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

Interrater Agreement of BT-RADS for Evaluation of Follow-up MRI in Patients with Treated Primary Brain Tumor

BACKGROUND AND PURPOSE

The Brain Tumor Reporting and Data System (BT-RADS) is a structured radiology reporting algorithm that was introduced to provide uniformity in posttreatment primary brain tumor follow-up and reporting, but its interrater reliability (IRR) assessment has not been widely studied. Our goal is to evaluate the IRR among neuroradiologists and radiology residents in the use of BT-RADS.

MATERIALS AND METHODS

This retrospective study reviewed 103 consecutive MR studies in 98 adult patients previously diagnosed with and treated for primary brain tumor (January 2019 to February 2019). Six readers with varied experience (4 neuroradiologists and 2 radiology residents) independently evaluated each case and assigned a BT-RADS score. Readers were blinded to the original score reports and the reports from other readers. Cases in which at least 1 neuroradiologist scored differently were subjected to consensus scoring. After the study, a post hoc reference score was also assigned by 2 readers by using future imaging and clinical information previously unavailable to readers. The interrater reliabilities were assessed by using the Gwet AC2 index with ordinal weights and percent agreement.

RESULTS

Of the 98 patients evaluated (median age, 53 years; interquartile range, 41-66 years), 53% were men. The most common tumor type was astrocytoma (77%) of which 56% were grade 4 glioblastoma. Gwet index for interrater reliability among all 6 readers was 0.83 (95% CI: 0.78-0.87). The Gwet index for the neuroradiologists' group (0.84 [95% CI: 0.79-0.89]) was not statistically different from that for the residents' group (0.79 [95% CI: 0.72-0.86]) (χ2 = 0.85; P = .36). All 4 neuroradiologists agreed on the same BT-RADS score in 57 of the 103 studies, 3 neuroradiologists agreed in 21 of the 103 studies, and 2 neuroradiologists agreed in 21 of the 103 studies. Percent agreement between neuroradiologist blinded scores and post hoc reference scores ranged from 41%-52%.

CONCLUSIONS

A very good interrater agreement was found when tumor reports were interpreted by independent blinded readers by using BT-RADS criteria. Further study is needed to determine if this high overall agreement can translate into greater consistency in clinical care.

Impact of Structured Reporting of Lower Extremity CT Angiography on Report Quality and Workflow Efficiency

We assessed the effects of structured reporting (SR) of lower extremity CT angiography (CTA) on report quality and workflow efficiency compared with conventional reports (CR). Surveys were conducted at an academic radiology department before and after the introduction of an SR template. Participants (n = 39, 21) rated report quality and report creation effort (1: very dissatisfied/low to 10: very satisfied/high) and whether SR represents an improvement over CR (1: completely disagree to 5: completely agree). Four residents and two supervising radiologists created both CR and SR of 40 CTA examinations. Report creation time was measured and the factual accuracy of residents' reports was judged. Report completeness (median 8.0 vs. 7.0, p = 0.016) and clinical usefulness (7.0 vs. 4.0, p = 0.029) were rated higher for SR. Supervising radiologists found report clarity improved by SR (8.0 vs. 4.5, p = 0.029). Report creation effort was unchanged (7.0 vs. 6.0, p > 0.05). SR was considered an improvement over CR (median 4.0, IQR,3.0-5.0). Report supervision was shortened by SR (6.2 ± 2.0 min vs. 10.6 ± 3.5 min, p < 0.001) but total time for report creation remained unchanged (36.6 ± 12.8 min vs. 36.4 ± 11.0 min, p > 0.05). Factual accuracy of residents' SR was deemed higher (8.0/9.5 vs. 7.0/7.0, p = 0.006/ < 0.001). In conclusion, SR has the potential to improve report quality and workflow efficiency for lower extremity CTA.

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.

Post-treatment imaging of gliomas: challenging the existing dogmas

Gliomas are the commonest malignant central nervous system tumours in adults and imaging is the cornerstone of diagnosis, treatment, and post-treatment follow-up of these patients. With the ever-evolving treatment strategies post-treatment imaging and interpretation in glioma remains challenging, more so with the advent of anti-angiogenic drugs and immunotherapy, which can significantly alter the appearance in this setting, thus making interpretation of routine imaging findings such as contrast enhancement, oedema, and mass effect difficult to interpret. This review details the various methods of management of glioma including the upcoming novel therapies and their impact on imaging findings, with a comprehensive description of the imaging findings in conventional and advanced imaging techniques. A systematic appraisal for the existing and emerging techniques of imaging in these settings and their clinical application including various response assessment guidelines and artificial intelligence based response assessment will also be discussed.

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.

A Fully Automated Post-Surgical Brain Tumor Segmentation Model for Radiation Treatment Planning and Longitudinal Tracking

Glioblastoma (GBM) has a poor survival rate even with aggressive surgery, concomitant radiation therapy (RT), and adjuvant chemotherapy. Standard-of-care RT involves irradiating a lower dose to the hyperintense lesion in T2-weighted fluid-attenuated inversion recovery MRI (T2w/FLAIR) and a higher dose to the enhancing tumor on contrast-enhanced, T1-weighted MRI (CE-T1w). While there have been several attempts to segment pre-surgical brain tumors, there have been minimal efforts to segment post-surgical tumors, which are complicated by a resection cavity and postoperative blood products, and tools are needed to assist physicians in generating treatment contours and assessing treated patients on follow up. This report is one of the first to train and test multiple deep learning models for the purpose of post-surgical brain tumor segmentation for RT planning and longitudinal tracking. Post-surgical FLAIR and CE-T1w MRIs, as well as their corresponding RT targets (GTV1 and GTV2, respectively) from 225 GBM patients treated with standard RT were trained on multiple deep learning models including: Unet, ResUnet, Swin-Unet, 3D Unet, and Swin-UNETR. These models were tested on an independent dataset of 30 GBM patients with the Dice metric used to evaluate segmentation accuracy. Finally, the best-performing segmentation model was integrated into our longitudinal tracking web application to assign automated structured reporting scores using change in percent cutoffs of lesion volume. The 3D Unet was our best-performing model with mean Dice scores of 0.72 for GTV1 and 0.73 for GTV2 with a standard deviation of 0.17 for both in the test dataset. We have successfully developed a lightweight post-surgical segmentation model for RT planning and longitudinal tracking.

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.

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).

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.

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.

Structured reporting in radiology: a systematic review to explore its potential

OBJECTIVES

Structured reporting (SR) in radiology reporting is suggested to be a promising tool in clinical practice. In order to implement such an emerging innovation, it is necessary to verify that radiology reporting can benefit from SR. Therefore, the purpose of this systematic review is to explore the level of evidence of structured reporting in radiology. Additionally, this review provides an overview on the current status of SR in radiology.

METHODS

A narrative systematic review was conducted, searching PubMed, Embase, and the Cochrane Library using the syntax 'radiol*' AND 'structur*' AND 'report*'. Structured reporting was divided in SR level 1, structured layout (use of templates and checklists), and SR level 2, structured content (a drop-down menu, point-and-click or clickable decision trees). Two reviewers screened the search results and included all quantitative experimental studies that discussed SR in radiology. A thematic analysis was performed to appraise the evidence level.

RESULTS

The search resulted in 63 relevant full text articles out of a total of 8561 articles. Thematic analysis resulted in 44 SR level 1 and 19 level 2 reports. Only one paper was scored as highest level of evidence, which concerned a double cohort study with randomized trial design.

CONCLUSION

The level of evidence for implementing SR in radiology is still low and outcomes should be interpreted with caution.

KEY POINTS

• Structured reporting is increasingly being used in radiology, especially in abdominal and neuroradiological CT and MRI reports. • SR can be subdivided into structured layout (SR level 1) and structured content (SR level 2), in which the first is defined as being a template in which the reporter has to report; the latter is an IT-based manner in which the content of the radiology report can be inserted and displayed into the report. • Despite the extensive amount of research on the subject of structured reporting, the level of evidence is low.

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.

Identifying and understanding the contextual factors that shaped mid-implementation outcomes during the COVID-19 pandemic in organizations implementing mental health recovery innovations into services

BACKGROUND

Seven housing and health services organizations were guided through a process of translating Chapter Six of the Canadian Guidelines for Recovery-Oriented Practice into a recovery-oriented innovation and plan for its implementation. At the time of the COVID-19 outbreak and lockdown measures, six of the seven organizations had begun implementing their chosen innovation (peer workers, wellness recovery action planning facilitator training, staff training and a family support group). This mid-implementation study used the Consolidated Framework for Implementation Research (CFIR) to identify contextual factors that influenced organizations to continue or postpone implementation of recovery-oriented innovations in the early months of the COVID-19 pandemic.

METHODS

Twenty-seven semi-structured 45-min interviews were conducted between May and June 2020 (21 implementation team members and six providers of the innovation (trainers, facilitators, peer workers). Interview guides and analysis were based on the CFIR. Content analysis combined deductive and inductive approaches. Summaries of coded data were given ratings based on strength and valence of the construct's impact on implementation. Ratings were visualized by mid-implementation outcome and recovery innovation to identify constructs which appear to distinguish between sites with a more or less favorable mid-implementation outcomes.

RESULTS

Four mid-implementation outcomes were observed at this snapshot in time (from most to least positive): continued implementation with adaptation (one site), postponement with adaptation and estimated relaunch date (four sites), indefinite postponement with no decision on relaunch date (one site), and no implementation of innovation yet (one site). Two constructs had either a negative influence (external policies and incentives-renamed COVID-19-related external policy for this study) or a positive influence (leadership engagement), regardless of implementation outcome. Four factors appeared to distinguish between more or less positive mid-implementation outcome: adaptability, implementation climate and relative priority, available resources, and formally appointed internal implementation leaders (renamed "engaging implementation teams during the COVID-19 pandemic" for this study).

CONCLUSIONS

The COVID-19 pandemic is an unprecedented outer setting factor. Studies that use the CFIR at the mid-implementation stage are rare, as are studies focusing on the outer setting. Through robust qualitative analysis, we identify the key factors that shaped the course of implementation of recovery innovations over this turbulent time.

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.

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.

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.

Assessment of Explicitly Stated Interval Change on Noncontrast Head CT Radiology Reports

BACKGROUND AND PURPOSE

Consistent and standardized reporting of interval change for certain diagnoses may improve the clinical utility of radiology reports. The purpose of this study was to assess explicitly stated interval change of various findings in noncontrast head CT reports.

MATERIALS AND METHODS

A retrospective review was performed on successive noncontrast head CT radiology reports from the first 2 weeks of January 2014. Reports with at least 1 prior comparison CT scan were included. Reports with normal examination findings and those that made comparison with only other types of examinations (eg, MR imaging) were excluded. Descriptive and subgroup statistical analyses were performed.

RESULTS

In total, 200 patients with 230 reports and 979 radiographic findings were identified. The average interval between reports was 344.9 ± 695.9 days (range, 0-3556 days). Interval change was mentioned 67.3% (n = 659) of the time for all findings (n = 979). Explicitly stated interval change was significantly associated with nonremote findings (P < .001) and generalized statements of interval change (P < .001). The proportion of interval change reported ranged from 95.3% of the time for hemorrhagic to 36.4% for soft-tissue/osseous categorizations.

CONCLUSIONS

Interval change reporting was variable, mentioned for 67.3% of noncontrast head CT report findings with a prior comparison CT scan. Structured radiology reports may improve the consistent and clear reporting of interval change for certain findings.