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BackgroundThe US government considers veterans to have been exposed to Agent Orange if they served in Vietnam while the carcinogen was in use, and these veterans are often deemed at high risk of prostate cancer (PCa). Here, we assess whether presumed Agent Orange exposure is independently associated with increased risk of any metastatic or fatal PCa in a diverse Veteran cohort still alive in the modern era (at least 2011), when accounting for race/ethnicity, family history, and genetic risk.Patients and methodsParticipants in the Million Veteran Program (MVP; enrollment began in 2011) who were on active duty during the Vietnam War era (August 1964-April 1975) were included (n = 301,470). Agent Orange exposure was determined using the US government definition. Genetic risk was assessed via a validated polygenic hazard score. Associations with age at diagnosis of any PCa, metastatic PCa, and death from PCa were assessed via Cox proportional hazards models.Results and interpretationOn univariable analysis, exposure to Agent Orange was not associated with increased PCa (hazard ratio [HR]: 1.02, 95% confidence interval [CI]: 1.00-1.04, p = 0.06), metastatic PCa (HR: 0.98, 95% CI: 0.91-1.05, p = 0.55), or fatal PCa (HR: 0.94, 95% CI: 0.79-1.09, p = 0.41). When accounting for race/ethnicity and family history, Agent Orange exposure was independently associated with slightly increased risk of PCa (HR: 1.06, 95% CI: 1.04-1.09,

BackgroundGenetic scores may provide an objective measure of prostate cancer risk and thus inform screening decisions. We evaluated whether a polygenic hazard score based on 290 genetic variants (PHS290) is associated with prostate cancer risk in a diverse population, including Black men, who have higher average risk of prostate cancer death but are often treated as a homogeneously high-risk group.MethodsThis was a retrospective analysis of the Million Veteran Program, a national, population-based cohort study of US military veterans conducted 2011-2021. Cox proportional hazards analyses tested for association of genetic and other risk factors (including self-reported race and ethnicity and family history) with age at death from prostate cancer, age at diagnosis of metastatic (nodal or distant) prostate cancer, and age at diagnosis of any prostate cancer.ResultsA total of 590 750 male participants were included. Median age at last follow-up was 69 years. PHS290 was associated with fatal prostate cancer in the full cohort and for each racial and ethnic group (P

BackgroundMultiparametric magnetic resonance imaging (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the subjective Prostate Imaging Reporting and Data System (PI-RADS) system and quantitative apparent diffusion coefficient (ADC) are inconsistent. Restriction spectrum imaging (RSI) is an advanced diffusion-weighted MRI technique that yields a quantitative imaging biomarker for csPCa called the RSI restriction score (RSIrs).ObjectiveTo evaluate RSIrs for automated patient-level detection of csPCa.Design setting and participantsWe retrospectively studied all patients (n = 151) who underwent 3 T mpMRI and RSI (a 2-min sequence on a clinical scanner) for suspected prostate cancer at University of California San Diego during 2017-2019 and had prostate biopsy within 180 d of MRI.InterventionWe calculated the maximum RSIrs and minimum ADC within the prostate, and obtained PI-RADS v2.1 from medical records.Outcome measurements and statistical analysisWe compared the performance of RSIrs, ADC, and PI-RADS for the detection of csPCa (grade group ≥2) on the best available histopathology (biopsy or prostatectomy) using the area under the curve (AUC) with two-tailed α = 0.05. We also explored whether the combination of PI-RADS and RSIrs might be superior to PI-RADS alone and performed subset analyses within the peripheral and transition zones.Results and limitationsAUC values for ADC, RSIrs, and PI-RADS were 0.48 (95% confidence interval: 0.39, 0.58), 0.78 (0.70, 0.85), and 0.77 (0.70, 0.84), respectively. RSIrs and PI-RADS were each superior to ADC for patient-level detection of csPCa (p

BackgroundProstate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets.MethodsIn total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.ResultsThe final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.ConclusionsWe demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

BackgroundWe previously developed an African-ancestry-specific polygenic hazard score (PHS46+African) that substantially improved prostate cancer risk stratification in men with African ancestry. The model consists of 46 SNPs identified in Europeans and 3 SNPs from 8q24 shown to improve model performance in Africans. Herein, we used principal component (PC) analysis to uncover subpopulations of men with African ancestry for whom the utility of PHS46+African may differ.Materials and methodsGenotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Genetic variation in a window spanning 3 African-specific 8q24 SNPs was estimated using 93 PCs. A Cox proportional hazards framework was used to identify the pair of PCs most strongly associated with the performance of PHS46+African. A calibration factor (CF) was formulated using Cox coefficients to quantify the extent to which the performance of PHS46+African varies with PC.ResultsCF of PHS46+African was strongly associated with the first and twentieth PCs. Predicted CF ranged from 0.41 to 2.94, suggesting that PHS46+African may be up to 7 times more beneficial to some African men than others. The explained relative risk for PHS46+African varied from 3.6% to 9.9% for individuals with low and high CF values, respectively. By cross-referencing our data set with 1000 Genomes, we identified significant associations between continental and calibration groupings.ConclusionWe identified PCs within 8q24 that were strongly associated with the performance of PHS46+African. Further research to improve the clinical utility of polygenic risk scores (or models) is needed to improve health outcomes for men of African ancestry.

Stereotactic radiosurgery planning for cerebral arteriovenous malformations (AVM) is complicated by the variability in appearance of an AVM nidus across different imaging modalities. We developed a deep learning approach to automatically segment cerebrovascular-anatomical maps from multiple high-resolution magnetic resonance imaging/angiography (MRI/MRA) sequences in AVM patients, with the goal of facilitating target delineation. Twenty-three AVM patients who were evaluated for radiosurgery and underwent multi-parametric MRI/MRA were included. A hybrid semi-automated and manual approach was used to label MRI/MRAs with arteries, veins, brain parenchyma, cerebral spinal fluid (CSF), and embolized vessels. Next, these labels were used to train a convolutional neural network to perform this task. Imaging from 17 patients (6362 image slices) was used for training, and 6 patients (1224 slices) for validation. Performance was evaluated by Dice Similarity Coefficient (DSC). Classification performance was good for arteries, veins, brain parenchyma, and CSF, with DSCs of 0.86, 0.91, 0.98, and 0.91, respectively in the validation image set. Performance was lower for embolized vessels, with a DSC of 0.75. This demonstrates the proof of principle that accurate, high-resolution cerebrovascular-anatomical maps can be generated from multiparametric MRI/MRA. Clinical validation of their utility in radiosurgery planning is warranted.

BackgroundParkinson's disease (PD) is a highly age-related disorder, where common genetic risk variants affect both disease risk and age at onset. A statistical approach that integrates these effects across all common variants may be clinically useful for individual risk stratification. A polygenic hazard score methodology, leveraging a time-to-event framework, has recently been successfully applied in other age-related disorders.ObjectivesWe aimed to develop and validate a polygenic hazard score model in sporadic PD.MethodsUsing a Cox regression framework, we modeled the polygenic hazard score in a training data set of 11,693 PD patients and 9841 controls. The score was then validated in an independent test data set of 5112 PD patients and 5372 controls and a small single-study sample of 360 patients and 160 controls.ResultsA polygenic hazard score predicts the onset of PD with a hazard ratio of 3.78 (95% confidence interval 3.49-4.10) when comparing the highest to the lowest risk decile. Combined with epidemiological data on incidence rate, we apply the score to estimate genetically stratified instantaneous PD risk across age groups.ConclusionsWe demonstrate the feasibility of a polygenic hazard approach in PD, integrating the genetic effects on disease risk and age at onset in a single model. In combination with other predictive biomarkers, the approach may hold promise for risk stratification in future clinical trials of disease-modifying therapies, which aim at postponing the onset of PD. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Diffusion-weighted magnetic resonance imaging (DWI) of the musculoskeletal system has various applications, including visualization of bone tumors. However, DWI acquired with echo-planar imaging is susceptible to distortions due to static magnetic field inhomogeneities. This study aimed to estimate spatial displacements of bone and to examine whether distortion corrected DWI images more accurately reflect underlying anatomy. Whole-body MRI data from 127 prostate cancer patients were analyzed. The reverse polarity gradient (RPG) technique was applied to DWI data to estimate voxel-level distortions and to produce a distortion corrected DWI dataset. First, an anatomic landmark analysis was conducted, in which corresponding vertebral landmarks on DWI and anatomic T2-weighted images were annotated. Changes in distance between DWI- and T2-defined landmarks (i.e., changes in error) after distortion correction were calculated. In secondary analyses, distortion estimates from RPG were used to assess spatial displacements of bone metastases. Lastly, changes in mutual information between DWI and T2-weighted images of bone metastases after distortion correction were calculated. Distortion correction reduced anatomic error of vertebral DWI up to 29 mm. Error reductions were consistent across subjects (Wilcoxon signed-rank p

BackgroundParkinson's disease (PD) is a highly age-related disorder, where common genetic risk variants affect both disease risk and age at onset. A statistical approach that integrates these effects across all common variants may be clinically useful for individual risk stratification. A polygenic hazard score methodology, leveraging a time-to-event framework, has recently been successfully applied in other age-related disorders.ObjectivesWe aimed to develop and validate a polygenic hazard score model in sporadic PD.MethodsUsing a Cox regression framework, we modeled the polygenic hazard score in a training data set of 11,693 PD patients and 9841 controls. The score was then validated in an independent test data set of 5112 PD patients and 5372 controls and a small single-study sample of 360 patients and 160 controls.ResultsA polygenic hazard score predicts the onset of PD with a hazard ratio of 3.78 (95% confidence interval 3.49-4.10) when comparing the highest to the lowest risk decile. Combined with epidemiological data on incidence rate, we apply the score to estimate genetically stratified instantaneous PD risk across age groups.ConclusionsWe demonstrate the feasibility of a polygenic hazard approach in PD, integrating the genetic effects on disease risk and age at onset in a single model. In combination with other predictive biomarkers, the approach may hold promise for risk stratification in future clinical trials of disease-modifying therapies, which aim at postponing the onset of PD. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

BackgroundClinical variables-age, family history, genetics-are used for prostate cancer risk stratification. Recently, polygenic hazard scores (PHS46, PHS166) were validated as associated with age at prostate cancer diagnosis. While polygenic scores are associated with all prostate cancer (not specific for fatal cancers), PHS46 was also associated with age at prostate cancer death. We evaluated if adding PHS to clinical variables improves associations with prostate cancer death.MethodsGenotype/phenotype data were obtained from a nested case-control Cohort of Swedish Men (n = 3279; 2163 with prostate cancer, 278 prostate cancer deaths). PHS and clinical variables (family history, alcohol intake, smoking, heart disease, hypertension, diabetes, body mass index) were tested via univariable Cox proportional hazards models for association with age at prostate cancer death. Multivariable Cox models with/without PHS were compared with log-likelihood tests.ResultsMedian age at last follow-up/prostate cancer death was 78.0 (IQR: 72.3-84.1) and 81.4 (75.4-86.3) years, respectively. On univariable analysis, PHS46 (HR 3.41 [95% CI 2.78-4.17]), family history (HR 1.72 [1.46-2.03]), alcohol (HR 1.74 [1.40-2.15]), diabetes (HR 0.53 [0.37-0.75]) were each associated with prostate cancer death. On multivariable analysis, PHS46 (HR 2.45 [1.99-2.97]), family history (HR 1.73 [1.48-2.03]), alcohol (HR 1.45 [1.19-1.76]), diabetes (HR 0.62 [0.42-0.90]) all remained associated with fatal disease. Including PHS46 or PHS166 improved multivariable models for fatal prostate cancer (p

BackgroundDiffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI4 ) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4 -C1 , yielded greatest tumor conspicuity.PurposeTo evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI4 -C1 ) as a quantitative voxel-level classifier of PCa.Study typeRetrospective.SubjectsForty-six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty-three men had benign prostates, and the other 23 men had PCa.Field strength/sequenceA 3 T, multishell diffusion-weighted and axial T2-weighted sequences.AssessmentHigh-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI4 -C1 and conventional ADC. Classifier images were also generated.Statistical testsVoxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. RSI4 -C1 was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false-positive rate for a sensitivity of 90% (FPR90 ). Statistical significance was assessed using bootstrap difference with two-sided α = 0.05.ResultsRSI4 -C1 outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951-0.991] vs. 0.922 [0.878-0.948]) and lower FPR90 (0.032 [0.009-0.082] vs. 0.201 [0.132-0.290]). These improvements were statistically significant (P

BackgroundWe previously developed an African-ancestry-specific polygenic hazard score (PHS46+African) that substantially improved prostate cancer risk stratification in men with African ancestry. The model consists of 46 SNPs identified in Europeans and 3 SNPs from 8q24 shown to improve model performance in Africans. Herein, we used principal component (PC) analysis to uncover subpopulations of men with African ancestry for whom the utility of PHS46+African may differ.Materials and methodsGenotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Genetic variation in a window spanning 3 African-specific 8q24 SNPs was estimated using 93 PCs. A Cox proportional hazards framework was used to identify the pair of PCs most strongly associated with the performance of PHS46+African. A calibration factor (CF) was formulated using Cox coefficients to quantify the extent to which the performance of PHS46+African varies with PC.ResultsCF of PHS46+African was strongly associated with the first and twentieth PCs. Predicted CF ranged from 0.41 to 2.94, suggesting that PHS46+African may be up to 7 times more beneficial to some African men than others. The explained relative risk for PHS46+African varied from 3.6% to 9.9% for individuals with low and high CF values, respectively. By cross-referencing our data set with 1000 Genomes, we identified significant associations between continental and calibration groupings.ConclusionWe identified PCs within 8q24 that were strongly associated with the performance of PHS46+African. Further research to improve the clinical utility of polygenic risk scores (or models) is needed to improve health outcomes for men of African ancestry.

BackgroundPolygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46).Materials and method180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.Results166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.ConclusionsIncorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

AbstractPurposeMultiparametric MRI (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the qualitative PI-RADS system and quantitative apparent diffusion coefficient (ADC) yield inconsistent results. An advanced Restrictrion Spectrum Imaging (RSI) model may yield a better quantitative marker for csPCa, the RSI restriction score (RSIrs). We evaluated RSIrs for patient-level detection of csPCa.Materials and MethodsRetrospective analysis of men who underwent mpMRI with RSI and prostate biopsy for suspected prostate cancer from 2017-2019. Maximum RSIrs within the prostate was assessed by area under the receiver operating characteristic curve (AUC) for discriminating csPCa (grade group ≥2) from benign or grade group 1 biopsies. Performance of RSIrs was compared to minimum ADC and PI-RADS v2-2.1via bootstrap confidence intervals and bootstrap difference (two-tailed α=0.05). We also tested whether the combination of PI-RADS and RSIrs (PI-RADS+RSIrs) was superior to PI-RADS, alone.Results151 patients met criteria for inclusion. AUC values for ADC, RSIrs, and PI-RADS were 0.50 [95% confidence interval: 0.41, 0.60], 0.76 [0.68, 0.84], and 0.78 [0.71, 0.85], respectively. RSIrs (p=0.0002) and PI-RADS (p

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p

BackgroundMulticompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer.PurposeTo optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.Study typeRetrospective.SubjectsForty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.Field strength/sequence3T multishell diffusion-weighted sequence.AssessmentMulticompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps.Statistical testsAnalysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models.ResultsThe lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm2 /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P

Polygenic hazard score (PHS) models are associated with age at diagnosis of prostate cancer. Our model developed in Europeans (PHS46) showed reduced performance in men with African genetic ancestry. We used a cross-validated search to identify single nucleotide polymorphisms (SNPs) that might improve performance in this population. Anonymized genotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Ten iterations of a 10-fold cross-validation search were conducted to select SNPs that would be included in the final PHS46+African model. The coefficients of PHS46+African were estimated in a Cox proportional hazards framework using age at diagnosis as the dependent variable and PHS46, and selected SNPs as predictors. The performance of PHS46 and PHS46+African was compared using the same cross-validated approach. Three SNPs (rs76229939, rs74421890 and rs5013678) were selected for inclusion in PHS46+African. All three SNPs are located on chromosome 8q24. PHS46+African showed substantial improvements in all performance metrics measured, including a 75% increase in the relative hazard of those in the upper 20% compared to the bottom 20% (2.47-4.34) and a 20% reduction in the relative hazard of those in the bottom 20% compared to the middle 40% (0.65-0.53). In conclusion, we identified three SNPs that substantially improved the association of PHS46 with age at diagnosis of prostate cancer in men with African genetic ancestry to levels comparable to Europeans.

BackgroundWhole-brain radiotherapy (WBRT) remains an important treatment for over 200,000 cancer patients in the United States annually. Hippocampal-avoidant WBRT (HA-WBRT) reduces neurocognitive toxicity compared to standard WBRT, but HA-WBRT contouring and planning are more complex and time-consuming than standard WBRT. We designed and evaluated a workflow using commercially available artificial intelligence tools for automated hippocampal segmentation and treatment planning to efficiently generate clinically acceptable HA-WBRT radiotherapy plans.MethodsWe retrospectively identified 100 consecutive adult patients treated for brain metastases outside the hippocampal region. Each patient's T1 post-contrast brain MRI was processed using NeuroQuant, an FDA-approved software that provides segmentations of brain structures in less than 8 min. Automated hippocampal segmentations were reviewed for accuracy, then converted to files compatible with a commercial treatment planning system, where hippocampal avoidance regions and planning target volumes (PTV) were generated. Other organs-at-risk (OARs) were previously contoured per clinical routine. A RapidPlan knowledge-based planning routine was applied for a prescription of 30 Gy in 10 fractions using volumetric modulated arc therapy (VMAT) delivery. Plans were evaluated based on NRG CC001 dose-volume objectives (Brown et al. in J Clin Oncol, 2020).ResultsOf the 100 cases, 99 (99%) had acceptable automated hippocampi segmentations without manual intervention. Knowledge-based planning was applied to all cases; the median processing time was 9 min 59 s (range 6:53-13:31). All plans met per-protocol dose-volume objectives for PTV per the NRG CC001 protocol. For comparison, only 65.5% of plans on NRG CC001 met PTV goals per protocol, with 26.1% within acceptable variation. In this study, 43 plans (43%) met OAR constraints, and the remaining 57 (57%) were within acceptable variation, compared to 42.5% and 48.3% on NRG CC001, respectively. No plans in this study had unacceptable dose to OARs, compared to 0.8% of manually generated plans from NRG CC001. 8.4% of plans from NRG CC001 were not scored or unable to be evaluated.ConclusionsAn automated pipeline harnessing the efficiency of commercially available artificial intelligence tools can generate clinically acceptable VMAT HA-WBRT plans with minimal manual intervention. This process could improve clinical efficiency for a treatment established to improve patient outcomes over standard WBRT.

We determined the effect of sample size on performance of polygenic hazard score (PHS) models in prostate cancer. Age and genotypes were obtained for 40,861 men from the PRACTICAL consortium. The dataset included 201,590 SNPs per subject, and was split into training and testing sets. Established-SNP models considered 65 SNPs that had been previously associated with prostate cancer. Discovery-SNP models used stepwise selection to identify new SNPs. The performance of each PHS model was calculated for random sizes of the training set. The performance of a representative Established-SNP model was estimated for random sizes of the testing set. Mean HR98/50 (hazard ratio of top 2% to average in test set) of the Established-SNP model increased from 1.73 [95% CI: 1.69-1.77] to 2.41 [2.40-2.43] when the number of training samples was increased from 1 thousand to 30 thousand. Corresponding HR98/50 of the Discovery-SNP model increased from 1.05 [0.93-1.18] to 2.19 [2.16-2.23]. HR98/50 of a representative Established-SNP model using testing set sample sizes of 0.6 thousand and 6 thousand observations were 1.78 [1.70-1.85] and 1.73 [1.71-1.76], respectively. We estimate that a study population of 20 thousand men is required to develop Discovery-SNP PHS models while 10 thousand men should be sufficient for Established-SNP models.

BackgroundA polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening.MethodsUnited Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3-T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups.ResultsThe expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age.ConclusionsPHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS.ImpactPersonalized genetic risk assessments could inform prostate cancer screening decisions.

To evaluate different non-Gaussian representations for the diffusion-weighted imaging (DWI) signal in the b-value range 200 to 3000 s/mm2 in benign and malignant breast lesions.Forty-three patients diagnosed with benign (n = 18) or malignant (n = 25) tumors of the breast underwent DWI (b-values 200, 600, 1200, 1800, 2400, and 3000 s/mm2 ). Six different representations were fit to the average signal from regions of interest (ROIs) at different b-value ranges. Quality of fit was assessed by the corrected Akaike information criterion (AICc), and the Friedman test was used for assessing representation ranks. The area under the curve (AUC) of receiver operating characteristic curves were used to evaluate the power of derived parameters to differentiate between malignant and benign lesions. The lesion ROI was divided in central and peripheral parts to assess potential effect of heterogeneity. Sensitivity to noise-floor correction was also evaluated.The Padé exponent was ranked as the best based on AICc, whereas 3 models (kurtosis, fractional, and biexponential) achieved the highest AUC = 0.99 for lesion differentiation. The monoexponential model at bmax = 600 s/mm2 already provides AUC = 0.96, with considerably shorter acquisition time and simpler analysis. Significant differences between central and peripheral parts of lesions were found in malignant lesions. The mono- and biexponential models were most stable against varying degrees of noise-floor correction.Non-Gaussian representations are required for fitting of the DWI curve at high b-values in breast lesions. However, the added clinical value from the high b-value data for differentiation of benign and malignant lesions is not clear.

BackgroundOptimal prostate cancer (PCa) screening strategies will focus on men likely to have potentially lethal disease. Age-specific incidence rates (ASIRs) by modern clinical risk groups could inform risk stratification efforts for screening.MethodsThis cross-sectional population study identified all men diagnosed with PCa in Norway from 2014 to 2017 (n = 20,356). Age, Gleason score (primary plus secondary), and clinical stage were extracted. Patients were assigned to clinical risk groups: low, favorable intermediate, unfavorable intermediate, high, regional, and metastatic. Chi-square tests analyzed the independence of Gleason scores and modern PCa risk groups with age. ASIRs for each risk group were calculated as the product of Norwegian ASIRs for all PCa and the proportions observed for each risk category.ResultsOlder age was significantly associated with a higher Gleason score and more advanced disease. The percentages of men with Gleason 8 to 10 disease among men aged 55 to 59, 65 to 69, 75 to 79, and 85 to 89 years were 16.5%, 23.4%, 37.2%, and 59.9%, respectively (P

IntroductionWe investigated multi-domain baseline neurocognition of primary brain tumor patients prior to radiotherapy (RT), including clinical predictors of function and association between pre-RT and post-RT impairment on a prospective trial.MethodsA multi-domain neuropsychological battery (memory, executive functioning, language, attention, processing) was performed on 37 patients, pre-RT and 3-(n = 21), 6-(n = 22) and 12-(n = 14) months post-RT. Impairment rate was the proportion of patients with standardized T-scores ≤ 1.5 standard deviations below normative means. Per-patient impairment across all domains was calculated using a global deficit score (GDS; higher value indicates more impairment). Associations between baseline GDS and clinical variables were tested. Global GDS impairment rate at each time point was the fraction of patients with GDS scores > 0.5.ResultsStatistically significant baseline neurocognitive impairments were identified on 4 memory (all p ≤ 0.03) and 2 out of 3 (p = 0.01, p = 0.027) executive functioning tests. Per-patient baseline GDS was significantly associated with tumor volume (p = 0.048), tumor type (p = 0.043), seizure history (p = 0.007), and use of anti-epileptics (p = 0.009). The percentage of patients with the same impairment status at 3-, 6-, and 12-months as at baseline were 88%, 85%, and 85% respectively.ConclusionsMemory and executive functioning impairment were the most common cognitive deficits prior to RT. Patients with larger tumors, more aggressive histology, and use of anti-epileptics had higher baseline GDS values. GDS is a promising tool to encompass multi-domain neurocognitive function, and baseline GDS can identify those at risk of cognitive impairment.

PurposeWe used quantitative magnetic resonance imaging to prospectively analyze the association between microstructural damage to memory-associated structures within the medial temporal lobe and longitudinal memory performance after brain radiation therapy (RT).Methods and materialsPatients with a primary brain tumor receiving fractionated brain RT were enrolled on a prospective trial (n = 27). Patients underwent high-resolution volumetric brain magnetic resonance imaging, diffusion-weighted imaging, and neurocognitive testing before and 3, 6, and 12 months post-RT. Medial temporal lobe regions (hippocampus; entorhinal, parahippocampal, and temporal pole white matter [WM]) were autosegmented, quantifying volume and diffusion biomarkers of WM integrity (mean diffusivity [MD]; fractional anisotropy [FA]). Reliable change indices measured changes in verbal (Hopkins Verbal Learning Test-Revised) and visuospatial (Brief Visuospatial Memory Test-Revised [BVMT-R]) memory. Linear mixed-effects models assessed longitudinal associations between imaging parameters and memory.ResultsVisuospatial memory significantly declined at 6 months post-RT (mean reliable change indices, -1.3; P = .012). Concurrent chemotherapy and seizures trended toward a significant association with greater decline in visuospatial memory (P = .053 and P = .054, respectively). Higher mean dose to the left temporal pole WM was significantly associated with decreased FA (r = -0.667; P = .002). Over all time points, smaller right hippocampal volume (P = .021), lower right entorhinal FA (P = .023), greater right entorhinal MD (P = .047), and greater temporal pole MD (BVMT-R total recall, P = .003; BVMT-R delayed recall, P = .042) were associated with worse visuospatial memory. The interaction between right entorhinal MD (BVMT-R total recall, P = .021; BVMT-R delayed recall, P = .004) and temporal pole FA (BVMT-R delayed recall, P = .024) significantly predicted visuospatial memory performance.ConclusionsBrain tumor patients exhibited visuospatial memory decline post-RT. Microstructural damage to critical memory regions, including the hippocampus and medial temporal lobe WM, were associated with post-RT memory decline. The integrity of medial temporal lobe structures is critical to memory performance post-RT, representing possible avoidance targets for memory preservation.

Background and purposeThe amygdalae are deep brain nuclei critical to emotional processing and the creation and storage of memory. It is not known whether the amygdalae are affected by brain radiotherapy (RT). We sought to quantify dose-dependent amygdala change one year after brain RT.Materials and methods52 patients with primary brain tumors were retrospectively identified. Study patients underwent high-resolution, volumetric magnetic resonance imaging before RT and 1 year afterward. Images were processed using FDA-cleared software for automated segmentation of amygdala volume. Tumor, surgical changes, and segmentation errors were manually censored. Mean amygdala RT dose was tested for correlation with amygdala volume change 1 year after RT via the Pearson correlation coefficient. A linear mixed-effects model was constructed to evaluate potential predictors of amygdala volume change, including age, tumor hemisphere, sex, seizure history, and bevacizumab treatment during the study period. As 51 of 52 patients received chemotherapy, possible chemotherapy effects could not be studied. A two-tailed p-value

Background and purposeExecutive function (EF) decline is common after brain radiation therapy (RT), yet the etiology is unclear. We analyzed the association between longitudinal changes in frontal lobe white matter microstructure and decline in EF following RT in brain tumor patients on a prospective clinical trial.Materials and methodsDiffusion tensor imaging was obtained on 22 patients with brain tumors prior to RT, as well as 3- and 6-months post-RT, in a prospective, observational trial. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were calculated within the superficial white matter (SWM) of the anterior cingulate (AC) and dorsolateral prefrontal cortex. Measures of cognitive flexibility, verbal fluency, and verbal set-shifting were obtained pre- and post-RT. Reliable change indices were calculated to determine significant baseline to 6-month EF changes.ResultsDecreases in FA and increases in MD were observed in the caudal AC (CAC) at 3-months post-RT. CAC changes were characterized by increased RD bilaterally. From baseline to 6-months post-RT, decreased FA and increased MD and RD of the CAC was associated with decline in verbal set-shifting ability, whereas increased MD in the CAC was associated with a decline in cognitive flexibility.ConclusionWhite matter underlying the AC may be particularly vulnerable to radiation effects. Early microstructural loss within AC SWM represents an important biomarker for EF decline, and dose reduction in this region may represent a possibility for cognitive preservation for patients receiving radiotherapy.

BACKGROUND:High b-value diffusion-weighted imaging has application in the detection of cancerous tissue across multiple body sites. Diffusional kurtosis and bi-exponential modeling are two popular model-based techniques, whose performance in relation to each other has yet to be fully explored. PURPOSE:To determine the relationship between excess kurtosis and signal fractions derived from bi-exponential modeling in the detection of suspicious prostate lesions. MATERIAL AND METHODS:This retrospective study analyzed patients with normal prostate tissue (n = 12) or suspicious lesions (n = 13, one lesion per patient), as determined by a radiologist whose clinical care included a high b-value diffusion series. The observed signal intensity was modeled using a bi-exponential decay, from which the signal fraction of the slow-moving component was derived ( SFs). In addition, the excess kurtosis was calculated using the signal fractions and ADCs of the two exponentials ( KCOMP). As a comparison, the kurtosis was also calculated using the cumulant expansion for the diffusion signal ( KCE). RESULTS:Both K and KCE were found to increase with SFs within the range of SFs commonly found within the prostate. Voxel-wise receiver operating characteristic performance of SFs, KCE, and KCOMP in discriminating between suspicious lesions and normal prostate tissue was 0.86 (95% confidence interval [CI] = 0.85 - 0.87), 0.69 (95% CI = 0.68-0.70), and 0.86 (95% CI = 0.86-0.87), respectively. CONCLUSION:In a two-component diffusion environment, KCOMP is a scaled value of SFs and is thus able to discriminate suspicious lesions with equal precision . KCE provides a computationally inexpensive approximation of kurtosis but does not provide the same discriminatory abilities as SFs and KCOMP.

BackgroundMolecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas-IDH, 1p/19q, and MGMT status-show distinct quantitative MRI characteristics on FLAIR imaging.MethodsSixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes.ResultsPatients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status.ConclusionQuantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management.

Background and purposeIncidental irradiation of normal brain tissue during radiotherapy is linked to cognitive decline, and may be mediated by damage to healthy cortex. Non-coplanar techniques may be used for cortical sparing. We compared normal brain sparing and probability of cortical atrophy using 4π radiation therapy planning vs. standard fixed gantry intensity-modulated radiotherapy (IMRT).Material and methodsPlans from previously irradiated brain tumor patients ("original IMRT", n = 13) were re-planned to spare cortex using both 4π optimization ("4π") and IMRT optimization ("optimized IMRT"). Homogeneity index (HI), gradient measure, doses to cortex and white matter (excluding tumor), brainstem, optics, and hippocampus were compared with matching PTV coverage. Probability of three grades of post-treatment cortical atrophy was modeled based on previously established dose response curves.ResultsWith matching PTV coverage, 4π significantly improved HI by 27% (p = 0.005) and gradient measure by 8% (p = 0.001) compared with optimized IMRT. 4π optimization reduced mean and equivalent uniform doses (EUD) to all standard OARs, with 14-15% reduction in hippocampal EUD (p ≤ 0.003) compared with the other two plans. 4π significantly reduced dose to fractional cortical volumes (V50, V40 and V30) compared with the original IMRT plans, and reduced cortical V30 by 7% (p = 0.008) compared with optimized IMRT. White matter EUD, mean dose, and fractional volumes V50, V40 and V30 were also significantly lower with 4π (p ≤ 0.001). With 4π, probability of grade 1, 2 and 3 cortical atrophy decreased by 12%, 21% and 26% compared with original IMRT and by 8%, 14% and 3% compared with optimized IMRT, respectively (p ≤ 0.001).Conclusions4π radiotherapy significantly improved cortical sparing and reduced doses to standard brain OARs, white matter, and the hippocampus. This was achieved with superior PTV dose homogeneity. Such sparing could reduce the probability of cortical atrophy that may lead to cognitive decline.

BACKGROUND AND PURPOSE:Incidental irradiation of normal brain tissue during radiotherapy is linked to cognitive decline, and may be mediated by damage to healthy cortex. Non-coplanar techniques may be used for cortical sparing. We compared normal brain sparing and probability of cortical atrophy using 4π radiation therapy planning vs. standard fixed gantry intensity-modulated radiotherapy (IMRT). MATERIAL AND METHODS:Plans from previously irradiated brain tumor patients ("original IMRT", n = 13) were re-planned to spare cortex using both 4π optimization ("4π") and IMRT optimization ("optimized IMRT"). Homogeneity index (HI), gradient measure, doses to cortex and white matter (excluding tumor), brainstem, optics, and hippocampus were compared with matching PTV coverage. Probability of three grades of post-treatment cortical atrophy was modeled based on previously established dose response curves. RESULTS:With matching PTV coverage, 4π significantly improved HI by 27% (p = 0.005) and gradient measure by 8% (p = 0.001) compared with optimized IMRT. 4π optimization reduced mean and equivalent uniform doses (EUD) to all standard OARs, with 14-15% reduction in hippocampal EUD (p ≤ 0.003) compared with the other two plans. 4π significantly reduced dose to fractional cortical volumes (V50, V40 and V30) compared with the original IMRT plans, and reduced cortical V30 by 7% (p = 0.008) compared with optimized IMRT. White matter EUD, mean dose, and fractional volumes V50, V40 and V30 were also significantly lower with 4π (p ≤ 0.001). With 4π, probability of grade 1, 2 and 3 cortical atrophy decreased by 12%, 21% and 26% compared with original IMRT and by 8%, 14% and 3% compared with optimized IMRT, respectively (p ≤ 0.001). CONCLUSIONS:4π radiotherapy significantly improved cortical sparing and reduced doses to standard brain OARs, white matter, and the hippocampus. This was achieved with superior PTV dose homogeneity. Such sparing could reduce the probability of cortical atrophy that may lead to cognitive decline.

ObjectivesTo develop and validate a genetic tool to predict age of onset of aggressive prostate cancer (PCa) and to guide decisions of who to screen and at what age.DesignAnalysis of genotype, PCa status, and age to select single nucleotide polymorphisms (SNPs) associated with diagnosis. These polymorphisms were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (that is, not eligible for surveillance according to National Comprehensive Cancer Network guidelines; any of Gleason score ≥7, stage T3-T4, PSA (prostate specific antigen) concentration ≥10 ng/L, nodal metastasis, distant metastasis). The resulting polygenic hazard score is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and PSA screening data. The hazard score was calculated for these men to test prediction of survival free from PCa.SettingMultiple institutions that were members of international PRACTICAL consortium.ParticipantsAll consortium participants of European ancestry with known age, PCa status, and quality assured custom (iCOGS) array genotype data. The development dataset comprised 31 747 men; the validation dataset comprised 6411 men.Main outcome measuresPrediction with hazard score of age of onset of aggressive cancer in validation set.ResultsIn the independent validation set, the hazard score calculated from 54 single nucleotide polymorphisms was a highly significant predictor of age at diagnosis of aggressive cancer (z=11.2, P98th centile) were compared with those with average scores (30th-70th centile), the hazard ratio for aggressive cancer was 2.9 (95% confidence interval 2.4 to 3.4). Inclusion of family history in a combined model did not improve prediction of onset of aggressive PCa (P=0.59), and polygenic hazard score performance remained high when family history was accounted for. Additionally, the positive predictive value of PSA screening for aggressive PCa was increased with increasing polygenic hazard score.ConclusionsPolygenic hazard scores can be used for personalised genetic risk estimates that can predict for age at onset of aggressive PCa.

In the initial online publication, the values in the last two rows in Table 1 were in the wrong rows. The original article has been corrected.

The 2016 World Health Organization Classification of Tumors of the Central Nervous System incorporates the use of molecular information into the classification of brain tumors, including grade II and III gliomas, providing new prognostic information that cannot be delineated based on histopathology alone. We hypothesized that these genomic subgroups may also have distinct imaging features. A retrospective single institution study was performed on 40 patients with pathologically proven infiltrating WHO grade II/III gliomas with a pre-treatment MRI and molecular data on IDH, chromosomes 1p/19q and ATRX status. Two blinded Neuroradiologists qualitatively assessed MR features. The relationship between each parameter and molecular subgroup (IDH-wildtype; IDH-mutant-1p/19q codeleted-ATRX intact; IDH-mutant-1p/19q intact-ATRX loss) was evaluated with Fisher's exact test. Progression free survival (PFS) was also analyzed. A border that could not be defined on FLAIR was most characteristic of IDH-wildtype tumors, whereas IDH-mutant tumors demonstrated either well-defined or slightly ill-defined borders (p = 0.019). Degree of contrast enhancement and presence of restricted diffusion did not distinguish molecular subgroups. Frontal lobe predominance was associated with IDH-mutant tumors (p = 0.006). The IDH-wildtype subgroup had significantly shorter PFS than the IDH-mutant groups (p

Radiation therapy (RT) is a critical treatment modality for patients with brain tumors, although it can cause adverse effects. Recent data suggest that brain RT is associated with dose-dependent cortical atrophy, which could disrupt neocortical networks. This study examines whether brain RT affects structural network properties in brain tumor patients. We applied graph theory to MRI-derived cortical thickness estimates of 54 brain tumor patients before and after RT. Cortical surfaces were parcellated into 68 regions and correlation matrices were created for patients pre- and post-RT. Significant changes in graph network properties were tested using nonparametric permutation tests. Linear regressions were conducted to measure the association between dose and changes in nodal network connectivity. Increases in transitivity, modularity, and global efficiency (n = 54, p

PurposeWe used multi-b-value diffusion models to characterize microstructural white matter changes after brain radiation into fast and slow components, in order to better understand the pathophysiology of radiation-induced tissue damage.MethodsFourteen patients were included in this retrospective analysis with imaging prior to, and at 1, 4-5, and 9-10 months after radiotherapy (RT). Diffusion signal decay within brain white matter was fit to a biexponential model to separate changes within the slow and fast components. Linear mixed-effects models were used to obtain estimates of the effect of radiation dose and time on the model parameters.ResultsWe found an increase of 0.11 × 10-4 and 0.14 × 10-4 mm2 /s in the fast diffusion coefficient per unit dose-time (Gy-month) in the longitudinal and transverse directions, respectively. By contrast, the longitudinal slow diffusion coefficient decreased independently of dose, by 0.18 × 10-4 , 0.16 × 10-4 , and 0.098 × 10-4 mm2 /s at 1, 4, and 9 months post-RT, respectively.ConclusionsRadiation-induced white matter changes in the first year following RT are driven by dose-dependent increases in the fast component and dose-independent decreases in the slow component.

Background and purposeRegional differences in sensitivity to white matter damage after brain radiotherapy (RT) are not well-described. We characterized the spatial heterogeneity of dose-response across white matter tracts using diffusion tensor imaging (DTI).Materials and methodsForty-nine patients with primary brain tumors underwent MRI with DTI before and 9-12months after partial-brain RT. Maps of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were generated. Atlas-based white matter tracts were identified. A secondary analysis using skeletonized tracts was also performed. Linear mixed-model analysis of the relationship between mean and max dose and percent change in DTI metrics was performed.ResultsTracts with the strongest correlation of FA change with mean dose were the fornix (-0.46 percent/Gy), cingulum bundle (-0.44 percent/Gy), and body of corpus callosum (-0.23 percent/Gy), p

Purpose and objectivesNeurologic deficits after brain radiation therapy (RT) typically involve decline in higher-order cognitive functions such as attention and memory rather than sensory defects or paralysis. We sought to determine whether areas of the cortex critical to cognition are selectively vulnerable to radiation dose-dependent atrophy.Methods and materialsWe measured change in cortical thickness in 54 primary brain tumor patients who underwent fractionated, partial brain RT. The study patients underwent high-resolution, volumetric magnetic resonance imaging (T1-weighted; T2 fluid-attenuated inversion recovery, FLAIR) before RT and 1 year afterward. Semiautomated software was used to segment anatomic regions of the cerebral cortex for each patient. Cortical thickness was measured for each region before RT and 1 year afterward. Two higher-order cortical regions of interest (ROIs) were tested for association between radiation dose and cortical thinning: entorhinal (memory) and inferior parietal (attention/memory). For comparison, 2 primary cortex ROIs were also tested: pericalcarine (vision) and paracentral lobule (somatosensory/motor). Linear mixed-effects analyses were used to test all other cortical regions for significant radiation dose-dependent thickness change. Statistical significance was set at α = 0.05 using 2-tailed tests.ResultsCortical atrophy was significantly associated with radiation dose in the entorhinal (P=.01) and inferior parietal ROIs (P=.02). By contrast, no significant radiation dose-dependent effect was found in the primary cortex ROIs (pericalcarine and paracentral lobule). In the whole-cortex analysis, 9 regions showed significant radiation dose-dependent atrophy, including areas responsible for memory, attention, and executive function (P≤.002).ConclusionsAreas of cerebral cortex important for higher-order cognition may be most vulnerable to radiation-related atrophy. This is consistent with clinical observations that brain radiation patients experience deficits in domains of memory, executive function, and attention. Correlations of regional cortical atrophy with domain-specific cognitive functioning in prospective trials are warranted.

BackgroundRadiation-induced cognitive impairment may be mediated by hippocampal damage, but the structural integrity of this region in tumor patients at baseline is unclear. Hippocampal volumes of 31 glioma patients prior to receiving radiotherapy were compared to a group of 34 healthy controls.Materials and methodsLeft and right hippocampi on T1-weighted pre-contrast magnetic resonance images were automatically segmented using Freesurfer, and visually inspected for segmentation errors. Normalized hippocampal volume for each subject was calculated as the sum of left and right hippocampal volumes divided by the estimated total intracranial volume. The normalized amygdala volume was similarly analyzed as a reference structure.ResultsA Wilcoxon rank-sum test showed a significant difference in normalized hippocampal volumes between patients and controls (mean value 0.499 vs. 0.524, p = .01). No statistically significant difference was found for the amygdala. A post-hoc analysis revealed a significant difference in normalized hippocampal volumes between patients who had experienced seizures (mean value: 0.480, p