Management-Based Structured Reporting of Posttreatment Glioma Response With the Brain Tumor Reporting and Data System

RANO 2.0 criteria: concepts applicable to the neuroradiologist's clinical practice

PURPOSE OF REVIEW

The Response Assessment in Neuro-Oncology (RANO) 2.0 criteria aim at improving the standardization and reliability of treatment response assessment in clinical trials studying central nervous system (CNS) gliomas. This review presents the evidence supporting RANO 2.0 updates and discusses which concepts can be applicable to the clinical practice, particularly in the clinical radiographic reads.

RECENT FINDINGS

Updates in RANO 2.0 were supported by recent retrospective analyses of multicenter data from recent clinical trials. As proposed in RANO 2.0, in tumors receiving radiation therapy, the post-RT MRI scan should be used as a reference baseline for the following scans, as opposed to the pre-RT scan, and radiographic findings suggesting progression within three months after radiation therapy completion should be verified with confirmatory scans. Volumetric assessments should be considered, when available, especially for low-grade gliomas, and the evaluation of nonenhancing disease should have a marginal role in glioblastoma. However, the radiographic reads in the clinical setting also benefit from aspects that lie outside RANO 2.0 criteria, such as qualitative evaluations, patient-specific clinical considerations, and advanced imaging.

SUMMARY

While RANO 2.0 criteria are meant for the standardization of the response assessment in clinical trials, some concepts have the potential to improve patients' management in the clinical practice.

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.

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.

Comparison of Volumetric and 2D Measurements and Longitudinal Trajectories in the Response Assessment of BRAF V600E-Mutant Pediatric Gliomas in the Pacific Pediatric Neuro-Oncology Consortium Clinical Trial

BACKGROUND AND PURPOSE

Response on imaging is widely used to evaluate treatment efficacy in clinical trials of pediatric gliomas. While conventional criteria rely on 2D measurements, volumetric analysis may provide a more comprehensive response assessment. There is sparse research on the role of volumetrics in pediatric gliomas. Our purpose was to compare 2D and volumetric analysis with the assessment of neuroradiologists using the Brain Tumor Reporting and Data System (BT-RADS) in BRAF V600E-mutant pediatric gliomas.

MATERIALS AND METHODS

Manual volumetric segmentations of whole and solid tumors were compared with 2D measurements in 31 participants (292 follow-up studies) in the Pacific Pediatric Neuro-Oncology Consortium 002 trial (NCT01748149). Two neuroradiologists evaluated responses using BT-RADS. Receiver operating characteristic analysis compared classification performance of 2D and volumetrics for partial response. Agreement between volumetric and 2D mathematically modeled longitudinal trajectories for 25 participants was determined using the model-estimated time to best response.

RESULTS

Of 31 participants, 20 had partial responses according to BT-RADS criteria. Receiver operating characteristic curves for the classification of partial responders at the time of first detection (median = 2 months) yielded an area under the curve of 0.84 (95% CI, 0.69-0.99) for 2D area, 0.91 (95% CI, 0.80-1.00) for whole-volume, and 0.92 (95% CI, 0.82-1.00) for solid volume change. There was no significant difference in the area under the curve between 2D and solid (P = .34) or whole volume (P = .39). There was no significant correlation in model-estimated time to best response (ρ = 0.39, P >.05) between 2D and whole-volume trajectories. Eight of the 25 participants had a difference of ≥90 days in transition from partial response to stable disease between their 2D and whole-volume modeled trajectories.

CONCLUSIONS

Although there was no overall difference between volumetrics and 2D in classifying partial response assessment using BT-RADS, further prospective studies will be critical to elucidate how the observed differences in tumor 2D and volumetric trajectories affect clinical decision-making and outcomes in some individuals.

Arterial Spin-Labeling and DSC Perfusion Metrics Improve Agreement in Neuroradiologists' Clinical Interpretations of Posttreatment High-Grade Glioma Surveillance MR Imaging-An Institutional Experience

BACKGROUND AND PURPOSE

MR perfusion has shown value in the evaluation of posttreatment high-grade gliomas, but few studies have shown its impact on the consistency and confidence of neuroradiologists' interpretation in routine clinical practice. We evaluated the impact of adding MR perfusion metrics to conventional contrast-enhanced MR imaging in posttreatment high-grade glioma surveillance imaging.

MATERIALS AND METHODS

This retrospective study included 45 adults with high-grade gliomas who had posttreatment perfusion MR imaging. Four neuroradiologists assigned Brain Tumor Reporting and Data System scores for each examination on the basis of the interpretation of contrast-enhanced MR imaging and then after the addition of arterial spin-labeling-CBF, DSC-relative CBV, and DSC-fractional tumor burden. Interrater agreement and rater agreement with a multidisciplinary consensus group were assessed with κ statistics. Raters used a 5-point Likert scale to report confidence scores. The frequency of clinically meaningful score changes resulting from the addition of each perfusion metric was determined.

RESULTS

Interrater agreement was moderate for contrast-enhanced MR imaging alone (κ = 0.63) and higher with perfusion metrics (arterial spin-labeling-CBF, κ = 0.67; DSC-relative CBV, κ = 0.66; DSC-fractional tumor burden, κ = 0.70). Agreement between raters and consensus was highest with DSC-fractional tumor burden (κ = 0.66-0.80). Confidence scores were highest with DSC-fractional tumor burden. Across all raters, the addition of perfusion resulted in clinically meaningful interpretation changes in 2%-20% of patients compared with contrast-enhanced MR imaging alone.

CONCLUSIONS

Adding perfusion to contrast-enhanced MR imaging improved interrater agreement, rater agreement with consensus, and rater confidence in the interpretation of posttreatment high-grade glioma MR imaging, with the highest agreement and confidence scores seen with DSC-fractional tumor burden. Perfusion MR imaging also resulted in interpretation changes that could change therapeutic management in up to 20% of patients.

The role of gadolinium-based contrast agents in magnetic resonance imaging structured reporting and data systems (RADS)

Among the 28 reporting and data systems (RADS) available in the literature, we identified 15 RADS that can be used in Magnetic Resonance Imaging (MRI). Performing examinations without using gadolinium-based contrast agents (GBCA) has benefits, but GBCA administration is often required to achieve an early and accurate diagnosis. The aim of the present review is to summarize the current role of GBCA in MRI RADS. This overview suggests that GBCA are today required in most of the current RADS and are expected to be used in most MRIs performed in patients with cancer. Dynamic contrast enhancement is required for correct scores calculation in PI-RADS and VI-RADS, although scientific evidence may lead in the future to avoid the GBCA administration in these two RADS. In Bone-RADS, contrast enhancement can be required to classify an aggressive lesion. In RADS scoring on whole body-MRI datasets (MET-RADS-P, MY-RADS and ONCO-RADS), in NS-RADS and in Node-RADS, GBCA administration is optional thanks to the intrinsic high contrast resolution of MRI. Future studies are needed to evaluate the impact of the high T1 relaxivity GBCA on the assignment of RADS scores.

Comparison of volumetric and 2D-based response methods in the PNOC-001 pediatric low-grade glioma clinical trial

Background

Although response in pediatric low-grade glioma (pLGG) includes volumetric assessment, more simplified 2D-based methods are often used in clinical trials. The study's purpose was to compare volumetric to 2D methods.

Methods

An expert neuroradiologist performed solid and whole tumor (including cyst and edema) volumetric measurements on MR images using a PACS-based manual segmentation tool in 43 pLGG participants (213 total follow-up images) from the Pacific Pediatric Neuro-Oncology Consortium (PNOC-001) trial. Classification based on changes in volumetric and 2D measurements of solid tumor were compared to neuroradiologist visual response assessment using the Brain Tumor Reporting and Data System (BT-RADS) criteria for a subset of 65 images using receiver operating characteristic (ROC) analysis. Longitudinal modeling of solid tumor volume was used to predict BT-RADS classification in 54 of the 65 images.

Results

There was a significant difference in ROC area under the curve between 3D solid tumor volume and 2D area (0.96 vs 0.78, P = .005) and between 3D solid and 3D whole volume (0.96 vs 0.84, P = .006) when classifying BT-RADS progressive disease (PD). Thresholds of 15-25% increase in 3D solid tumor volume had an 80% sensitivity in classifying BT-RADS PD included in their 95% confidence intervals. The longitudinal model of solid volume response had a sensitivity of 82% and a positive predictive value of 67% for detecting BT-RADS PD.

Conclusions

Volumetric analysis of solid tumor was significantly better than 2D measurements in classifying tumor progression as determined by BT-RADS criteria and will enable more comprehensive clinical management.

What to report in sellar tumor MRI? A nationwide survey among German pituitary surgeons, radiation oncologists, and endocrinologists

PURPOSE

While MRI has become the imaging modality of choice in the diagnosis of sellar tumors, no systematic attempt has yet been made to align radiological reporting of findings with the information needed by the various medical disciplines dealing with these patients. Therefore, we aimed to determine the prevailing preferences in this regard through a nationwide expert survey.

METHODS

First, an interdisciplinary literature-based catalog of potential reporting elements for sellar tumor MRI examinations was created. Subsequently, a web-based survey regarding the clinical relevance of these items was conducted among board certified members of the German Society of Neurosurgery, German Society of Radiation Oncology, and the Pituitary Working Group of the German Society of Endocrinology.

RESULTS

A total of 95 experts (40 neurosurgeons, 28 radiation oncologists, and 27 endocrinologists) completed the survey. The description of the exact tumor location, size, and involvement of the anatomic structures adjacent to the sella turcica (optic chiasm, cavernous sinus, and skull base), occlusive hydrocephalus, relationship to the pituitary gland and infundibulum, and certain structural characteristics of the mass (cyst formation, hemorrhage, and necrosis) was rated most important (> 75% agreement). In contrast, the characterization of anatomic features of the nasal cavity and sphenoid sinus as well as the findings of advanced MRI techniques (e.g., perfusion and diffusion imaging) was considered relevant by less than 50% of respondents.

CONCLUSION

To optimally address the information needs of the interdisciplinary treatment team, MRI reports of sellar masses should primarily focus on the accurate description of tumor location, size, internal structure, and involvement of adjacent anatomic compartments.

Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting

For patients suffering from central nervous system tumors, prognosis estimation, treatment decisions, and postoperative assessments are made from the analysis of a set of magnetic resonance (MR) scans. Currently, the lack of open tools for standardized and automatic tumor segmentation and generation of clinical reports, incorporating relevant tumor characteristics, leads to potential risks from inherent decisions' subjectivity. To tackle this problem, the proposed Raidionics open-source software has been developed, offering both a user-friendly graphical user interface and stable processing backend. The software includes preoperative segmentation models for each of the most common tumor types (i.e., glioblastomas, lower grade gliomas, meningiomas, and metastases), together with one early postoperative glioblastoma segmentation model. Preoperative segmentation performances were quite homogeneous across the four different brain tumor types, with an average Dice around 85% and patient-wise recall and precision around 95%. Postoperatively, performances were lower with an average Dice of 41%. Overall, the generation of a standardized clinical report, including the tumor segmentation and features computation, requires about ten minutes on a regular laptop. The proposed Raidionics software is the first open solution enabling an easy use of state-of-the-art segmentation models for all major tumor types, including preoperative and postsurgical standardized reports.

Using Brain Tumor MRI Structured Reporting to Quantify the Impact of Imaging on Brain Tumor Boards

Multidisciplinary tumor boards (TB) are an essential part of brain tumor care, but quantifying the impact of imaging on patient management is challenging due to treatment complexity and a lack of quantitative outcome measures. This work uses a structured reporting system for classifying brain tumor MRIs, the brain tumor reporting and data system (BT-RADS), in a TB setting to prospectively assess the impact of imaging review on patient management. Published criteria were used to prospectively assign three separate BT-RADS scores (an initial radiology report, secondary TB presenter review, and TB consensus) to brain MRIs reviewed at an adult brain TB. Clinical recommendations at TB were noted and management changes within 90 days after TB were determined by chart review. In total, 212 MRIs in 130 patients (median age = 57 years) were reviewed. Agreement was 82.2% between report and presenter, 79.0% between report and consensus, and 90.1% between presenter and consensus. Rates of management change increased with increasing BT-RADS scores (0-3.1%, 1a-0%, 1b-66.7%, 2-8.3%, 3a-38.5%, 3b-55.9, 3c-92.0%, and 4-95.6%). Of 184 (86.8%) cases with clinical follow-up within 90 days after the tumor board, 155 (84.2%) of the recommendations were implemented. Structured scoring of MRIs provides a quantitative way to assess rates of agreement interpretation alongside how often management changes are recommended and implemented in a TB setting.

Direct Evaluation of Treatment Response in Brain Metastatic Disease with Deep Neuroevolution

Cancer centers have an urgent and unmet clinical and research need for AI that can guide patient management. A core component of advancing cancer treatment research is assessing response to therapy. Doing so by hand, for example, as per RECIST or RANO criteria, is tedious and time-consuming, and can miss important tumor response information. Most notably, the prevalent response criteria often exclude lesions, the non-target lesions, altogether. We wish to assess change in a holistic fashion that includes all lesions, obtaining simple, informative, and automated assessments of tumor progression or regression. Because genetic sub-types of cancer can be fairly specific and patient enrollment in therapy trials is often limited in number and accrual rate, we wish to make response assessments with small training sets. Deep neuroevolution (DNE) is a novel radiology artificial intelligence (AI) optimization approach that performs well on small training sets. Here, we use a DNE parameter search to optimize a convolutional neural network (CNN) that predicts progression versus regression of metastatic brain disease. We analyzed 50 pairs of MRI contrast-enhanced images as our training set. Half of these pairs, separated in time, qualified as disease progression, while the other 25 image pairs constituted regression. We trained the parameters of a CNN via "mutations" that consisted of random CNN weight adjustments and evaluated mutation "fitness" as summed training set accuracy. We then incorporated the best mutations into the next generation's CNN, repeating this process for approximately 50,000 generations. We applied the CNNs to our training set, as well as a separate testing set with the same class balance of 25 progression and 25 regression cases. DNE achieved monotonic convergence to 100% training set accuracy. DNE also converged monotonically to 100% testing set accuracy. We have thus shown that DNE can accurately classify brain metastatic disease progression versus regression. Future work will extend the input from 2D image slices to full 3D volumes, and include the category of "no change." We believe that an approach such as ours can ultimately provide a useful and informative complement to RANO/RECIST assessment and volumetric AI analysis.

Spectroscopic MRI-Guided Proton Therapy in Non-Enhancing Pediatric High-Grade Glioma

Radiation therapy (RT) is a critical part of definitive therapy for pediatric high-grade glioma (pHGG). RT is designed to treat residual tumor defined on conventional MRI (cMRI), though pHGG lesions may be ill-characterized on standard imaging. Spectroscopic MRI (sMRI) measures endogenous metabolite concentrations in the brain, and Choline (Cho)/N-acetylaspartate (NAA) ratio is a highly sensitive biomarker for metabolically active tumor. We provide a preliminary report of our study introducing a novel treatment approach of whole brain sMRI-guided proton therapy for pHGG. An observational cohort (c1 = 10 patients) receives standard of care RT; a therapeutic cohort (c2 = 15 patients) receives sMRI-guided proton RT. All patients undergo cMRI and sMRI, a high-resolution 3D whole-brain echo-planar spectroscopic imaging (EPSI) sequence (interpolated resolution of 12 µL) prior to RT and at several follow-up timepoints integrated into diagnostic scans. Treatment volumes are defined by cMRI for c1 and by cMRI and Cho/NAA ≥ 2x for c2. A longitudinal imaging database is used to quantify changes in lesion and metabolite volumes. Four subjects have been enrolled (c1 = 1/c2 = 3) with sMRI imaging follow-up of 4-18 months. Preliminary data suggest sMRI improves identification of pHGG infiltration based on abnormal metabolic activity, and using proton therapy to target sMRI-defined high-risk regions is safe and feasible.

The value of adding DWI and FLAIR signal changes in the resection cavity on the diagnostic performance of BT-RADS category 3 for tumor progression prediction in post-treated glioma patients: a prospective pilot study

Background

BT-RADS is a structured reporting system of post-treatment glioma. BT-RADS category 3 carries a probability of recurrent malignancy versus treatment-related changes. The aim of this study is to evaluate the additive value of DWI and resection cavity FLAIR signal changes to BT-RADS category 3 in the prediction of tumor progression. We prospectively evaluated follow-up contrast-enhanced MR imaging where 27 post-treated glioma patients were assigned BT-RADS category 3. In all images, FLAIR signal, enhancement component, mass effect, and ADCmean were assessed. We used imaging follow-up from the second stage of the study as the gold standard for comparing the diagnostic performance of BT-RADS category 3 for predicting tumor recurrence before and after the addition of DWI and resection cavity FLAIR signal changes. ROC curves analyses were assessed and compared using the Delong test.

Results

48.1% of patients had tumor recurrence and 51.9% of patients had treatment-related changes. There was significant difference between ADCmean in recurrent and non-recurrent groups measuring 0.9 and 1.15 × 10−3mm2/s, respectively (p value < 0.001). BT-RADS, BT-RADS added DWI, and BT-RADS added DWI and resection cavity FLAIR signal had a specificity of 64.3, 71.4, and 71.4%, sensitivity of 76.9, 84.6, and 92.3%, and accuracy of 70.5, 77.8, and 81.5%, with improved AUC from 0.706 (95% CI of 0.50–0.86) to 0.78 (95% CI of 0.58–0.92) to 0.819 (95% CI of 0.64–0.94), respectively.

Conclusions

Adding DWI and resection cavity FLAIR signal alteration improves the diagnostic performance of BT-RADS category 3.

A Novel Approach to Determining Tumor Progression Using a Three-Site Pilot Clinical Trial of Spectroscopic MRI-Guided Radiation Dose Escalation in Glioblastoma

Glioblastoma (GBM) is a fatal disease, with poor prognosis exacerbated by difficulty in assessing tumor extent with imaging. Spectroscopic MRI (sMRI) is a non-contrast imaging technique measuring endogenous metabolite levels of the brain that can serve as biomarkers for tumor extension. We completed a three-site study to assess survival benefits of GBM patients when treated with escalated radiation dose guided by metabolic abnormalities in sMRI. Escalated radiation led to complex post-treatment imaging, requiring unique approaches to discern tumor progression from radiation-related treatment effect through our quantitative imaging platform. The purpose of this study is to determine true tumor recurrence timepoints for patients in our dose-escalation multisite study using novel methodology and to report on median progression-free survival (PFS). Follow-up imaging for all 30 trial patients were collected, lesion volumes segmented and graphed, and imaging uploaded to our platform for visual interpretation. Eighteen months post-enrollment, the median PFS was 16.6 months with a median time to follow-up of 20.3 months. With this new treatment paradigm, incidence rate of tumor recurrence one year from treatment is 30% compared to 60-70% failure under standard care. Based on the delayed tumor progression and improved survival, a randomized phase II trial is under development (EAF211).

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.

MRT-Befundung hirneigener Tumoren

Hintergrund und Ziel

Eine strukturierte MRT-Befundung unter Verwendung konsensbasierter inhaltlicher Kategorien hat das Potenzial, die interdisziplinäre Kommunikation in der Neuroonkologie zu verbessern. Ziel dieser Studie war es daher, mittels einer bundesweiten Befragung von Mitgliedern medizinischer Fachgesellschaften mit neuroonkologischem Bezug die wesentlichen Befundungskategorien der Bildgebung hirneigener Tumoren aus klinischer Perspektive zu ermitteln.

Material und Methoden

Auf der Basis eines interdisziplinär entwickelten Katalogs von MRT-Befundungselementen wurde ein Online-Fragebogen erstellt. Im Anschluss wurden fachärztliche Mitglieder der Deutschen Gesellschaften für Neurochirurgie, Radioonkologie, Hämatologie und Medizinische Onkologie, Neurologie und Neuropathologie dazu eingeladen, die Items hinsichtlich ihrer klinischen Relevanz zu bewerten.

Ergebnisse

An der Umfrage nahmen insgesamt 171 Fachärzte aus dem Bundesgebiet teil (81 Neurochirurgen, 66 Strahlentherapeuten und 24 andere neuroonkologische Experten). Anzahl und anatomische Ausdehnung der Tumoren in einer kontrastmittelverstärkten T1- und 2‑D-T2-Sequenz (98,8 % vs. 97,1 %) sowie neu diagnostizierte Läsionen bei Folgeuntersuchungen (T1 + Kontrast 98,2 %; T2 94,7 %) wurden am häufigsten als essenziell betrachtet. Darüber hinaus beurteilten die Experten insbesondere die Beschreibung einer ependymalen und/oder leptomeningealen Tumordissemination (93,6 %) sowie Zeichen der Raumforderung inklusive Verschlusshydrozephalus und parenchymale Massenverschiebungen (jeweils > 75,0 %) als wesentlich. Eine standardmäßige Erwähnung von intratumoralen Verkalkungen, Hämorrhagien, Tumorgefäßarchitektur oder erweiterter Bildgebungsmethoden wie MR-Perfusion, Diffusion, Traktographie und Protonenspektroskopie bewertete lediglich eine Minderheit der Umfrageteilnehmer als praxisrelevant.

Schlussfolgerung

Ein zuweiserorientierter inhaltlicher Mindeststandard der magnetresonanztomographischen Hirntumordiagnostik sollte als klinisch relevante Kernelemente die exakte anatomische Ausbreitung der Raumforderung(en) inklusive ependymaler und meningealer Beteiligung sowie die einschlägigen Raumforderungszeichen enthalten.

Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16–54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5–15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.

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.

Optimizing Postoperative Adjuvant Therapy in Elderly Patients with Newly Diagnosed Glioblastoma: Single-Institution Audit of Clinical Outcomes from a Tertiary-Care Comprehensive Cancer Center in India

BACKGROUND

There is lack of consensus regarding optimal adjuvant therapy in elderly glioblastoma (GBM). We have been treating elderly (≥60 years) GBM patients with normofractionated or hypofractionated radiotherapy (RT) plus temozolomide (TMZ) based on Karnofsky performance status (KPS). Herein we report clinical outcomes in this cohort treated at our institute using this approach.

METHODS

Medical records of elderly GBM patients (≥60 years) treated between 2013 and 2017 with either normofractionated RT (59.4-60 Gy/30-33 fractions/6-6.5 weeks) or hypofractionated RT (35 Gy/10 fractions/2 weeks) plus TMZ were reviewed retrospectively. Outcomes of interest included progression-free survival (PFS), overall survival (OS), and ≥grade 3 myelotoxicity. Time-to-event outcomes were analyzed with Kaplan-Meier methods, compared using log-rank test, and reported as point estimates with 95% confidence interval (CI).

RESULTS

The normofractionated cohort (n = 126) was characterized by a higher proportion of patients younger than age 65 years, KPS ≥70, methylated O⁶-methylguanine DNA methyltransferase (MGMT), and receiving adjuvant TMZ including extended adjuvant TMZ (>6 cycles) compared with the hypofractionated cohort (n = 20), confirming selection bias. At a median follow-up of 13 months, 1-year Kaplan-Meier estimates of PFS and OS were 43% (95% CI: 36%-52%) and 56% (95% CI: 48%-64%), yielding median PFS and OS of 11.0 months and 13.1 months, respectively. Higher KPS, methylated MGMT, normofractionated RT, and extended adjuvant TMZ emerged as favorable prognostic factors. TMZ was well tolerated with a low risk of ≥grade 3 myelotoxicity.

CONCLUSIONS

Our single-institution clinical audit confirms poor survival in elderly GBM with suboptimal performance status but demonstrates acceptably fair outcomes in patients with preserved KPS comparable with the nonelderly cohort.

Final Report on Clinical Outcomes and Tumor Recurrence Patterns of a Pilot Study Assessing Efficacy of Belinostat (PXD-101) with Chemoradiation for Newly Diagnosed Glioblastoma

Glioblastoma (GBM) is highly aggressive and has a poor prognosis. Belinostat is a histone deacetylase inhibitor with blood–brain barrier permeability, anti-GBM activity, and the potential to enhance chemoradiation. The purpose of this clinical trial was to assess the efficacy of combining belinostat with standard-of-care therapy. Thirteen patients were enrolled in each of control and belinostat cohorts. The belinostat cohort was given a belinostat regimen (500–750 mg/m2 1×/day × 5 days) every three weeks (weeks 0, 3, and 6 of RT). All patients received temozolomide and radiation therapy (RT). RT margins of 5–10 mm were added to generate clinical tumor volumes and 3 mm added to create planning target volumes. Median overall survival (OS) was 15.8 months for the control cohort and 18.5 months for the belinostat cohort (p = 0.53). The recurrence volumes (rGTVs) for the control cohort occurred in areas that received higher radiation doses than that in the belinostat cohort. For those belinostat patients who experienced out-of-field recurrence, tumors were detectable by spectroscopic MRI before RT. Recurrence analysis suggests better in-field control with belinostat. This study highlights the potential of belinostat as a synergistic therapeutic agent for GBM. It may be particularly beneficial to combine this radio-sensitizing effect with spectroscopic MRI-guided RT.

Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task

For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime.

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.

Neuroimaging in the Era of the Evolving WHO Classification of Brain Tumors, From the AJR Special Series on Cancer Staging

The inclusion of molecular and genetic information with histopathologic information defines the framework for brain tumor classification and grading. This framework is reflected in the major restructuring of the WHO brain tumor classification system in 2016 and in numerous subsequent proposed updates reflecting ongoing developments in understanding the impact of tumor genotype on classification and grading. This incorporation of molecular and genetic features improves tumor diagnosis and prediction of tumor behavior and response to treatment. Neuroimaging is essential for the noninvasive assessment of pretreatment tumor grading and for identification and determination of therapeutic efficacy. Use of conventional neuroimaging and physiologic imaging techniques, such as diffusion- and perfusion-weighted MRI, can increase diagnostic confidence before and after treatment. Although the use of neuroimaging to consistently determine tumor genetics is not yet robust, promising developments are on the horizon. Given the complexity of the brain tumor microenvironment, the development and implementation of a standardized reporting system can aid in conveying to radiologists, referring providers, and patients important information about brain tumor response to treatment. The purpose of this article is to review the current state and role of neuroimaging in this continuously evolving field.

Imaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis

Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.

A Scalable Natural Language Processing for Inferring BT-RADS Categorization from Unstructured Brain Magnetic Resonance Reports

The aim of this study is to develop an automated classification method for Brain Tumor Reporting and Data System (BT-RADS) categories from unstructured and structured brain magnetic resonance imaging (MR) reports. This retrospective study included 1410 BT-RADS structured reports dated from January 2014 to December 2017 and a test set of 109 unstructured brain MR reports dated from January 2010 to December 2014. Text vector representations and semantic word embeddings were generated from individual report sections (i.e., "History," "Findings," etc.) using Tf-idf statistics and a fine-tuned word2vec model, respectively. Section-wise ensemble models were trained using gradient boosting (XGBoost), elastic net regularization, and random forests, and classification accuracy was evaluated on an independent test set of unstructured brain MR reports and a validation set of BT-RADS structured reports. Section-wise ensemble models using XGBoost and word2vec semantic word embeddings were more accurate than those using Tf-idf statistics when classifying unstructured reports, with an f1 score of 0.72. In contrast, models using traditional Tf-idf statistics outperformed the word2vec semantic approach for categorization from structured reports, with an f1 score of 0.98. Proposed natural language processing pipeline is capable of inferring BT-RADS report scores from unstructured reports after training on structured report data. Our study provides a detailed experimentation process and may provide guidance for the development of RADS-focused information extraction (IE) applications from structured and unstructured radiology reports.

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.

Using a Website to Teach a Structured Reporting System, the Brain Tumor Reporting and Data System

BACKGROUND

The Brain Tumor Reporting and Data System (BT-RADS) is a proposed standardized radiology reporting scheme for magnetic resonance imagings in brain tumor patients. A website was created to introduce the classification system and to promote its use during daily radiology readouts with trainees.

OBJECTIVES

To demonstrate how a website can help implement a structured reporting at a tertiary academic facility.

METHODS

A website, www.btrads.com, including visual aids and an interactive scoring tool was developed to educate trainees about a structured reporting system for brain tumor magnetic resonance imagings. Number of website visitors, resource downloads, and scoring tool users was gathered during the study period of May 1, 2018 to April 30, 2019. Authors surveyed a group of 71 radiology trainees and 34 faculty physicians who care for brain tumor patients to assess the perceived educational and clinical value of BT-RADS.

RESULTS

The website was visited by 10,058 unique users in 1 year. The most commonly downloaded support material was the full guide (382 downloads). The interactive scoring tool was used 267 times. The use of BT-RADS at a single institution over 12 months reached over 70%. While survey results from trainees did not reach statistical significance, faculty oncologists, neurosurgeons, and radiologists felt that BT-RADS was a valuable clinical tool that improved interdisciplinary communication, facilitated educational discussions, and helped make treatment decisions.

CONCLUSIONS

A website designed to implement a novel structured radiology report facilitated template acceptance across a large neuroradiology section. Groups seeking to modify reporting practices should consider using a website.

Adding DSC PWI and DWI to BT-RADS can help identify postoperative recurrence in patients with high-grade gliomas

BACKGROUND

The Brain Tumor Reporting and Data System (BT-RADS) category 3 is suitable for identifying cases with intermediate probability of tumor recurrence that do not meet the Response Assessment in Neuro-Oncology (RANO) criteria for progression. The aim of this study was to evaluate the added value of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC PWI) and diffusion-weighted imaging (DWI) to BT-RADS for differentiating tumor recurrence from non-recurrence in postoperative high-grade glioma (HGG) patients with category 3 lesions.

METHODS

Patients with BT-RADS category 3 lesions were included. The maximal relative cerebral blood volume (rCBV_max) and the mean apparent diffusion coefficient (ADC_mean) values were measured. The added value of DSC PWI and DWI to BT-RADS was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

Fifty-one of 91 patients had tumor recurrence, and 40 patients did not. There were significant differences in rCBV_max and ADC_mean between the tumor recurrence group and non-recurrence group. Compared to BT-RADS alone, the addition of DSC PWI to BT-RADS increased the area under curve (AUC) from 0.76 (95% confidence interval [CI] 0.66-0.84) to 0.90 (95% CI 0.81-0.95) for differentiating tumor recurrence from non-recurrence. The addition of DWI to BT-RADS increased the AUC from 0.76 (95% CI 0.66-0.84) to 0.88 (95% CI 0.80-0.94). The combination of BT-RADS, DSC PWI, and DWI exhibited the best diagnostic performance (AUC = 0.95; 95% CI 0.88-0.98) for differentiating tumor recurrence from non-recurrence.

CONCLUSION

Adding DSC PWI and DWI to BT-RADS can significantly improve the diagnostic performance for differentiating tumor recurrence from non-recurrence in BT-RADS category 3 lesions.

Imaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis

Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.

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.

The ASNR-ACR-RSNA Common Data Elements Project: What Will It Do for the House of Neuroradiology?

The American Society of Neuroradiology has teamed up with the American College of Radiology and the Radiological Society of North America to create a catalog of neuroradiology common data elements that addresses specific clinical use cases. Fundamentally, a common data element is a question, concept, measurement, or feature with a set of controlled responses. This could be a measurement, subjective assessment, or ordinal value. Common data elements can be both machine- and human-generated. Rather than redesigning neuroradiology reporting, the goal is to establish the minimum number of "essential" concepts that should be in a report to address a clinical question. As medicine shifts toward value-based service compensation methodologies, there will be an even greater need to benchmark quality care and allow peer-to-peer comparisons in all specialties. Many government programs are now focusing on these measures, the most recent being the Merit-Based Incentive Payment System and the Medicare Access Children's Health Insurance Program Reauthorization Act of 2015. Standardized or structured reporting is advocated as one method of assessing radiology report quality, and common data elements are a means for expressing these concepts. Incorporating common data elements into clinical practice fosters a number of very useful downstream processes including establishing benchmarks for quality-assurance programs, ensuring more accurate billing, improving communication to providers and patients, participating in public health initiatives, creating comparative effectiveness research, and providing classifiers for machine learning. Generalized adoption of the recommended common data elements in clinical practice will provide the means to collect and compare imaging report data from multiple institutions locally, regionally, and even nationally, to establish quality benchmarks.