessment of response of brain metastases to radiation therapy. Methods: Twenty patients with 45 analyzable brain metastases had MRI scans prior to whole brain radiation therapy (WBRT) and at the end of the 2-week therapy. The volumetric DCE images covering the whole brain were acquired on a 3T scanner with approximately 5 s temporal resolution and a total scan time of about 3 min. DCE curves from all voxels of the 45 brain metastases were normalized and then temporally aligned. A DCE matrix that is constructed from the aligned DCE curves of all voxels of the 45 lesions obtained prior to WBRT is processed by principal component analysis to generate the principal components (PCs). Then, the projection coefficient maps prior to and at the end of WBRT are created for each lesion. Next, a pattern recognition technique, based upon fuzzy-c-means clustering, is used to delineate the tumor subvolumes relating to the value of the significant projection coefficients. The relationship between changes in different tumor subvolumes and treatment response was evaluated to differentiate responsive from stable and progressive tumors. Performance of the PC-defined tumor subvolume was also evaluated by receiver operating characteristic (ROC) analysis in prediction of nonresponsive lesions and compared with physiological-defined tumor subvolumes. Results: The projection coefficient maps of the first three PCs contain almost all response-related information in DCE curves of brain metastases. The first projection coefficient, related to the area under DCE curves, is the major component to determine response while the third one has a complimentary role. In ROC analysis, the area under curve of 0.88 ± 0.05 and 0.86 ± 0.06 were achieved for the PC-defined and physiological-defined tumor subvolume in response assessment. Conclusions: The PC-defined subvolume of a brain metastasis could predict tumor response to therapy similar to the physiological-defined one, while the former is determined more rapidly for clinical decision-making support. [ABSTRACT FROM AUTHOR]

Purpose: To determine whether cerebral blood volume (CBV) and cerebral blood flow can predict the response of high-grade gliomas to radiotherapy (RT) by taking into account spatial heterogeneity and temporal changes in perfusion.Methods and Materials: Twenty-three patients with high-grade gliomas underwent conformal RT, with magnetic resonance imaging perfusion before and at Weeks 1-2 and 3-4 during RT. Tumor perfusion was classified as high, medium, or low. The prognostic values of pre-RT perfusion and the changes during RT for early prediction of tumor response to RT were evaluated.Results: The fractional high-CBV tumor volume before RT and the fluid-attenuated inversion recovery imaging tumor volume were identified as predictors for survival (p = 0.01). Changes in tumor CBV during the early treatment course also predicted for survival. Better survival was predicted by a decrease in the fractional low-CBV tumor volume at Week 1 of RT vs. before RT, a decrease in the fractional high-CBV tumor volume at Week 3 vs. Week 1 of RT, and a smaller pre-RT fluid-attenuated inversion recovery imaging tumor volume (p = 0.01).Conclusion: Early temporal changes during RT in heterogeneous regions of high and low perfusion in gliomas might predict for different physiologic responses to RT. This might also open the opportunity to identify tumor subvolumes that are radioresistant and might benefit from intensified RT. [ABSTRACT FROM AUTHOR]

Objective: The internal high-dose volume varies widely for a given prescribed dose during stereotactic radiosurgery (SRS) to treat brain metastases (BMs). This may be altered during treatment planning, and the authors have previously shown that this improves local control (LC) for non-small cell lung cancer BMs without increasing toxicity. Here, they seek to identify potentially actionable dosimetric predictors of LC after SRS for melanoma BM., Methods: The records of patients with unresected melanoma BM treated with single-fraction Gamma Knife RS between 2006 and 2017 were reviewed. LC was assessed on a per-lesion basis, defined as stability or a decrease in lesion size. Outcome-oriented approaches were utilized to determine optimal dichotomization for dosimetric variables relative to LC. Univariable and multivariable Cox regression analysis was implemented to evaluate the impact of collected parameters on LC., Results: Two hundred eighty-seven melanoma BMs in 79 patients were identified. The median age was 56 years (range 31-86 years). The median follow-up was 7.6 months (range 0.5-81.6 months), and the median survival was 9.3 months (range 1.3-81.6 months). Lesions were optimally stratified by volume receiving at least 30 Gy (V30) greater than or equal to versus less than 25%. V30 was ≥ and < 25% in 147 and 140 lesions, respectively. For all patients, 1-year LC was 83% versus 66% for V30 ≥ and < 25%, respectively (p = 0.001). Stratifying by volume, lesions 2 cm or less (n = 215) had 1-year LC of 82% versus 70% (p = 0.013) for V30 ≥ and < 25%, respectively. Lesions > 2 to 3 cm (n = 32) had 1-year LC of 100% versus 43% (p = 0.214) for V30 ≥ and < 25%, respectively. V30 was still predictive of LC even after controlling for the use of immunotherapy and targeted therapy. Radionecrosis occurred in 2.8% of lesions and was not significantly associated with V30., Conclusions: For a given prescription dose, an increased internal high-dose volume, as indicated by measures such as V30 ≥ 25%, is associated with improved LC but not increased toxicity in single-fraction SRS for melanoma BM. Internal dose escalation is an independent predictor of improved LC even in patients receiving immunotherapy and/or targeted therapy. This represents a dosimetric parameter that is actionable at the time of treatment planning and warrants further evaluation.

Noninvasive imaging methods are sought to objectively predict early response to therapy for high-grade glioma tumors. Quantitative metrics derived from diffusion-weighted imaging, such as apparent diffusion coefficient (ADC), have previously shown promise when used in combination with voxel-based analysis reflecting regional changes. The functional diffusion mapping (fDM) metric is hypothesized to be associated with volume of tumor exhibiting an increasing ADC owing to effective therapeutic action. In this work, the reference fDM-predicted survival (from previous study) for 3 weeks from treatment initiation (midtreatment) is compared to multiple histogram-based metrics using Kaplan-Meier estimator for 80 glioma patients stratified to responders and nonresponders based on the population median value for the given metric. The ADC histogram metric reflecting reduction in midtreatment volume of solid tumor (ADC < 1.25 × 10 -3 mm 2 /s) by >8% population-median with respect to pretreatment is found to have the same predictive power as the reference fDM of increasing midtreatment ADC volume above 4%. This study establishes the level of correlation between fDM increase and low-ADC tumor volume shrinkage for prediction of early response to radiation therapy in patients with glioma malignancies.

Purpose/objectives: Multiparametric advanced MR and [ 18 F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) imaging may be important biomarkers for prognosis as well for distinguishing recurrent glioblastoma multiforme (GBM) from treatment-related changes., Methods/materials: We retrospectively evaluated 30 patients treated with chemoradiation for GBM and underwent advanced MR and FDG-PET for confirmation of tumor progression. Multiparametric MRI and FDG-PET imaging metrics were evaluated for their association with 6-month overall (OS) and progression-free survival (PFS) based on pathological, radiographic, and clinical criteria., Results: 17 males and 13 females were treated between 2001 and 2014, and later underwent FDG-PET at suspected recurrence. Baseline FDG-PET and MRI imaging was obtained at a median of 7.5 months [interquartile range (IQR) 3.7-12.4] following completion of chemoradiation. Median follow-up after FDG-PET imaging was 10 months (IQR 7.2-13.0). Receiver-operator characteristic curve analysis identified that lesions characterized by a ratio of the SUV max to the normal contralateral brain (SUV max /NB index) >1.5 and mean apparent diffusion coefficient (ADC) value of ≤1,400 × 10 -6  mm 2 /s correlated with worse 6-month OS and PFS. We defined three patient groups that predicted the probability of tumor progression: SUV max /NB index >1.5 and ADC ≤1,400 × 10 -6 mm 2 /s defined high-risk patients ( n  = 7), SUV max /NB index ≤1.5 and ADC >1,400 × 10 -6 mm 2 /s defined low-risk patients ( n  = 11), and intermediate-risk ( n  = 12) defined the remainder of the patients. Median OS following the time of the FDG-PET scan for the low, intermediate, and high-risk groups were 23.5, 10.5, and 3.8 months ( p  < 0.01). Median PFS were 10.0, 4.4, and 1.9 months ( p  = 0.03). Rates of progression at 6-months in the low, intermediate, and high-risk groups were 36, 67, and 86% ( p  = 0.04)., Conclusion: Recurrent GBM in the molecular era is associated with highly variable outcomes. Multiparametric MR and FDG-PET biomarkers may provide a clinically relevant, non-invasive and cost-effective method of predicting prognosis and improving clinical decision making in the treatment of patients with suspected tumor recurrence.

Objective: Differentiation between recurrent neoplasm and postradiation change in patients previously treated for primary brain tumors is often difficult based on imaging features alone. The purpose of this study was to develop a method using alterations in the ratios of standard brain metabolites-choline (Cho), creatine (Cr), and N-acetylaspartate (NAA)-to predict the probability of tumor recurrence in patients previously treated for brain tumors with new contrast-enhancing lesions., Materials and Methods: Thirty-three patients who had undergone treatment for primary brain tumors in whom routine MRI showed new contrast-enhancing lesions were retrospectively studied. The final diagnosis was assigned using histopathology (n = 13) or imaging follow-up (n = 20; range, 2-27 months). Ratios of three metabolites (Cho, Cr, and NAA) were calculated, and the results were correlated with the final diagnosis using a Wilcoxon's rank sum analysis. A logistic regression model was then used to create a prediction model based on the most statistically significant ratio., Results: Elevations of the metabolic ratios Cho/Cr (p < 0.001) and Cho/NAA (p < 0.001) and a decrease in the ratio NAA/Cr (p = 0.018) were found in patients with recurrent tumor (n = 20) versus those with postradiation change (n = 13). A prediction model using the Cho/NAA ratio yielded a sensitivity of 85%, a specificity of 69.2%, and an area under the receiver operating characteristic curve of 0.92., Conclusion: An elevated Cho/NAA ratio correlated with evidence of tumor recurrence and allowed creation of a prediction rule to aid in lesion classification. The results suggest that MR spectroscopy is a useful tool in assigning patients with nonspecific enhancing lesions to either invasive biopsy or conservative management.