Response Assessment in Neuro-Oncology Criteria and Clinical Endpoints

Diagnostic validity and reliability of BT-RADS in the management of recurrent high-grade glioma

BACKGROUND AND PURPOSE

BT-RADS is a new framework system for reporting the treatment response of brain tumors. The aim of the study was to assess the diagnostic performance and reliability of the BT-RADS in predicting the recurrence of high-grade glioma (HGG).

MATERIALS AND METHODS

This prospective single-center study recruited 81 cases with previously operated and pathologically proven HGG. The patients underwent baseline and follow-up contrast-enhanced MRI (CE-MRI). Two neuro-radiologists with ten years-experience in neuroimaging independently analyzed and interpreted the MRI images and assigned a BT-RADS category for each case. To assess the diagnostic accuracy of the BT-RADS for detecting recurrent HGG, the reference standard was the histopathology for BT-RADS categories 3 and 4, while neurological clinical examination and clinical follow up were used as a reference for BT-RADS categories 1 and 2. The inter-reader agreement was assessed using the Cohen's Kappa test.

RESULTS

The study included 81 cases of HGG, of which 42 were recurrent and 39 were non-recurrent HGG cases based on the reference test. BT-RADS 3B was the best cutoff for predicting recurrent HGG with a sensitivity of 90.5 % to 92.9 %, specificity of 76.9 % to 84.6 %, and accuracy of 83.9 % to 88.9 %, based on both readers. The BT-RADS showed a substantial inter-reader agreement with a K of 0.710 (P = 0.001).

CONCLUSIONS

The BT-RADS is a valid and reliable framework for predicting recurrent HGG. Moreover, BT-RADS can help neuro-oncologists make clinical decisions that can potentially improve the patient's outcome.

Neurocognitive Effects and Necrosis in Childhood Cancer Survivors Treated With Radiation Therapy: A PENTEC Comprehensive Review

PURPOSE

A PENTEC review of childhood cancer survivors who received brain radiation therapy (RT) was performed to develop models that aid in developing dose constraints for RT-associated central nervous system (CNS) morbidities.

METHODS AND MATERIALS

A comprehensive literature search, through the PENTEC initiative, was performed to identify published data pertaining to 6 specific CNS toxicities in children treated with brain RT. Treatment and outcome data on survivors were extracted and used to generate normal tissue complication probability (NTCP) models.

RESULTS

The search identified investigations pertaining to 2 of the 6 predefined CNS outcomes: neurocognition and brain necrosis. For neurocognition, models for 2 post-RT outcomes were developed to (1) calculate the risk for a below-average intelligence quotient (IQ) (IQ <85) and (2) estimate the expected IQ value. The models suggest that there is a 5% risk of a subsequent IQ <85 when 10%, 20%, 50%, or 100% of the brain is irradiated to 35.7, 29.1, 22.2, or 18.1 Gy, respectively (all at 2 Gy/fraction and without methotrexate). Methotrexate (MTX) increased the risk for an IQ <85 similar to a generalized uniform brain dose of 5.9 Gy. The model for predicting expected IQ also includes the effect of dose, age, and MTX. Each of these factors has an independent, but probably cumulative effect on IQ. The necrosis model estimates a 5% risk of necrosis for children after 59.8 Gy or 63.6 Gy (2 Gy/fraction) to any part of the brain if delivered as primary RT or reirradiation, respectively.

CONCLUSIONS

This PENTEC comprehensive review establishes objective relationships between patient age, RT dose, RT volume, and MTX to subsequent risks of neurocognitive injury and necrosis. A lack of consistent RT data and outcome reporting in the published literature hindered investigation of the other predefined CNS morbidity endpoints.

Validating Brain Tumor Reporting and Data System (BT-RADS) as a Diagnostic Tool for Glioma Follow-Up after Surgery

Gliomas are a type of brain tumor that requires accurate monitoring for progression following surgery. The Brain Tumor Reporting and Data System (BT-RADS) has emerged as a potential tool for improving diagnostic accuracy and reducing the need for repeated operations. This prospective multicenter study aimed to evaluate the diagnostic accuracy and reliability of BT-RADS in predicting tumor progression (TP) in postoperative glioma patients and evaluate its acceptance in clinical practice. The study enrolled patients with a history of partial or complete resection of high-grade glioma. All patients underwent two consecutive follow-up brain MRI examinations. Five neuroradiologists independently evaluated the MRI examinations using the BT-RADS. The diagnostic accuracy of the BT-RADS for predicting TP was calculated using histopathology after reoperation and clinical and imaging follow-up as reference standards. Reliability based on inter-reader agreement (IRA) was assessed using kappa statistics. Reader acceptance was evaluated using a short survey. The final analysis included 73 patients (male, 67.1%; female, 32.9%; mean age, 43.2 ± 12.9 years; age range, 31-67 years); 47.9% showed TP, and 52.1% showed no TP. According to readers, TP was observed in 25-41.7% of BT-3a, 61.5-88.9% of BT-3b, 75-90.9% of BT-3c, and 91.7-100% of BT-RADS-4. Considering >BT-RADS-3a as a cutoff value for TP, the sensitivity, specificity, and accuracy of the BT-RADS were 68.6-85.7%, 84.2-92.1%, and 78.1-86.3%, respectively, according to the reader. The overall IRA was good (κ = 0.75) for the final BT-RADS classification and very good for detecting new lesions (κ = 0.89). The readers completely agreed with the statement "the application of the BT-RADS should be encouraged" (score = 25). The BT-RADS has good diagnostic accuracy and reliability for predicting TP in postoperative glioma patients. However, BT-RADS 3 needs further improvements to increase its diagnostic accuracy.

Volumetric measurement of intracranial meningiomas: a comparison between linear, planimetric, and machine learning with multiparametric voxel-based morphometry methods

PURPOSE

To compare the accuracy of three volumetric methods in the radiological assessment of meningiomas: linear (ABC/2), planimetric, and multiparametric machine learning-based semiautomated voxel-based morphometry (VBM), and to investigate the relevance of tumor shape in volumetric error.

METHODS

Retrospective imaging database analysis at the authors' institutions. We included patients with a confirmed diagnosis of meningioma and preoperative cranial magnetic resonance imaging eligible for volumetric analyses. After tumor segmentation, images underwent automated computation of shape properties such as sphericity, roundness, flatness, and elongation.

RESULTS

Sixty-nine patients (85 tumors) were included. Tumor volumes were significantly different using linear (13.82 cm³ [range 0.13-163.74 cm³]), planimetric (11.66 cm³ [range 0.17-196.2 cm³]) and VBM methods (10.24 cm³ [range 0.17-190.32 cm³]) (p < 0.001). Median volume and percentage errors between the planimetric and linear methods and the VBM method were 1.08 cm³ and 11.61%, and 0.23 cm³ and 5.5%, respectively. Planimetry and linear methods overestimated the actual volume in 79% and 63% of the patients, respectively. Correlation studies showed excellent reliability and volumetric agreement between manual- and computer-based methods. Larger and flatter tumors had greater accuracy on planimetry, whereas less rounded tumors contributed negatively to the accuracy of the linear method.

CONCLUSION

Semiautomated VBM volumetry for meningiomas is not influenced by tumor shape properties, whereas planimetry and linear methods tend to overestimate tumor volume. Furthermore, it is necessary to consider tumor roundness prior to linear measurement so as to choose the most appropriate method for each patient on an individual basis.

Evolution and implementation of radiographic response criteria in neuro-oncology

Radiographic response assessment in neuro-oncology is critical in clinical practice and trials. Conventional criteria, such as the MacDonald and response assessment in neuro-oncology (RANO) criteria, rely on bidimensional (2D) measurements of a single tumor cross-section. Although RANO criteria are established for response assessment in clinical trials, there is a critical need to address the complexity of brain tumor treatment response with multiple new approaches being proposed. These include volumetric analysis of tumor compartments, structured MRI reporting systems like the Brain Tumor Reporting and Data System, and standardized approaches to advanced imaging techniques to distinguish tumor response from treatment effects. In this review, we discuss the strengths and limitations of different neuro-oncology response criteria and summarize current research findings on the role of novel response methods in neuro-oncology clinical trials and practice.

A brain tumor reporting and data system to optimize imaging surveillance and prognostication in high-grade gliomas

BACKGROUND AND PURPOSE

High-grade glioma (HGG), including glioblastoma, is the most common primary brain neoplasm and has a dismal prognosis. After initial treatment, follow-up decisions are guided by longitudinal MRI performed at routine intervals. The Brain Tumor Reporting and Data System (BT-RADS) is a proposed structured reporting system for posttreatment brain MRIs. The purpose of this study is to determine the relationship between BT-RADS scores and overall survival in HGG patients.

METHODS

Chart review of grade 4 glioma patients who had an MRI at a single institution from November 2018 to November 2019 was performed. BT-RADS scores, tumor characteristics, and overall survival were recorded. Likelihood of improvement, stability, or worsening on the subsequent study was calculated for each score. Survival analysis was performed using Kaplan-Meier method, log-rank test, and a time-dependent cox model. Significance level of .05 was used.

RESULTS

The study identified 91 HGG patients who underwent a total of 538 MRIs. Mean age of patients was 57 years old. Score with the highest likelihood for worsening on the next follow-up was 3b. The risk of death was 53% higher with each incremental increase in BT-RADS scores (hazard ratio, 1.53; 95% confidence interval [CI], 1.07-2.19; p = .019). The risk of death was 167% higher in O-6-methylguanine-DNA-methyltransferase unmethylated tumors (hazard ratio, 2.67; 95% CI, 1.34-5.33; p = .005).

CONCLUSIONS

BT-RADS scores can be used as a reference guide to anticipate whether patients' subsequent MRI will be improved, stable, or worsened. The scoring system can also be used to predict clinical outcomes and prognosis.

Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging

Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.

Predictors of Lung Adenocarcinoma With Leptomeningeal Metastases: A 2022 Targeted-Therapy-Assisted molGPA Model

Objective

To explore prognostic indicators of lung adenocarcinoma with leptomeningeal metastases (LM) and provide an updated graded prognostic assessment model integrated with molecular alterations (molGPA).

Methods

A cohort of 162 patients was enrolled from 202 patients with lung adenocarcinoma and LM. By randomly splitting data into the training (80%) and validation (20%) sets, the Cox regression and random survival forest methods were used on the training set to identify statistically significant variables and construct a prognostic model. The C-index of the model was calculated and compared with that of previous molGPA models.

Results

The Cox regression and random forest models both identified four variables, which included KPS, LANO neurological assessment, TKI therapy line, and controlled primary tumor, as statistically significant predictors. A novel targeted-therapy-assisted molGPA model (2022) using the above four prognostic factors was developed to predict LM of lung adenocarcinoma. The C-indices of this prognostic model in the training and validation sets were higher than those of the lung-molGPA (2017) and molGPA (2019) models.

Conclusions

The 2022 molGPA model, a substantial update of previous molGPA models with better prediction performance, may be useful in clinical decision making and stratification of future clinical trials.

Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma

BACKGROUND AND PURPOSE

Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of treatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.

MATERIALS AND METHODS

Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma (n = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites (n = 60-74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites (n = 29-43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points.

RESULTS

The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index  = 0.472-0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692-0.750) and progression-free survival (concordance index = 0.680-0.715).

CONCLUSIONS

A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.

Object Detection Improves Tumour Segmentation in MR Images of Rare Brain Tumours

Simple Summary

This study evaluates the impact of adding an object detection framework into brain tumour segmentation models, especially when the models are applied to different domains. In recent years, multiple models have been successfully applied to brain tumour segmentation tasks. However, the performance and stability of these models have never been evaluated when the training and target domain differ. In this study, we identify object detection as a simpler problem that can be injected into a segmentation model as an a priori, and which can increase the performance of our models. We propose an automatic segmentation model that, without model retraining or adaptation, showed good results when applied to a rare brain tumour.

Abstract

Tumour lesion segmentation is a key step to study and characterise cancer from MR neuroradiological images. Presently, numerous deep learning segmentation architectures have been shown to perform well on the specific tumour type they are trained on (e.g., glioblastoma in brain hemispheres). However, a high performing network heavily trained on a given tumour type may perform poorly on a rare tumour type for which no labelled cases allows training or transfer learning. Yet, because some visual similarities exist nevertheless between common and rare tumours, in the lesion and around it, one may split the problem into two steps: object detection and segmentation. For each step, trained networks on common lesions could be used on rare ones following a domain adaptation scheme without extra fine-tuning. This work proposes a resilient tumour lesion delineation strategy, based on the combination of established elementary networks that achieve detection and segmentation. Our strategy allowed us to achieve robust segmentation inference on a rare tumour located in an unseen tumour context region during training. As an example of a rare tumour, Diffuse Intrinsic Pontine Glioma (DIPG), we achieve an average dice score of 0.62 without further training or network architecture adaptation.

Targeting Primary Motor Cortex (M1) Functional Components in M1 Gliomas Enhances Safe Resection and Reveals M1 Plasticity Potentials

Simple Summary

Primary-Motor-Cortex (M1) hosts two functional components, at its posterior and anterior borders, the first being faster and more excitable than the second. Our study reports a novel technique for the on-line identification of these functional components during M1 tumors resection. It reports for the first time the potential plastic reorganization of M1 and specifically how its functional organization is affected by a growing tumor and correlated to clinical, tumor-related factors and patient motor functional performance. It also shows for the first time that detecting the M1 functional architecture and targeting the two M1 functional components facilitates tumor resection, increasing the rate of complete tumor removal, while maintaining the patient’s functional motor capacity.

Abstract

Primary-Motor-Cortex (M1) hosts two functional components, at its posterior and anterior borders, being the first faster and more excitable. We developed a mapping-technique for M1 components identification and determined their functional cortical-subcortical architecture in M1 gliomas and the impact of their identification on tumor resection and motor performance. A novel advanced mapping technique was used in 102 tumors within M1 or CorticoSpinal-Tract to identify M1-two components. High-Frequency-stimulation (2–5 pulses) with an on-line qualitative and quantitative analysis of motor responses was used; the two components’ cortical/subcortical spatial distribution correlated to clinical, tumor-related factor and patients’ motor outcome; a cohort treated with standard-mapping was used for comparison. The two functional components were always identified on-line; in tumors not affecting M1, its functional segregation was preserved. In M1 tumors, two architectures, both preserving the two components, were disclosed: in 50%, a normal cortical/subcortical architecture emerged, while 50% revealed a distorted architecture with loss of anatomical reference and somatotopy, not associated with tumor histo-molecular features or volume, but with a previous treatment. Motor performance was maintained, suggesting functional compensation. By preserving the highest and resecting the lowest excitability component, the complete-resection increased with low morbidity. The real-time identification of two M1 functional components and the preservation of the highest excitability one increases safe resection, revealing M1 plasticity potentials.

The Longitudinal Imaging Tracker (BrICS-LIT):A Cloud Platform for Monitoring Treatment Response in Glioblastoma Patients

Glioblastoma is a common and aggressive form of brain cancer affecting up to 20,000 new patients in the US annually. Despite rigorous therapies, current median survival is only 15-20 months. Patients who complete initial treatment undergo follow-up imaging at routine intervals to assess for tumor recurrence. Imaging is a central part of brain tumor management, but MRI findings in patients with brain tumor can be challenging to interpret and are further confounded by interpretation variability. Disease-specific structured reporting attempts to reduce variability in imaging results by implementing well-defined imaging criteria and standardized language. The Brain Tumor Reporting and Data System (BT-RADS) is one such framework streamlined for clinical workflows and includes quantitative criteria for more objective evaluation of follow-up imaging. To facilitate accurate and objective monitoring of patients during the follow-up period, we developed a cloud platform, the Brain Imaging Collaborative Suite's Longitudinal Imaging Tracker (BrICS-LIT). BrICS-LIT uses semiautomated tumor segmentation algorithms of both T2-weighted FLAIR and contrast-enhanced T1-weighted MRI to assist clinicians in quantitative assessment of brain tumors. The LIT platform can ultimately guide clinical decision-making for patients with glioblastoma by providing quantitative metrics for BT-RADS scoring. Further, this platform has the potential to increase objectivity when measuring efficacy of novel therapies for patients with brain tumor during their follow-up. Therefore, LIT will be used to track patients in a dose-escalated clinical trial, where spectroscopic MRI has been used to guide radiation therapy (Clinicaltrials.gov NCT03137888), and compare patients to a control group that received standard of care.

Transformational Role of Medical Imaging in (Radiation) Oncology

Simple Summary

Onboard, imaging techniques have brought about a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables us to better visualize where to deliver lethal doses of radiation and thus allows the shrinking of necessary geometric margins leading to reduced toxicities. Alongside improvements in treatment delivery, advances in medical imaging have also allowed us to better define the volumes we wish to target. The development of imaging techniques that can capture aspects of the tumor’s biology before, during and after therapy is transforming how treatment can be delivered. Technological changes have further made these biological imaging techniques available in real-time providing the opportunity to monitor a patient’s response to treatment closely and often before any volume changes are visible on conventional radiological images. Here we discuss the development of robust quantitative imaging biomarkers and how they can personalize therapy towards meaningful clinical endpoints.

Abstract

Onboard, real-time, imaging techniques, from the original megavoltage planar imaging devices, to the emerging combined MRI-Linear Accelerators, have brought a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables lethal doses of radiation to be delivered to target volumes with progressively more accuracy and thus allows shrinking of necessary geometric margins, leading to reduced toxicities. Alongside these improvements in treatment delivery, advances in medical imaging, e.g., PET, and MRI, have also allowed target volumes themselves to be better defined. The development of functional and molecular imaging is now driving a conceptually larger step transformation to both better understand the cancer target and disease to be treated, as well as how tumors respond to treatment. A biological description of the tumor microenvironment is now accepted as an essential component of how to personalize and adapt treatment. This applies not only to radiation oncology but extends widely in cancer management from surgical oncology planning and interventional radiology, to evaluation of targeted drug delivery efficacy in medical oncology/immunotherapy. Here, we will discuss the role and requirements of functional and metabolic imaging techniques in the context of brain tumors and metastases to reliably provide multi-parametric imaging biomarkers of the tumor microenvironment.

The rationale and development of a CyberKnife© registry for pediatric patients with CNS lesions

BACKGROUND

CyberKnife© Radiosurgery (CKRS) is a recognized treatment concept for CNS lesions in adults due to its high precision and efficacy beside a high patient comfort. However, scientific evidence for this treatment modality in pediatric patients is scarce. A dedicated registry was designed to document CyberKnife© procedures in children, aiming to test the hypothesis that it is safe and efficient for the treatment of CNS lesions.

METHODS

The CyberKnife© registry is designed as a retrospective and prospective multicenter observational study (German Clinical Trials Register ( https://www.drks.de ), DRKS-ID 00016973). Patient recruitment will be ongoing throughout a 5-year period and includes collection of demographic, treatment, clinical, and imaging data. Follow-up results will be monitored for 10 years. All data will be registered in a centralized electronic database at the Charité-Universitätsmedizin. The primary endpoint is stable disease for benign and vascular lesions at 5 years of follow-up and local tumor control for malign lesions at 1- and 2-year follow-up. Secondary endpoints are radiation toxicity, side effects, and neurocognitive development.

CONCLUSION

The CyberKnife© registry intends to generate scientific evidence for all treatment- and outcome-related aspects in pediatric patients with treated CNS lesions. The registry may define safety and efficacy of CKRS in children and serve as a basis for future clinical trials, inter-methodological comparisons and changes of treatment algorithms.

Quantitative Improvement in Brain Tumor MRI Through Structured Reporting (BT-RADS)

RATIONALE AND OBJECTIVES

Determine the objective benefits of structured reporting of brain tumors through Brain tumor-RADS (BT-RADS) by analyzing discrete quantifiable metrics of the reports themselves.

MATERIALS AND METHODS

Following Institutional Review Board approval, post-treatment glioma reports were acquired from two matched 3-month time periods for pre- and postimplementation of BT-RADS. The reports were analyzed for presence of history words, such as "Avastin" and "methylguanine-DNA methyltransferase," as well as hedge words, such as "Possibly" and "Likely." The word counts of the total report and of the impression section were also assessed, as well as whether or not the report contained addenda.

RESULTS

In total, 211 pre-BT-RADS and 172 post-BT-RADS reports were analyzed. Post-BT-RADS reports demonstrated greater reporting of history words, including "Avastin" (7.6% vs. 20.9%, p < 0.001) and "methylguanine-DNA methyltransferase" (10.9% vs. 31.4%, p < 0.0001). They also demonstrated reduced usage of hedge words, including "Possibly" (3.8% vs. 0.6%, p < 0.05) and "Likely" (49.8% vs. 28.5%, p < 0.01). Furthermore, post-BT-RADS reports possessed fewer words in total report length (389 vs. 245.2, p < 0.001), as well as in the impression section (53.7 vs. 42.6, p < 0.01). Finally, fewer post-BT-RADS reports contained addenda (10% vs. 1.2%, p < 0.01).

CONCLUSION

Following implementation of BT-RADS, glioma reports demonstrated greater consistency and completeness of clinical history, less ambiguity, and more conciseness.

Current and Future Imaging Methods for Evaluating Response to Immunotherapy in Neuro-Oncology

Imaging plays a central role in evaluating responses to therapy in neuro-oncology patients. The advancing clinical use of immunotherapies has demonstrated that treatment-related inflammatory responses mimic tumor growth via conventional imaging, thus spurring the development of new imaging approaches to adequately distinguish between pseudoprogression and progressive disease. To this end, an increasing number of advanced imaging techniques are being evaluated in preclinical and clinical studies. These novel molecular imaging approaches will serve to complement conventional response assessments during immunotherapy. The goal of these techniques is to provide definitive metrics of tumor response at earlier time points to inform treatment decisions, which has the potential to improve patient outcomes. This review summarizes the available immunotherapy regimens, clinical response criteria, current state-of-the-art imaging approaches, and groundbreaking strategies for future implementation to evaluate the anti-tumor and immune responses to immunotherapy in neuro-oncology applications.

Institutional Implementation of a Structured Reporting System: Our Experience with the Brain Tumor Reporting and Data System

RATIONALE AND OBJECTIVES

Analyze the impact of implementing a structured reporting system for primary brain tumors, the Brain Tumor Reporting and Data System, on attitudes toward radiology reports at a single institution.

MATERIALS AND METHODS

Following Institutional Review Board approval, an initial 22 question, 5 point (1-worst to 5-best), survey was sent to faculty members, house staff members, and nonphysician providers at our institution who participate in the direct care of brain tumor patients. Results were used to develop a structured reporting strategy for brain tumors which was implemented across an entire neuroradiology section in a staged approach. Nine months following structured reporting implementation, a follow-up 27 question survey was sent to the same group of providers. Keyword search of radiology reports was used to assess usage of Brain Tumor Reporting and Data System over time.

RESULTS

Fifty-three brain tumor care providers responded to the initial survey and 38 to the follow-up survey. After implementing BT-RADS, respondents reported improved attitudes across multiple areas including: report consistency (4.3 vs. 3.4; p < 0.001), report ambiguity (4.2 vs. 3.2, p < 0.001), radiologist/physician communication (4.5 vs. 3.8; p < 0.001), facilitation of patient management (4.2 vs. 3.6; p = 0.003), and confidence in reports (4.3 vs. 3.5; p < 0.001). Providers were more satisfied with the BT-RADS structured reporting system (4.3 vs. 3.7; p = 0.04). Use of the reporting template progressively increased with 81% of brain tumor reports dictated using the new template by 9 months.

CONCLUSION

Implementing a structured template for brain tumor imaging improves perception of radiology reports among radiologists and referring providers involved in the care of brain tumor patients.

Emerging Gene Fusion Drivers in Primary and Metastatic Central Nervous System Malignancies: A Review of Available Evidence for Systemic Targeted Therapies

Primary and metastatic tumors of the central nervous system present a difficult clinical challenge, and they are a common cause of disease progression and death. For most patients, treatment consists primarily of surgery and/or radiotherapy. In recent years, systemic therapies have become available or are under investigation for patients whose tumors are driven by specific genetic alterations, and some of these targeted treatments have been associated with dramatic improvements in extracranial and intracranial disease control and survival. However, the success of other systemic therapies has been hindered by inadequate penetration of the drug into the brain parenchyma. Advances in molecular characterization of oncogenic drivers have led to the identification of new gene fusions driving oncogenesis in some of the most common sources of intracranial tumors. Systemic therapies targeting many of these alterations have been approved recently or are in clinical development, and the ability to penetrate the blood-brain barrier is now widely recognized as an important property of such drugs. We review this rapidly advancing field with a focus on recently uncovered gene fusions and brain-penetrant systemic therapies targeting them.

IMPLICATIONS FOR PRACTICE

Driver gene fusions involving receptor tyrosine kinases have been identified across a wide range of tumor types, including primary central nervous system (CNS) tumors and extracranial solid tumors that are associated with high rates of metastasis to the CNS (e.g., lung, breast, melanoma). This review discusses the systemic therapies that target emerging gene fusions, with a focus on brain-penetrant agents that will target the intracranial disease and, where present, also extracranial disease.

Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors for Central Nervous System Metastases from Non-Small Cell Lung Cancer

Central nervous system (CNS) metastases are a common complication in patients with epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC), resulting in a poor prognosis and limited treatment options. Treatment of CNS metastases requires a multidisciplinary approach, and the optimal treatment options and sequence of therapies are yet to be established. Many systemic therapies have poor efficacy in the CNS due to the challenges of crossing the blood-brain barrier (BBB), creating a major unmet need for the development of agents with good BBB-penetrating biopharmaceutical properties. Although the CNS penetration of first- and second-generation EGFR tyrosine kinase inhibitors (TKIs) is generally low, EGFR-TKI treatment has been shown to delay time to CNS progression in patients with CNS metastases from EGFR-mutated disease. However, a major challenge with EGFR-TKI treatment for patients with NSCLC is the development of acquired resistance, which occurs in most patients treated with a first-line EGFR-TKI. Novel EGFR-TKIs, such as osimertinib, have been specifically designed to address the challenges of acquired resistance and poor BBB permeability and have demonstrated efficacy in the CNS. A rational, iterative drug development process to design agents that could penetrate the BBB could prevent morbidity and mortality associated with CNS disease progression. To ensure a consistent approach to evaluating CNS efficacy, special consideration also needs to be given to clinical trial endpoints.

IMPLICATIONS FOR PRACTICE

Historically, treatment options for patients who develop central nervous system (CNS) metastases have been limited and associated with poor outcomes. The development of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) has improved outcomes for patients with EGFR-mutated disease, and emerging data have demonstrated the ability of these drugs to cross the blood-brain barrier and elicit significant intracranial responses. Recent studies have indicated a role for next-generation EGFR-TKIs, such as osimertinib, in the treatment of CNS metastases. In the context of an evolving treatment paradigm, treatment should be individualized to the patient and requires a multidisciplinary approach.

An Update on the Approach to the Imaging of Brain Tumors

PURPOSE OF REVIEW

Neuroimaging plays a critical role in diagnosis of brain tumors and in assessment of response to therapy. However, challenges remain, including accurately and reproducibly assessing response to therapy, defining endpoints for neuro-oncology trials, providing prognostic information, and differentiating progressive disease from post-therapeutic changes particularly in the setting of antiangiogenic and other novel therapies.

RECENT FINDINGS

Recent advances in the imaging of brain tumors include application of advanced MRI imaging techniques to assess tumor response to therapy and analysis of imaging features correlating to molecular markers, grade, and prognosis. This review aims to summarize recent advances in imaging as applied to current diagnostic and therapeutic neuro-oncologic challenges.