An adolescent girl with syndrome of inappropriate antidiuretic hormone secretion preceding the diagnosis of olfactory neuroblastoma - a case report.

Objectives: We present an adolescent in whom olfactory neuroblastoma (ONB) was detected on follow-up magnetic resonance imaging (MRI) 2.5 years after SIADH diagnosis. Our case contrasts prior pediatric reports in which ONB and SIADH were diagnosed concurrently. Case presentation: A previously healthy 13-year-old girl was found to have SIADH during evaluation for restrictive eating. Work-up ruled out adrenal, thyroid and paraneoplastic causes, diuretic use, and vasopressin receptor and aquaporin channel mutations. Brain MRI was normal except for paranasal sinus (PNS) inflammatory changes to the left fronto-maxillary sinuses and frontoethmoidal recess. The sodium levels normalized with fluid restriction (800-900 ml/m2/day). Multiple repeated attempts to liberalize fluid intake resulted in recurrent hyponatremia. Follow-up brain MRIs 4 and 11 months after the initial presentation showed persistent PNS inflammatory changes. A subsequent brain MRI 31 months after initial presentation demonstrated a lesion in the left frontoethmoidal recess extending into the left nasal cavity and biopsy showed low grade ONB. The patient underwent surgery with normalization of serum sodium on liberalized fluid intake. Seven days after surgery, she had recurrence of SIADH, and brain MRI showed remnant of the ONB at the fovea ethmoidalis. She completed adjuvant radiotherapy though her SIADH persisted. Conclusions: Our case highlights the importance of considering ONB in the evaluation of children with SIADH. Idiopathic SIADH is rare in children and if no cause is identified, computed tomography of sinuses and nasal endoscopy should be considered earlier in the work-up of these patients, particularly in the absence of sinus symptoms.

"Synthetic" DSC perfusion MRI with adjustable acquisition parameters in brain tumors using dynamic spin-and-gradient-echo echoplanar imaging.

BACKGROUND AND PURPOSE: Normalized relative cerebral blood volume (nrCBV) and percentage of signal recovery (PSR) computed from dynamic susceptibility contrast (DSC) perfusion imaging are useful biomarkers for differential diagnosis and treatment response assessment in brain tumors. However, their measurements are dependent on DSC acquisition factors, and CBV-optimized protocols technically differ from PSR-optimized protocols. This study aimed to generate "synthetic" DSC data with adjustable synthetic acquisition parameters using dual-echo gradient-echo (GE) DSC datasets extracted from dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI). Synthetic DSC was aimed at: 1) simultaneously create nrCBV and PSR maps using optimal sequence parameters, 2) compare DSC datasets with heterogeneous external cohorts, and 3) assess the impact of acquisition factors on DSC metrics. MATERIALS AND METHODS: Thirty-eight patients with contrast-enhancing brain tumors were prospectively imaged with dynamic SAGE-EPI during a non-preloaded single-dose contrast injection and included in this cross-sectional study. Multiple synthetic DSC curves with desired pulse sequence parameters were generated using the Bloch equations applied to the dual-echo GE data extracted from dynamic SAGE-EPI datasets, with or without optional preload simulation. RESULTS: Dynamic SAGE-EPI allowed for simultaneous generation of CBV-optimized and PSR-optimized DSC datasets with a single contrast injection, while PSR computation from guideline-compliant CBV-optimized protocols resulted in rank variations within the cohort (Spearman's ρ=0.83-0.89, i.e. 31%-21% rank variation). Treatment-naïve glioblastoma exhibited lower parameter-matched PSR compared to the external cohorts of treatment-naïve primary CNS lymphomas (PCNSL) (p<0.0001), supporting a role of synthetic DSC for multicenter comparisons. Acquisition factors highly impacted PSR, and nrCBV without leakage correction also showed parameter-dependence, although less pronounced. However, this dependence was remarkably mitigated by post-hoc leakage correction. CONCLUSIONS: Dynamic SAGE-EPI allows for simultaneous generation of CBV-optimized and PSR-optimized DSC data with one acquisition and a single contrast injection, facilitating the use of a single perfusion protocol for all DSC applications. This approach may also be useful for comparisons of perfusion metrics across heterogeneous multicenter datasets, as it facilitates post-hoc harmonization. ABBREVIATIONS: DSC = dynamic susceptibility contrast; FA = flip angle; GBCA = gadolinium-based contrast agent; GBM = glioblastoma; GE = gradient echo; IDH = isocitrate dehydrogenase; IDHm = IDH-mutant; IDHwt = IDH-wild-type; 1p19qcod = 1p19q codeleted; 1p19qint = 1p19q intact; MRI = magnetic resonance imaging; PCNSL = primary CNS lymphoma; PSR = percentage of signal recovery; Rec = recurrent; SAGE-EPI = spin-and-gradient-echo echoplanar imaging; CBV = cerebral blood volume; nrCBV = normalized relative CBV; ROI = region of interest; TE = echo time; TN = treatment-naïve; TR = repetition time.

A Neuroradiologist's Guide to Operationalizing the Response Assessment in Neuro-Oncology (RANO) Criteria Version 2.0 for Gliomas in Adults.

Radiographic assessment plays a crucial role in the management of patients with central nervous system (CNS) tumors, aiding in treatment planning and evaluation of therapeutic efficacy by quantifying response. Recently, an updated version of the Response Assessment in Neuro-Oncology (RANO) criteria (RANO 2.0) was developed to improve upon prior criteria and provide an updated, standardized framework for assessing treatment response in clinical trials for gliomas in adults. This article provides an overview of significant updates to the criteria including (1) the use of a unified set of criteria for high and low grade gliomas in adults; (2) the use of the post-radiotherapy MRI scan as the baseline for evaluation in newly diagnosed high-grade gliomas; (3) the option for the trial to mandate a confirmation scan to more reliably distinguish pseudoprogression from tumor progression; (4) the option of using volumetric tumor measurements; and (5) the removal of subjective non-enhancing tumor evaluations in predominantly enhancing gliomas (except for specific therapeutic modalities). Step-by-step pragmatic guidance is hereby provided for the neuroradiologist and imaging core lab involved in operationalization and technical execution of RANO 2.0 in clinical trials, including the display of representative cases and in-depth discussion of challenging scenarios.

Identification of a Single-Dose, Low-Flip-Angle-Based CBV Threshold for Fractional Tumor Burden Mapping in Recurrent Glioblastoma.

BACKGROUND AND PURPOSE: DSC-MR imaging can be used to generate fractional tumor burden (FTB) maps via application of relative CBV thresholds to spatially differentiate glioblastoma recurrence from posttreatment radiation effects (PTRE). Image-localized histopathology was previously used to validate FTB maps derived from a reference DSC-MR imaging protocol by using preload, a moderate flip angle (MFA, 60°), and postprocessing leakage correction. Recently, a DSC-MR imaging protocol with a low flip angle (LFA, 30°) with no preload was shown to provide leakage-corrected relative CBV (rCBV) equivalent to the reference protocol. This study aimed to identify the rCBV thresholds for the LFA protocol that generate the most accurate FTB maps, concordant with those obtained from the reference MFA protocol. MATERIALS AND METHODS: Fifty-two patients with grade-IV glioblastoma who had prior surgical resection and received chemotherapy and radiation therapy were included in the study. Two sets of DSC-MR imaging data were collected sequentially first by using LFA protocol with no preload, which served as the preload for the subsequent MFA protocol. Standardized relative CBV maps (sRCBV) were obtained for each patient and coregistered with the anatomic postcontrast T1-weighted images. The reference MFA-based FTB maps were computed by using previously published sRCBV thresholds (1.0 and 1.56). A receiver operating characteristics (ROC) analysis was conducted to identify the optimal, voxelwise LFA sRCBV thresholds, and the sensitivity, specificity, and accuracy of the LFA-based FTB maps were computed with respect to the MFA-based reference. RESULTS: The mean sRCBV values of tumors across patients exhibited strong agreement (concordance correlation coefficient = 0.99) between the 2 protocols. Using the ROC analysis, the optimal lower LFA threshold that accurately distinguishes PTRE from tumor recurrence was found to be 1.0 (sensitivity: 87.77%; specificity: 90.22%), equivalent to the ground truth. To identify aggressive tumor regions, the ROC analysis identified an upper LFA threshold of 1.37 (sensitivity: 90.87%; specificity: 91.10%) for the reference MFA threshold of 1.56. CONCLUSIONS: For LFA-based FTB maps, an sRCBV threshold of 1.0 and 1.37 can differentiate PTRE from recurrent tumors. FTB maps aid in surgical planning, guiding pathologic diagnosis and treatment strategies in the recurrent setting. This study further confirms the reliability of single-dose LFA-based DSC-MR imaging.

A cross-sectional study to test equivalence of low- versus intermediate-flip angle dynamic susceptibility contrast MRI measures of relative cerebral blood volume in patients with high-grade gliomas at 1.5 Tesla field strength.

Introduction: 1.5 Tesla (1.5T) remain a significant field strength for brain imaging worldwide. Recent computer simulations and clinical studies at 3T MRI have suggested that dynamic susceptibility contrast (DSC) MRI using a 30° flip angle ("low-FA") with model-based leakage correction and no gadolinium-based contrast agent (GBCA) preload provides equivalent relative cerebral blood volume (rCBV) measurements to the reference-standard acquisition using a single-dose GBCA preload with a 60° flip angle ("intermediate-FA") and model-based leakage correction. However, it remains unclear whether this holds true at 1.5T. The purpose of this study was to test this at 1.5T in human high-grade glioma (HGG) patients. Methods: This was a single-institution cross-sectional study of patients who had undergone 1.5T MRI for HGG. DSC-MRI consisted of gradient-echo echo-planar imaging (GRE-EPI) with a low-FA without preload (30°/P-); this then subsequently served as a preload for the standard intermediate-FA acquisition (60°/P+). Both normalized (nrCBV) and standardized relative cerebral blood volumes (srCBV) were calculated using model-based leakage correction (C+) with IBNeuro™ software. Whole-enhancing lesion mean and median nrCBV and srCBV from the low- and intermediate-FA methods were compared using the Pearson's, Spearman's and intraclass correlation coefficients (ICC). Results: Twenty-three HGG patients composing a total of 31 scans were analyzed. The Pearson and Spearman correlations and ICCs between the 30°/P-/C+ and 60°/P+/C+ acquisitions demonstrated high correlations for both mean and median nrCBV and srCBV. Conclusion: Our study provides preliminary evidence that for HGG patients at 1.5T MRI, a low FA, no preload DSC-MRI acquisition can be an appealing alternative to the reference standard higher FA acquisition that utilizes a preload.

Early post-bevacizumab change in rCBV from DSC-MRI identifies pseudoresponse in recurrent glioblastoma: Results from ACRIN 6677/RTOG 0625.

Background: Progressive enhancement predicted poor survival in ACRIN 6677/RTOG 0625, a multi-center trial of bevacizumab with irinotecan or temozolomide in recurrent glioblastoma, but pseudoresponse likely limited enhancement-based survival prognostication in T1 non-progressors. We aimed to determine whether early change in cerebral blood volume from baseline (ΔCBV) could further stratify the T1 non-progressors according to overall (OS) and progression-free (PFS) survival. Methods: 37/123 enrolled patients had DSC-MRI, including 13, 15, and 8 patients without 2D-T1 progression at 2, 8, and 16 weeks post-treatment initiation, respectively. Mean CBV normalized to white matter (nRCBV) and mean standardized CBV (sRCBV) were extracted from enhancing tumor. ROC curves were derived for ΔCBV using six-month PFS and one-year OS as reference standards. Kaplan-Meier survival estimates and log-rank test compared PFS and OS for both ΔCBV (increase vs. decrease) and T1 response status (stable vs. decreasing enhancement). Results: PFS and OS were significantly worse for increasing CBV at 2 weeks (p=0.003 and p=0.002 for nRCBV, and p=0.03 and p=0.03 for sRCBV, respectively), but not for 2D-T1 patients with stable vs. decreasing enhancement (p=0.44 and p=0.86, respectively). ΔCBV at week 2 was also a good prognostic marker for OS-1 and PFS-6 using ROC analysis. By contrast, 2D-T1 response status at weeks 2, 8, and 16 was not associated with PFS-6. ΔCBV at 16 weeks (p=0.008 for sRCBV) but not 8 weeks (p=0.74 for nRCBV and p=0.56 for sRCBV) was associated with significant difference in median survival, but no difference in survival was observed for 2D-T1 patients with stable vs. decreasing enhancement at 8 weeks (p=0.69) or 16 weeks (p=0.21). At 16 weeks, OS did not differ significantly between 2D-T1 progressors and 2D-T1 non-progressors with increasing CBV (median survival 3.3 months post week 16 scan vs. 9.2 months, respectively; p=0.13), suggesting that 2D-T1 non-progressors with increasing CBV may have a prognosis like that of 2D-T1 progressors. Conclusion: After 2 weeks of anti-angiogenic therapy, ΔCBV in 2D-T1 non-progressors significantly prognosticated PFS and OS, whereas 2D-T1 response status did not, identifying a subpopulation that benefits from bevacizumab. Combining 2D-T1 progression and ΔCBV may yield a response assessment paradigm with 3-tiered OS stratification.

Advanced MRI Protocols to Discriminate Glioma From Treatment Effects: State of the Art and Future Directions.

In the follow-up treatment of high-grade gliomas (HGGs), differentiating true tumor progression from treatment-related effects, such as pseudoprogression and radiation necrosis, presents an ongoing clinical challenge. Conventional MRI with and without intravenous contrast serves as the clinical benchmark for the posttreatment surveillance imaging of HGG. However, many advanced imaging techniques have shown promise in helping better delineate the findings in indeterminate scenarios, as posttreatment effects can often mimic true tumor progression on conventional imaging. These challenges are further confounded by the histologic admixture that can commonly occur between tumor growth and treatment-related effects within the posttreatment bed. This review discusses the current practices in the surveillance imaging of HGG and the role of advanced imaging techniques, including perfusion MRI and metabolic MRI.

Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors.

BACKGROUND: Longitudinal measurement of tumor burden with magnetic resonance imaging (MRI) is an essential component of response assessment in pediatric brain tumors. We developed a fully automated pipeline for the segmentation of tumors in pediatric high-grade gliomas, medulloblastomas, and leptomeningeal seeding tumors. We further developed an algorithm for automatic 2D and volumetric size measurement of tumors. METHODS: The preoperative and postoperative cohorts were randomly split into training and testing sets in a 4:1 ratio. A 3D U-Net neural network was trained to automatically segment the tumor on T1 contrast-enhanced and T2/FLAIR images. The product of the maximum bidimensional diameters according to the RAPNO (Response Assessment in Pediatric Neuro-Oncology) criteria (AutoRAPNO) was determined. Performance was compared to that of 2 expert human raters who performed assessments independently. Volumetric measurements of predicted and expert segmentations were computationally derived and compared. RESULTS: A total of 794 preoperative MRIs from 794 patients and 1003 postoperative MRIs from 122 patients were included. There was excellent agreement of volumes between preoperative and postoperative predicted and manual segmentations, with intraclass correlation coefficients (ICCs) of 0.912 and 0.960 for the 2 preoperative and 0.947 and 0.896 for the 2 postoperative models. There was high agreement between AutoRAPNO scores on predicted segmentations and manually calculated scores based on manual segmentations (Rater 2 ICC = 0.909; Rater 3 ICC = 0.851). Lastly, the performance of AutoRAPNO was superior in repeatability to that of human raters for MRIs with multiple lesions. CONCLUSIONS: Our automated deep learning pipeline demonstrates potential utility for response assessment in pediatric brain tumors. The tool should be further validated in prospective studies.

Consensus recommendations for MRI and PET imaging of primary central nervous system lymphoma: guideline statement from the International Primary CNS Lymphoma Collaborative Group (IPCG).

Advanced molecular and pathophysiologic characterization of primary central nervous system lymphoma (PCNSL) has revealed insights into promising targeted therapeutic approaches. Medical imaging plays a fundamental role in PCNSL diagnosis, staging, and response assessment. Institutional imaging variation and inconsistent clinical trial reporting diminishes the reliability and reproducibility of clinical response assessment. In this context, we aimed to: (1) critically review the use of advanced positron emission tomography (PET) and magnetic resonance imaging (MRI) in the setting of PCNSL; (2) provide results from an international survey of clinical sites describing the current practices for routine and advanced imaging, and (3) provide biologically based recommendations from the International PCNSL Collaborative Group (IPCG) on adaptation of standardized imaging practices. The IPCG provides PET and MRI consensus recommendations built upon previous recommendations for standardized brain tumor imaging protocols (BTIP) in primary and metastatic disease. A biologically integrated approach is provided to addresses the unique challenges associated with the imaging assessment of PCNSL. Detailed imaging parameters facilitate the adoption of these recommendations by researchers and clinicians. To enhance clinical feasibility, we have developed both "ideal" and "minimum standard" protocols at 3T and 1.5T MR systems that will facilitate widespread adoption.

Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach.

Breast cancer remains a global challenge, causing over 600,000 deaths in 2018 (ref. 1). To achieve earlier cancer detection, health organizations worldwide recommend screening mammography, which is estimated to decrease breast cancer mortality by 20-40% (refs. 2,3). Despite the clear value of screening mammography, significant false positive and false negative rates along with non-uniformities in expert reader availability leave opportunities for improving quality and access4,5. To address these limitations, there has been much recent interest in applying deep learning to mammography6-18, and these efforts have highlighted two key difficulties: obtaining large amounts of annotated training data and ensuring generalization across populations, acquisition equipment and modalities. Here we present an annotation-efficient deep learning approach that (1) achieves state-of-the-art performance in mammogram classification, (2) successfully extends to digital breast tomosynthesis (DBT; '3D mammography'), (3) detects cancers in clinically negative prior mammograms of patients with cancer, (4) generalizes well to a population with low screening rates and (5) outperforms five out of five full-time breast-imaging specialists with an average increase in sensitivity of 14%. By creating new 'maximum suspicion projection' (MSP) images from DBT data, our progressively trained, multiple-instance learning approach effectively trains on DBT exams using only breast-level labels while maintaining localization-based interpretability. Altogether, our results demonstrate promise towards software that can improve the accuracy of and access to screening mammography worldwide.

Radiographic read paradigms and the roles of the central imaging laboratory in neuro-oncology clinical trials.

Determination of therapeutic benefit in intracranial tumors is intimately dependent on serial assessment of radiographic images. The Response Assessment in Neuro-Oncology (RANO) criteria were established in 2010 to provide an updated framework to better characterize tumor response to contemporary treatments. Since this initial update a number of RANO criteria have provided some basic principles for the interpretation of changes on MR images; however, the details of how to operationalize RANO and other criteria for use in clinical trials are ambiguous and not standardized. In this review article designed for the neuro-oncologist or treating clinician, we outline essential steps for performing radiographic assessments by highlighting primary features of the Imaging Charter (referred to as the Charter for the remainder of this article), a document that describes the clinical trial imaging methodology and methods to ensure operationalization of the Charter into the workings of a clinical trial. Lastly, we provide recommendations for specific changes to optimize this methodology for neuro-oncology, including image registration, requirement of growing tumor for eligibility in trials of recurrent tumor, standardized image acquisition guidelines, and hybrid reader paradigms that allow for both unbiased measurements and more comprehensive interpretation.

Value of dynamic contrast perfusion MRI to predict early response to bevacizumab in newly diagnosed glioblastoma: results from ACRIN 6686 multicenter trial.

BACKGROUND: In Radiation Therapy Oncology Group (RTOG) 0825, a phase III trial of standard therapy with bevacizumab or without (placebo) in newly diagnosed glioblastoma, 44 patients underwent dynamic contrast enhanced (DCE) and/or dynamic susceptibility contrast (DSC) MRI in the American College of Radiology Imaging Network (ACRIN) trial 6686. The association between early changes in relative cerebral blood volume (rCBV) and volume transfer constant (Ktrans) with overall survival (OS) was evaluated. METHODS: MRI was performed at postop baseline (S0), immediately before (S1), 1 day after (S2), and 7 weeks after (S3) bevacizumab or placebo initiation. Mean normalized and standardized rCBV (nRCBV, sRCBV) and Ktrans were measured within contrast-enhancing lesion. Wilcoxon rank sum tests compared parameter changes from S1-S2 and S1-S3. Association with OS and progression-free survival (PFS) were determined using Kaplan-Meier and log-rank tests. Treatment response for groups stratified by pretreatment nRCBV (S0, S1) was explored. The intraclass correlation coefficient and repeatability coefficient for the placebo arm (S1-S2) were used to assess repeatability. RESULTS: Evaluable were 27-36 datasets per time point. Significant differences between treatment arms were found for changes in nRCBV and sRCBV from S1-S2 and S1-S3, and in Ktrans for S1-S3. Improved PFS (P = 0.05) but not OS (P = 0.46) was observed. High pretreatment rCBV predicted improved OS for bevacizumab-treated patients. Based on the intraclass correlation coefficient, sRCBV (0.92) was more repeatable than nRCBV (0.71) and Ktrans (0.75), consistent with repeatability coefficient values. CONCLUSIONS: Bevacizumab significantly changes rCBV but not Ktrans as early as 1 day posttreatment in newly diagnosed glioblastoma unrelated to outcomes. Improvements in clinical trial design to maximize rCBV benefit are indicated.

Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data.

Purpose: Deep learning (DL) algorithms have shown promising results for brain tumor segmentation in MRI. However, validation is required prior to routine clinical use. We report the first randomized and blinded comparison of DL and trained technician segmentations. Approach: We compiled a multi-institutional database of 741 pretreatment MRI exams. Each contained a postcontrast T1-weighted exam, a T2-weighted fluid-attenuated inversion recovery exam, and at least one technician-derived tumor segmentation. The database included 729 unique patients (470 males and 259 females). Of these exams, 641 were used for training the DL system, and 100 were reserved for testing. We developed a platform to enable qualitative, blinded, controlled assessment of lesion segmentations made by technicians and the DL method. On this platform, 20 neuroradiologists performed 400 side-by-side comparisons of segmentations on 100 test cases. They scored each segmentation between 0 (poor) and 10 (perfect). Agreement between segmentations from technicians and the DL method was also evaluated quantitatively using the Dice coefficient, which produces values between 0 (no overlap) and 1 (perfect overlap). Results: The neuroradiologists gave technician and DL segmentations mean scores of 6.97 and 7.31, respectively ( p < 0.00007 ). The DL method achieved a mean Dice coefficient of 0.87 on the test cases. Conclusions: This was the first objective comparison of automated and human segmentation using a blinded controlled assessment study. Our DL system learned to outperform its "human teachers" and produced output that was better, on average, than its training data.

Evaluating the Use of rCBV as a Tumor Grade and Treatment Response Classifier Across NCI Quantitative Imaging Network Sites: Part II of the DSC-MRI Digital Reference Object (DRO) Challenge.

We have previously characterized the reproducibility of brain tumor relative cerebral blood volume (rCBV) using a dynamic susceptibility contrast magnetic resonance imaging digital reference object across 12 sites using a range of imaging protocols and software platforms. As expected, reproducibility was highest when imaging protocols and software were consistent, but decreased when they were variable. Our goal in this study was to determine the impact of rCBV reproducibility for tumor grade and treatment response classification. We found that varying imaging protocols and software platforms produced a range of optimal thresholds for both tumor grading and treatment response, but the performance of these thresholds was similar. These findings further underscore the importance of standardizing acquisition and analysis protocols across sites and software benchmarking.

Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas.

Despite the widespread clinical use of dynamic susceptibility contrast (DSC) MRI, DSC-MRI methodology has not been standardized, hindering its utilization for response assessment in multicenter trials. Recently, the DSC-MRI Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition issued an updated consensus DSC-MRI protocol compatible with the standardized brain tumor imaging protocol (BTIP) for high-grade gliomas that is increasingly used in the clinical setting and is the default MRI protocol for the National Clinical Trials Network. After reviewing the basis for controversy over DSC-MRI protocols, this paper provides evidence-based best practices for clinical DSC-MRI as determined by the Committee, including pulse sequence (gradient echo vs spin echo), BTIP-compliant contrast agent dosing (preload and bolus), flip angle (FA), echo time (TE), and post-processing leakage correction. In summary, full-dose preload, full-dose bolus dosing using intermediate (60°) FA and field strength-dependent TE (40-50 ms at 1.5 T, 20-35 ms at 3 T) provides overall best accuracy and precision for cerebral blood volume estimates. When single-dose contrast agent usage is desired, no-preload, full-dose bolus dosing using low FA (30°) and field strength-dependent TE provides excellent performance, with reduced contrast agent usage and elimination of potential systematic errors introduced by variations in preload dose and incubation time.

Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement.

BACKGROUND: Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO). METHODS: Two cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment "baseline" MRIs) from 1 institution. RESULTS: The automatically generated FLAIR hyperintensity volume, contrast-enhancing tumor volume, and AutoRANO were highly repeatable for the double-baseline visits, with an intraclass correlation coefficient (ICC) of 0.986, 0.991, and 0.977, respectively, on the cohort of postoperative GBM patients. Furthermore, there was high agreement between manually and automatically measured tumor volumes, with ICC values of 0.915, 0.924, and 0.965 for preoperative FLAIR hyperintensity, postoperative FLAIR hyperintensity, and postoperative contrast-enhancing tumor volumes, respectively. Lastly, the ICCs for comparing manually and automatically derived longitudinal changes in tumor burden were 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively. CONCLUSIONS: Our automated algorithm demonstrates potential utility for evaluating tumor burden in complex posttreatment settings, although further validation in multicenter clinical trials will be needed prior to widespread implementation.

Evaluating Multisite rCBV Consistency from DSC-MRI Imaging Protocols and Postprocessing Software Across the NCI Quantitative Imaging Network Sites Using a Digital Reference Object (DRO).

Relative cerebral blood volume (rCBV) cannot be used as a response metric in clinical trials, in part, because of variations in biomarker consistency and associated interpretation across sites, stemming from differences in image acquisition and postprocessing methods (PMs). This study leveraged a dynamic susceptibility contrast magnetic resonance imaging digital reference object to characterize rCBV consistency across 12 sites participating in the Quantitative Imaging Network (QIN), specifically focusing on differences in site-specific imaging protocols (IPs; n = 17), and PMs (n = 19) and differences due to site-specific IPs and PMs (n = 25). Thus, high agreement across sites occurs when 1 managing center processes rCBV despite slight variations in the IP. This result is most likely supported by current initiatives to standardize IPs. However, marked intersite disagreement was observed when site-specific software was applied for rCBV measurements. This study's results have important implications for comparing rCBV values across sites and trials, where variability in PMs could confound the comparison of therapeutic effectiveness and/or any attempts to establish thresholds for categorical response to therapy. To overcome these challenges and ensure the successful use of rCBV as a clinical trial biomarker, we recommend the establishment of qualifying and validating site- and trial-specific criteria for scanners and acquisition methods (eg, using a validated phantom) and the software tools used for dynamic susceptibility contrast magnetic resonance imaging analysis (eg, using a digital reference object where the ground truth is known).

Interreader Variability of Dynamic Contrast-enhanced MRI of Recurrent Glioblastoma: The Multicenter ACRIN 6677/RTOG 0625 Study.

Purpose To evaluate factors contributing to interreader variation (IRV) in parameters measured at dynamic contrast material-enhanced (DCE) MRI in patients with glioblastoma who were participating in a multicenter trial. Materials and Methods A total of 18 patients (mean age, 57 years ± 13 [standard deviation]; 10 men) who volunteered for the advanced imaging arm of ACRIN 6677, a substudy of the RTOG 0625 clinical trial for recurrent glioblastoma treatment, underwent analyzable DCE MRI at one of four centers. The 78 imaging studies were analyzed centrally to derive the volume transfer constant (Ktrans) for gadolinium between blood plasma and tissue extravascular extracellular space, fractional volume of the extracellular extravascular space (ve), and initial area under the gadolinium concentration curve (IAUGC). Two independently trained teams consisting of a neuroradiologist and a technologist segmented the enhancing tumor on three-dimensional spoiled gradient-recalled acquisition in the steady-state images. Mean and median parameter values in the enhancing tumor were extracted after registering segmentations to parameter maps. The effect of imaging time relative to treatment, map quality, imager magnet and sequence, average tumor volume, and reader variability in tumor volume on IRV was studied by using intraclass correlation coefficients (ICCs) and linear mixed models. Results Mean interreader variations (± standard deviation) (difference as a percentage of the mean) for mean and median IAUGC, mean and median Ktrans, and median ve were 18% ± 24, 17% ± 23, 27% ± 34, 16% ± 27, and 27% ± 34, respectively. ICCs for these metrics ranged from 0.90 to 1.0 for baseline and from 0.48 to 0.76 for posttreatment examinations. Variability in reader-derived tumor volume was significantly related to IRV for all parameters. Conclusion Differences in reader tumor segmentations are a significant source of interreader variation for all dynamic contrast-enhanced MRI parameters. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Wolf in this issue.

Prognostic value of contrast enhancement and FLAIR for survival in newly diagnosed glioblastoma treated with and without bevacizumab: results from ACRIN 6686.

Background: ACRIN 6686/RTOG 0825 was a phase III trial of conventional chemoradiation plus adjuvant temozolomide with bevacizumab or without (placebo) in newly diagnosed glioblastoma. This study investigated whether changes in contrast-enhancing and fluid attenuated inversion recovery (FLAIR)-hyperintense tumor assessed by central reading prognosticate overall survival (OS). Methods: Two hundred eighty-four patients (171 men; median age 57 y, range 19-79; 159 on bevacizumab) had MRI at post-op (baseline) and pre-cycle 4 of adjuvant temozolomide (22 wk post chemoradiation initiation). Four central readers measured bidimensional lesion enhancement (2D-T1) and FLAIR hyperintensity at both time points. Changes from baseline to pre-cycle 4 for both markers were dichotomized (increasing vs non-increasing). Cox proportional hazards model and Kaplan-Meier survival estimates were used for inference. Results: Adjusting for treatment, increasing 2D-T1 (n = 262, hazard ratio [HR] = 2.07, 95% CI: 1.48-2.91, P < 0.0001) and FLAIR (n = 273, HR = 1.75, 95% CI: 1.26-2.41, P = 0.0008) significantly predicted worse OS. Median OS (days) was significantly shorter for patients with increasing versus non-increasing 2D-T1 for both bevacizumab (443 vs 535, P = 0.004) and placebo (526 vs 887, P = 0.001). Median OS was significantly shorter for patients with increasing versus non-increasing FLAIR for placebo (595 vs 872, P = 0.001), and trended similarly for bevacizumab (499 vs 535, P = 0.0935). Adjusting for 2D-T1 and treatment, increasing FLAIR represented significantly higher risk for death (HR = 1.59 [1.11-2.26], P = 0.01). Conclusion: Increased 2D-T1 significantly predicts worse OS in both treatment groups, implying absence of a substantial proportion of pseudoprogression 22 weeks after initiation of standard therapy. FLAIR adds value beyond 2D-T1 in predicting OS, potentially addressing the pseudoresponse effect by substratifying bevacizumab-treated patients with non-increasing 2D-T1.