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The NCCN Guidelines for Central Nervous System (CNS) Cancers focus on management of the following adult CNS cancers: glioma (WHO grade 1, WHO grade 2–3 oligodendroglioma [1p19q codeleted, IDH-mutant], WHO grade 2–4 IDH-mutant astrocytoma, WHO grade 4 glioblastoma), intracranial and spinal ependymomas, medulloblastoma, limited and extensive brain metastases, leptomeningeal metastases, non–AIDS-related primary CNS lymphomas, metastatic spine tumors, meningiomas, and primary spinal cord tumors. The information contained in the algorithms and principles of management sections in the NCCN Guidelines for CNS Cancers are designed to help clinicians navigate through the complex management of patients with CNS tumors. Several important principles guide surgical management and treatment with radiotherapy and systemic therapy for adults with brain tumors. The NCCN CNS Cancers Panel meets at least annually to review comments from reviewers within their institutions, examine relevant new data from publications and abstracts, and reevaluate and update their recommendations. These NCCN Guidelines Insights summarize the panel’s most recent recommendations regarding molecular profiling of gliomas.

PurposeBrain radiotherapy can impair fine motor skills (FMS). FMS are essential for activities of daily living, enabling hand-eye coordination for manipulative movements. We developed normal tissue complication probability (NTCP) models for decline in FMS after fractionated brain RT.MethodsOn a prospective trial, 44 primary brain tumor patients received fractioned RT, underwent high-resolution volumetric MRI and diffusion tensor imaging, and comprehensive FMS assessments (Delis-Kaplan Executive Function System Trail Making Test Motor Speed [DKEFS-MS], and Grooved Pegboard Dominant/Non-Dominant Hands [PDH/PNDH]) at baseline and 6 months post-RT. Regions of interest subserving motor function (including cortex, superficial white matter, thalamus, basal ganglia, and white matter tracts) were autosegmented using validated methods and manually verified. Dosimetric and clinical variables were included in multivariate NTCP models, using automated bootstrapped logistic regression, least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests with nested cross-validation.ResultsHalf of patients showed decline on PNDH, 17 of 42 (40.4%) on PDH, and 11 of 44 (25%) on DKEFS-TM. Automated bootstrapped logistic regression selected a one-term model including maximum dose to dominant postcentral white matter. LASSO selected this term and steroid use. The top five variables in random forest were all dosimetric: mean and maximum dose to dominant corticospinal tract; maximum dose to dominant thalamus; mean dose to dominant caudate; maximum dose to dominant postcentral white matter. This technique performed best with AUC 0.69 (95% CI 0.68 – 0.70) on nested cross-validation.ConclusionWe present the first NTCP models for FMS impairment after brain RT. Dose to several supratentorial motor-associated ROIs correlated with decline in dominant hand fine motor dexterity in primary brain tumor patients, outperforming clinical variables. These data can guide prospective fine motor-sparing strategies for brain RT.

PURPOSE Measuring gliomas is a time-intensive process with significant inter-rater variability in post-surgical residual tumor and resection cavities. This likely contributes to the delayed assessment of progression and in-field recurrences from radiation. Automated segmentation of pre and post-treatment gliomas could reduce inter-rater variability and increase workflow efficiency for routine longitudinal radiographic assessment and treatment planning. We evaluated whether a 3D neural network is comparable to expert assessment of pre and post-treatment diffuse gliomas tissue types and resection cavities. METHODS A retrospective cohort of 647 MRIs of patients with diffuse gliomas (average 55.1 years, 45% female, 396 pre-treatment and 251 post-treatment, median 237 days post-surgery) from The Cancer Imaging Archive were stratified by operation status and tumor grade and randomly split into training (536) and testing (111) samples. T1, T1-post-contrast, T2, and FLAIR images were registered, skull-stripped, and interpolated to 1x1x1. Four classes — edema/infiltrative/post-treatment changes (ED), enhancing tissue (ET), necrotic core (NCR), and resection cavities (RC) — were manually segmented by an expert neuroradiologist. Using these segmentations and the four input image modalities, a nnU-Net, a state-of-the-art 3D U-Net convolutional neural network, was trained to predict segmentations of the four classes. RESULTS Segmentation performance in the test set for the ED, ET, and RC classes was at the level of inter-rater reliability with median Dice scores ranging from 0.85 to 1 and relative volume errors ranging from -0.05 to 0.02. There was a near-perfect correlation between manually segmented and predicted total lesion volumes (r2 values ranging from 0.95 to 0.98 among classes). CONCLUSIONS Accurate, automated volumetric quantification of diffuse glioma tissue volumes may improve response assessment in clinical trials and reduce provider burden and errors in measurement.

PURPOSE We investigated associations between imaging biomarkers of cerebellar injury and neurocognitive function among primary brain tumor patients receiving partial brain radiotherapy (RT). METHODS On a prospective trial, 65 patients underwent volumetric brain MRI, diffusion tensor imaging, and memory, executive function, language, attention, and processing speed (PS) assessment pre-, 3, 6, and 12 months post-RT. Delis-Kaplan Executive Function System Trail Making [DKEFS-TM] Visual Scanning and Number and Letter Sequencing and Wechsler-Adult Intelligence Scale-IV [WAIS] Coding subtests evaluated PS. Cerebellar regions of interest (ROIs) were autosegmented, including cortex and white matter (WM). Supratentorial cortical and WM structures subserving the above cognitive domains were also autosegmented. Supratentorial PS-associated ROIs included the posterior, mid-posterior, central, mid-anterior, and anterior corpus callosum along with the bilateral intrahemispheric WM. Volume (cm3) was measured in all ROIs at each timepoint. Diffusion biomarkers (fractional anisotropy [FA] and mean diffusivity [MD]) were measured in all WM structures at each timepoint. Linear mixed-effects models assessed cerebellar biomarkers as predictors of neurocognitive scores. If found to be associated, cerebellar biomarkers were subsequently evaluated as predictors of cognitive scores controlling for domain-specific supratentorial ROI biomarkers. RESULTS Left (p=0.04) and right (p< 0.001) cerebellar WM volume declined over time. Cerebellar biomarkers were not associated with memory, executive function, or language. Left cerebellar cortex atrophy correlated with worse DKEFS-TM Number (p=0.01) and Letter (p=0.01) Sequencing scores. Right cerebellar cortex atrophy correlated with worse DKEFS-TM Visual Scanning (p=0.02) and Number (p=0.03) and Letter (p=0.02) Sequencing scores. Greater right cerebellar WM MD correlated with worse DKFS-TM Visual Scanning performance (p=0.03). These associations remained significant after controlling for corpus callosum and intrahemispheric WM injury biomarkers. CONCLUSION Biomarkers of cerebellar injury correlated with worse post-RT PS, independent of corpus callosum and intrahemispheric WM damage. Cerebellar dose avoidance may preserve PS.

Diffusion-weighted imaging (DWI) has been at the forefront of cancer imaging since the early 2000s. Before its application in clinical oncology, this powerful technique had already achieved widespread recognition due to its utility in the diagnosis of cerebral infarction. Following this initial success, the ability of DWI to detect inherent tissue contrast began to be exploited in the field of oncology. Although the initial oncologic applications for tumor detection and characterization, assessing treatment response, and predicting survival were primarily in the field of neurooncology, the scope of DWI has since broadened to include oncologic imaging of the prostate gland, breast, and liver. Despite its growing success and application, misconceptions about the underlying physical basis of the DWI signal exist among researchers and clinicians alike. In this review, we provide a detailed explanation of the biophysical basis of diffusion contrast, emphasizing the difference between hindered and restricted diffusion, and elucidating how diffusion parameters in tissue are derived from the measurements via the diffusion model. We describe one advanced DWI modeling technique, called restriction spectrum imaging (RSI). This technique offers a more direct in vivo measure of tumor cells, due to its ability to distinguish separable pools of water within tissue based on their intrinsic diffusion characteristics. Using RSI as an example, we then highlight the ability of advanced DWI techniques to address key clinical challenges in neurooncology, including improved tumor conspicuity, distinguishing actual response to therapy from pseudoresponse, and delineation of white matter tracts in regions of peritumoral edema. We also discuss how RSI, combined with new methods for correction of spatial distortions inherent in diffusion MRI scans, may enable more precise spatial targeting of lesions, with implications for radiation oncology and surgical planning. See all articles in this Cancer Research section, “Physics in Cancer Research.” Cancer Res; 74(17); 4638–52. ©2014 AACR.

Proton therapy in pediatrics may improve the risk/benefit profile of radiotherapy at a greater upfront financial cost, but it may prove to be cost effective if chronic medical complications can be avoided. Tools to assist with decision making are needed to aid in selecting pediatric patients for protons, and cost-effectiveness models can provide an objective method for this.A Markov cohort-simulation model was developed to assess the expected costs and effectiveness for specific radiation doses to the hypothalamus with protons versus photons in pediatric patients. Costing data included cost of investment and the diagnosis and management of growth hormone deficiency. Longitudinal outcomes data were used to inform risk parameters for the model. With costs in 2012 US dollars and effectiveness measured in quality-adjusted life years, incremental cost-effectiveness ratios were used to measure outcomes.Proton therapy was cost effective for some scenarios based on the difference in hypothalamic sparing. Although some scenarios were not cost effective, others were not only cost effective for proton therapy but also demonstrated that protons were cost saving compared with photons.The current results provide the first evidence-based guide for identifying children with brain tumors who may benefit the most from proton therapy with respect to endocrine dysfunction. Proton therapy may be more cost effective for scenarios in which radiation dose to the hypothalamus can be spared, but protons may not be cost effective when tumors are involving or directly adjacent to the hypothalamus if there is a high dose to this structure.