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.

A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information

Resection and whole brain radiotherapy (WBRT) are standard treatments for brain metastases (BM) but are associated with cognitive side effects. Stereotactic radiosurgery (SRS) uses a targeted approach with less side effects than WBRT. SRS requires precise identification and delineation of BM. While artificial intelligence (AI) algorithms have been developed for this, their clinical adoption is limited due to poor model performance in the clinical setting. The limitations of algorithms are often due to the quality of datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and peritumoral edema 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging information. We used a streamlined approach to database-building through a PACS-integrated segmentation workflow.

Everolimus for Children With Recurrent or Progressive Low-Grade Glioma: Results From the Phase II PNOC001 Trial

PURPOSE

The PNOC001 phase II single-arm trial sought to estimate progression-free survival (PFS) associated with everolimus therapy for progressive/recurrent pediatric low-grade glioma (pLGG) on the basis of phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) pathway activation as measured by phosphorylated-ribosomal protein S6 and to identify prognostic and predictive biomarkers.

PATIENTS AND METHODS

Patients, age 3-21 years, with progressive/recurrent pLGG received everolimus orally, 5 mg/m2 once daily. Frequency of driver gene alterations was compared among independent pLGG cohorts of newly diagnosed and progressive/recurrent patients. PFS at 6 months (primary end point) and median PFS (secondary end point) were estimated for association with everolimus therapy.

RESULTS

Between 2012 and 2019, 65 subjects with progressive/recurrent pLGG (median age, 9.6 years; range, 3.0-19.9; 46% female) were enrolled, with a median follow-up of 57.5 months. The 6-month PFS was 67.4% (95% CI, 60.0 to 80.0) and median PFS was 11.1 months (95% CI, 7.6 to 19.8). Hypertriglyceridemia was the most common grade ≥3 adverse event. PI3K/AKT/mTOR pathway activation did not correlate with clinical outcomes (6-month PFS, active 68.4% v nonactive 63.3%; median PFS, active 11.2 months v nonactive 11.1 months; P = .80). Rare/novel KIAA1549::BRAF fusion breakpoints were most frequent in supratentorial midline pilocytic astrocytomas, in patients with progressive/recurrent disease, and correlated with poor clinical outcomes (median PFS, rare/novel KIAA1549::BRAF fusion breakpoints 6.1 months v common KIAA1549::BRAF fusion breakpoints 16.7 months; P < .05). Multivariate analysis confirmed their independent risk factor status for disease progression in PNOC001 and other, independent cohorts. Additionally, rare pathogenic germline variants in homologous recombination genes were identified in 6.8% of PNOC001 patients.

CONCLUSION

Everolimus is a well-tolerated therapy for progressive/recurrent pLGGs. Rare/novel KIAA1549::BRAF fusion breakpoints may define biomarkers for progressive disease and should be assessed in future clinical trials.

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.

Clinical implications of Peri-hematomal edema microperfusion fraction in intracerebral hemorrhage intravoxel incoherent motion imaging - A pilot study

BACKGROUND AND PURPOSE

Perihematomal edema (PHE) represents the secondary brain injury after intracerebral hemorrhage (ICH). However, neurobiological characteristics of post-ICH parenchymal injury other than PHE volume have not been fully characterized. Using intravoxel incoherent motion imaging (IVIM), we explored the clinical correlates of PHE diffusion and (micro)perfusion metrics in subacute ICH.

MATERIALS AND METHODS

In 41 consecutive patients scanned 1-to-7 days after supratentorial ICH, we determined the mean diffusion (D), pseudo-diffusion (D*), and perfusion fraction (F) within manually segmented PHE. Using univariable and multivariable statistics, we evaluated the relationship of these IVIM metrics with 3-month outcome based on the modified Rankin Scale (mRS).

RESULTS

In our cohort, the average (± standard deviation) age of patients was 68.6±15.6 years, median (interquartile) baseline National Institute of Health Stroke Scale (NIHSS) was 7 (3-13), 11 (27 %) patients had poor outcomes (mRS>3), and 4 (10 %) deceased during the follow-up period. In univariable analyses, admission NIHSS (p < 0.001), ICH volume (p = 0.019), ICH+PHE volume (p = 0.016), and average F of the PHE (p = 0.005) had significant correlation with 3-month mRS. In multivariable model, the admission NIHSS (p = 0.006) and average F perfusion fraction of the PHE (p = 0.003) were predictors of 3-month mRS.

CONCLUSION

The IVIM perfusion fraction (F) maps represent the blood flow within microvasculature. Our pilot study shows that higher PHE microperfusion in subacute ICH is associated with worse outcomes. Once validated in larger cohorts, IVIM metrics may provide insight into neurobiology of post-ICH secondary brain injury and identify at-risk patients who may benefit from neuroprotective therapy.

A Large Open Access Dataset of Brain Metastasis 3D Segmentations with Clinical and Imaging Feature Information

Resection and whole brain radiotherapy (WBRT) are the standards of care for the treatment of patients with brain metastases (BM) but are often associated with cognitive side effects. Stereotactic radiosurgery (SRS) involves a more targeted treatment approach and has been shown to avoid the side effects associated with WBRT. However, SRS requires precise identification and delineation of BM. While many AI algorithms have been developed for this purpose, their clinical adoption has been limited due to poor model performance in the clinical setting. Major reasons for non-generalizable algorithms are the limitations in the datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models to improve generalizability. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and whole tumor (including peritumoral edema) 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging feature information. We used a streamlined approach to database-building leveraging a PACS-integrated segmentation workflow.

Artificial Intelligence for Neuroimaging and Musculoskeletal Radiology: Overview of Current Commercial Algorithms

There is a rapidly increasing number of artificial intelligence (AI) products cleared by the Food and Drug Administration (FDA) for quantification, identification, and even diagnosis in clinical radiology. This review article aims to summarize the landscape of current commercial software products in neuroimaging and musculoskeletal radiology. We will discuss key applications, provide an overview of current FDA cleared products, and summarize relevant peer reviewed publications of these products when available.

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.

Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities

Technological innovation has enabled the development of machine learning (ML) tools that aim to improve the practice of radiologists. In the last decade, ML applications to neuro-oncology have expanded significantly, with the pre-operative prediction of glioma grade using medical imaging as a specific area of interest. We introduce the subject of ML models for glioma grade prediction by remarking upon the models reported in the literature as well as by describing their characteristic developmental workflow and widely used classifier algorithms. The challenges facing these models-including data sources, external validation, and glioma grade classification methods -are highlighted. We also discuss the quality of how these models are reported, explore the present and future of reporting guidelines and risk of bias tools, and provide suggestions for the reporting of prospective works. Finally, this review offers insights into next steps that the field of ML glioma grade prediction can take to facilitate clinical implementation.

Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis

Objectives

To systematically review, assess the reporting quality of, and discuss improvement opportunities for studies describing machine learning (ML) models for glioma grade prediction.

Methods

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA) statement. A systematic search was performed in September 2020, and repeated in January 2021, on four databases: Embase, Medline, CENTRAL, and Web of Science Core Collection. Publications were screened in Covidence, and reporting quality was measured against the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Descriptive statistics were calculated using GraphPad Prism 9.

Results

The search identified 11,727 candidate articles with 1,135 articles undergoing full text review and 85 included in analysis. 67 (79%) articles were published between 2018-2021. The mean prediction accuracy of the best performing model in each study was 0.89 ± 0.09. The most common algorithm for conventional machine learning studies was Support Vector Machine (mean accuracy: 0.90 ± 0.07) and for deep learning studies was Convolutional Neural Network (mean accuracy: 0.91 ± 0.10). Only one study used both a large training dataset (n>200) and external validation (accuracy: 0.72) for their model. The mean adherence rate to TRIPOD was 44.5% ± 11.1%, with poor reporting adherence for model performance (0%), abstracts (0%), and titles (0%).

Conclusions

The application of ML to glioma grade prediction has grown substantially, with ML model studies reporting high predictive accuracies but lacking essential metrics and characteristics for assessing model performance. Several domains, including generalizability and reproducibility, warrant further attention to enable translation into clinical practice.

Systematic Review Registration

PROSPERO, identifier CRD42020209938.

Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment

BACKGROUND

Differentiating gliomas and primary CNS lymphoma represents a diagnostic challenge with important therapeutic ramifications. Biopsy is the preferred method of diagnosis, while MR imaging in conjunction with machine learning has shown promising results in differentiating these tumors.

PURPOSE

Our aim was to evaluate the quality of reporting and risk of bias, assess data bases with which the machine learning classification algorithms were developed, the algorithms themselves, and their performance.

DATA SOURCES

Ovid EMBASE, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and the Web of Science Core Collection were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

STUDY SELECTION

From 11,727 studies, 23 peer-reviewed studies used machine learning to differentiate primary CNS lymphoma from gliomas in 2276 patients.

DATA ANALYSIS

Characteristics of data sets and machine learning algorithms were extracted. A meta-analysis on a subset of studies was performed. Reporting quality and risk of bias were assessed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and Prediction Model Study Risk Of Bias Assessment Tool.

DATA SYNTHESIS

The highest area under the receiver operating characteristic curve (0.961) and accuracy (91.2%) in external validation were achieved by logistic regression and support vector machines models using conventional radiomic features. Meta-analysis of machine learning classifiers using these features yielded a mean area under the receiver operating characteristic curve of 0.944 (95% CI, 0.898-0.99). The median TRIPOD score was 51.7%. The risk of bias was high for 16 studies.

LIMITATIONS

Exclusion of abstracts decreased the sensitivity in evaluating all published studies. Meta-analysis had high heterogeneity.

CONCLUSIONS

Machine learning-based methods of differentiating primary CNS lymphoma from gliomas have shown great potential, but most studies lack large, balanced data sets and external validation. Assessment of the studies identified multiple deficiencies in reporting quality and risk of bias. These factors reduce the generalizability and reproducibility of the findings.

Real-time PACS-integrated longitudinal brain metastasis tracking tool provides comprehensive assessment of treatment response to radiosurgery

Background

Treatment of brain metastases can be tailored to individual lesions with treatments such as stereotactic radiosurgery. Accurate surveillance of lesions is a prerequisite but challenging in patients with multiple lesions and prior imaging studies, in a process that is laborious and time consuming. We aimed to longitudinally track several lesions using a PACS-integrated lesion tracking tool (LTT) to evaluate the efficiency of a PACS-integrated lesion tracking workflow, and characterize the prevalence of heterogenous response (HeR) to treatment after Gamma Knife (GK).

Methods

We selected a group of brain metastases patients treated with GK at our institution. We used a PACS-integrated LTT to track the treatment response of each lesion after first GK intervention to maximally seven diagnostic follow-up scans. We evaluated the efficiency of this tool by comparing the number of clicks necessary to complete this task with and without the tool and examined the prevalence of HeR in treatment.

Results

A cohort of eighty patients was selected and 494 lesions were measured and tracked longitudinally for a mean follow-up time of 374 days after first GK. Use of LTT significantly decreased number of necessary clicks. 81.7% of patients had HeR to treatment at the end of follow-up. The prevalence increased with increasing number of lesions.

Conclusions

Lesions in a single patient often differ in their response to treatment, highlighting the importance of individual lesion size assessments for further treatment planning. PACS-integrated lesion tracking enables efficient lesion surveillance workflow and specific and objective result reports to treating clinicians.

Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries

Background

While there are innumerable machine learning (ML) research algorithms used for segmentation of gliomas, there is yet to be a US FDA cleared product. The aim of this study is to explore the systemic limitations of research algorithms that have prevented translation from concept to product by a review of the current research literature.

Methods

We performed a systematic literature review on 4 databases. Of 11 727 articles, 58 articles met the inclusion criteria and were used for data extraction and screening using TRIPOD.

Results

We found that while many articles were published on ML-based glioma segmentation and report high accuracy results, there were substantial limitations in the methods and results portions of the papers that result in difficulty reproducing the methods and translation into clinical practice.

Conclusions

In addition, we identified that more than a third of the articles used the same publicly available BRaTS and TCIA datasets and are responsible for the majority of patient data on which ML algorithms were trained, which leads to limited generalizability and potential for overfitting and bias.

Topographic correlates of driver mutations and endogenous gene expression in pediatric diffuse midline gliomas and hemispheric high-grade gliomas

We evaluate the topographic distribution of diffuse midline gliomas and hemispheric high-grade gliomas in children with respect to their normal gene expression patterns and pathologic driver mutation patterns. We identified 19 pediatric patients with diffuse midline or high-grade glioma with preoperative MRI from tumor board review. 7 of these had 500 gene panel mutation testing, 11 patients had 50 gene panel mutation testing and one 343 gene panel testing from a separate institution were included as validation set. Tumor imaging features and gene expression patterns were analyzed using Allen Brain Atlas. Twelve patients had diffuse midline gliomas and seven had hemispheric high-grade gliomas. Three diffuse midline gliomas had the K27M mutation in the tail of histone H3 protein. All patients undergoing 500 gene panel testing had additional mutations, the most common being in ACVR1, PPM1D, and p53. Hemispheric high-grade gliomas had either TP53 or IDH1 mutation and diffuse midline gliomas had H3 K27M-mutation. Gene expression analysis in normal brains demonstrated that genes mutated in diffuse midline gliomas had higher expression along midline structures as compared to the cerebral hemispheres. Our study suggests that topographic location of pediatric diffuse midline gliomas and hemispheric high-grade gliomas correlates with driver mutations of tumor to the endogenous gene expression in that location. This correlation suggests that cellular state that is required for increased gene expression predisposes that location to mutations and defines the driver mutations within tumors that arise from that region.

124I-MIBG PET/CT to Monitor Metastatic Disease in Children with Relapsed Neuroblastoma

The metaiodobenzylguanidine (MIBG) scan is one of the most sensitive noninvasive lesion detection modalities for neuroblastoma. Unlike ¹²³I-MIBG, ¹²⁴I-MIBG allows high-resolution PET. We evaluated ¹²⁴I-MIBG PET/CT for its diagnostic performance as directly compared with paired ¹²³I-MIBG scans. Methods: Before ¹³¹I-MIBG therapy, standard ¹²³I-MIBG imaging (5.2 MBq/kg) was performed on 7 patients, including whole-body (anterior-posterior) planar imaging, focused-field-of-view SPECT/CT, and whole-body ¹²⁴I-MIBG PET/CT (1.05 MBq/kg). After therapy, 2 of 7 patients also completed ¹²⁴I-MIBG PET/CT as well as paired ¹²³I-MIBG planar imaging and SPECT/CT. One patient underwent ¹²⁴I-MIBG PET/CT only after therapy. We evaluated all 8 patients who showed at least 1 ¹²³I-MIBG-positive lesion with a total of 10 scans. In 8 pairs, ¹²³I-MIBG and ¹²⁴I-MIBG were performed within 1 mo of each other. The locations of identified lesions, the number of total lesions, and the curie scores were recorded for the ¹²³I-MIBG and ¹²⁴I-MIBG scans. Finally, for 5 patients who completed at least 3 PET/CT scans after administration of ¹²⁴I-MIBG, we estimated the effective dose of ¹²⁴I-MIBG. Results: ¹²³I-MIBG whole-body planar scans, focused-field-of-view SPECT/CT scans, and whole-body ¹²⁴I-MIBG PET scans found 25, 32, and 87 total lesions, respectively. There was a statistically significant difference in lesion detection for ¹²⁴I-MIBG PET/CT versus ¹²³I-MIBG planar imaging (P < 0.0001) and ¹²³I-MIBG SPECT/CT (P < 0.0001). The curie scores were also higher for ¹²⁴I-MIBG PET/CT than for ¹²³I-MIBG planar imaging and SPECT/CT in 6 of 10 patients. ¹²⁴I-MIBG PET/CT demonstrated better detection of lesions throughout the body, including the chest, spine, head and neck, and extremities. The effective dose estimated for patient-specific ¹²⁴I-MIBG was approximately 10 times that of ¹²³I-MIBG; however, given that we administered a very low activity of ¹²⁴I-MIBG (1.05 MBq/kg), the effective dose was only approximately twice that of ¹²³I-MIBG despite the large difference in half-lives (100 vs. 13.2 h). Conclusion: The first-in-humans use of low-dose ¹²⁴I-MIBG PET for monitoring disease burden demonstrated tumor detection capability superior to that of ¹²³I-MIBG planar imaging and SPECT/CT.

Diffusion Characteristics of Pediatric Diffuse Midline Gliomas with Histone H3-K27M Mutation Using Apparent Diffusion Coefficient Histogram Analysis

BACKGROUND AND PURPOSE

Diffuse midline gliomas with histone H3 K27M mutation are biologically aggressive tumors with poor prognosis defined as a new diagnostic entity in the 2016 World Health Organization Classification of Tumors of the Central Nervous System. There are no qualitative imaging differences (enhancement, border, or central necrosis) between histone H3 wildtype and H3 K27M-mutant diffuse midline gliomas. Herein, we evaluated the utility of diffusion-weighted imaging to distinguish H3 K27M-mutant from histone H3 wildtype diffuse midline gliomas.

MATERIALS AND METHODS

We identified 31 pediatric patients (younger than 21 years of age) with diffuse gliomas centered in midline structures that had undergone assessment for histone H3 K27M mutation. We measured ADC within these tumors using a voxel-based 3D whole-tumor measurement method.

RESULTS

Our cohort included 18 infratentorial and 13 supratentorial diffuse gliomas centered in midline structures. Twenty-three (74%) tumors carried H3-K27M mutations. There was no difference in ADC histogram parameters (mean, median, minimum, maximum, percentiles) between mutant and wild-type tumors. Subgroup analysis based on tumor location also did not identify a difference in histogram descriptive statistics. Patients who survived <1 year after diagnosis had lower median ADC (1.10 × 10^-3mm²/s; 95% CI, 0.90-1.30) compared with patients who survived >1 year (1.46 × 10^-3mm²/s; 95% CI, 1.19-1.67; P < .06). Average ADC values for diffuse midline gliomas were 1.28 × 10^-3mm²/s (95% CI, 1.21-1.34) and 0.86 × 10^-3mm²/s (95% CI, 0.69-1.01) for hemispheric glioblastomas with P < .05.

CONCLUSIONS

Although no statistically significant difference in diffusion characteristics was found between H3-K27M mutant and H3 wildtype diffuse midline gliomas, lower diffusivity corresponds to a lower survival rate at 1 year after diagnosis. These findings can have an impact on the anticipated clinical course for this patient population and offer providers and families guidance on clinical outcomes.

3D Printed Absorber for Capturing Chemotherapy Drugs before They Spread through the Body

Despite efforts to develop increasingly targeted and personalized cancer therapeutics, dosing of drugs in cancer chemotherapy is limited by systemic toxic side effects. We have designed, built, and deployed porous absorbers for capturing chemotherapy drugs from the bloodstream after these drugs have had their effect on a tumor, but before they are released into the body where they can cause hazardous side effects. The support structure of the absorbers was built using 3D printing technology. This structure was coated with a nanostructured block copolymer with outer blocks that anchor the polymer chains to the 3D printed support structure and a middle block that has an affinity for the drug. The middle block is polystyrenesulfonate which binds to doxorubicin, a widely used and effective chemotherapy drug with significant toxic side effects. The absorbers are designed for deployment during chemotherapy using minimally invasive image-guided endovascular surgical procedures. We show that the introduction of the absorbers into the blood of swine models enables the capture of 64 ± 6% of the administered drug (doxorubicin) without any immediate adverse effects. Problems related to blood clots, vein wall dissection, and other biocompatibility issues were not observed. This development represents a significant step forward in minimizing toxic side effects of chemotherapy.

A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain

Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (18F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers. Materials and Methods Prospective 18F-FDG PET brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of InceptionV3 architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding. Results The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P < .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain. Conclusion By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Larvie in this issue.

Clinically Relevant and Minimally Invasive Tumor Surveillance of Pediatric Diffuse Midline Gliomas Using Patient-Derived Liquid Biopsy

PURPOSE

Pediatric diffuse midline glioma (DMG) are highly malignant tumors with poor clinical outcomes. Over 70% of patients with DMG harbor the histone 3 p.K27M (H3K27M) mutation, which correlates with a poorer clinical outcome, and is also used as a criterion for enrollment in clinical trials. Because complete surgical resection of DMG is not an option, biopsy at presentation is feasible, but rebiopsy at time of progression is rare. While imaging and clinical-based disease monitoring is the standard of care, molecular-based longitudinal characterization of these tumors is almost nonexistent. To overcome these hurdles, we examined whether liquid biopsy allows measurement of disease response to precision therapy.

EXPERIMENTAL DESIGN

We established a sensitive and specific methodology that detects major driver mutations associated with pediatric DMGs using droplet digital PCR (n = 48 subjects, n = 110 specimens). Quantification of circulating tumor DNA (ctDNA) for H3K27M was used for longitudinal assessment of disease response compared with centrally reviewed MRI data.

RESULTS

H3K27M was identified in cerebrospinal fluid (CSF) and plasma in 88% of patients with DMG, with CSF being the most enriched for ctDNA. We demonstrated the feasibility of multiplexing for detection of H3K27M, and additional driver mutations in patient's tumor and matched CSF, maximizing the utility of a single source of liquid biome. A significant decrease in H3K27M plasma ctDNA agreed with MRI assessment of tumor response to radiotherapy in 83% (10/12) of patients.

CONCLUSIONS

Our liquid biopsy approach provides a molecularly based tool for tumor characterization, and is the first to indicate clinical utility of ctDNA for longitudinal surveillance of DMGs.

Early detection of recurrent medulloblastoma: the critical role of diffusion-weighted imaging

Background

Imaging diagnosis of medulloblastoma recurrence relies heavily on identifying new contrast-enhancing lesions on surveillance imaging, with diffusion-weighted imaging (DWI) being used primarily for detection of complications. We propose that DWI is more sensitive in detecting distal and leptomeningeal recurrent medulloblastoma than T1-weighted postgadolinium imaging.

Methods

We identified 53 pediatric patients with medulloblastoma, 21 of whom developed definitive disease recurrence within the brain. MRI at diagnosis of recurrence and 6 months prior was evaluated for new lesions with reduced diffusion on DWI, contrast enhancement, size, and recurrence location.

Results

All recurrent medulloblastoma lesions demonstrated reduced diffusion. Apparent diffusion coefficient (ADC) measurements were statistically significantly lower (P = .00001) in recurrent lesions (mean=0.658, SD=0.072) as compared to contralateral normal region of interest (mean=0.923, SD=0.146). Sixteen patients (76.2%) with disease recurrence demonstrated contrast enhancement within the recurrent lesions. All 5 patients with nonenhancing recurrence demonstrated reduced diffusion, with a mean ADC of 0.695 ± 0.101 (normal=0.893 ± 0.100, P = .0027). While group 3 and group 4 molecular subtypes demonstrated distal recurrence more frequently, nonenhancing metastatic disease was found in all molecular subtypes.

Conclusion

Recurrent medulloblastoma lesions do not uniformly demonstrate contrast enhancement on MRI, but all demonstrate reduced diffusion. Our findings support that DWI is more sensitive than contrast enhancement for detection of medulloblastoma recurrence, particularly in cases of leptomeningeal nonenhancing disease and distal nonenhancing focal disease. As such, recurrent medulloblastoma can present as a reduced diffusion lesion in a patient with normal postgadolinium contrast MRI.

In vitro clearance of doxorubicin with a DNA-based filtration device designed for intravascular use with intra-arterial chemotherapy

To report a novel method using immobilized DNA within mesh to sequester drugs that have intrinsic DNA binding characteristics directly from flowing blood. DNA binding experiments were carried out in vitro with doxorubicin in saline (PBS solution), porcine serum, and porcine blood. Genomic DNA was used to identify the concentration of DNA that shows optimum binding clearance of doxorubicin from solution. Doxorubicin binding kinetics by DNA enclosed within porous mesh bags was evaluated. Flow model simulating blood flow in the inferior vena cava was used to determine in vitro binding kinetics between doxorubicin and DNA. The kinetics of doxorubicin binding to free DNA is dose-dependent and rapid, with 82-96 % decrease in drug concentration from physiologic solutions within 1 min of reaction time. DNA demonstrates faster binding kinetics by doxorubicin as compared to polystyrene resins that use an ion exchange mechanism. DNA contained within mesh yields an approximately 70 % decrease in doxorubicin concentration from solution within 5 min. In the IVC flow model, there is a 70 % drop in doxorubicin concentration at 60 min. A DNA-containing ChemoFilter device can rapidly clear clinical doses of doxorubicin from a flow model in simple and complex physiological solutions, thereby suggesting a novel approach to reduce the toxicity of DNA-binding drugs.

Imaging Characteristics of Pediatric Diffuse Midline Gliomas with Histone H3 K27M Mutation

BACKGROUND AND PURPOSE

The 2016 World Health Organization Classification of Tumors of the Central Nervous System includes "diffuse midline glioma with histone H3 K27M mutation" as a new diagnostic entity. We describe the MR imaging characteristics of this new tumor entity in pediatric patients.

MATERIALS AND METHODS

We retrospectively reviewed imaging features of pediatric patients with midline gliomas with or without the histone H3 K27 mutation. We evaluated the imaging features of these tumors on the basis of location, enhancement pattern, and necrosis.

RESULTS

Among 33 patients with diffuse midline gliomas, histone H3 K27M mutation was present in 24 patients (72.7%) and absent in 9 (27.3%). Of the tumors, 27.3% (n = 9) were located in the thalamus; 42.4% (n = 14), in the pons; 15% (n = 5), within the vermis/fourth ventricle; and 6% (n = 2), in the spinal cord. The radiographic features of diffuse midline gliomas with histone H3 K27M mutation were highly variable, ranging from expansile masses without enhancement or necrosis with large areas of surrounding infiltrative growth to peripherally enhancing masses with central necrosis with significant mass effect but little surrounding T2/FLAIR hyperintensity. When we compared diffuse midline gliomas on the basis of the presence or absence of histone H3 K27M mutation, there was no significant correlation between enhancement or border characteristics, infiltrative appearance, or presence of edema.

CONCLUSIONS

We describe, for the first time, the MR imaging features of pediatric diffuse midline gliomas with histone H3 K27M mutation. Similar to the heterogeneous histologic features among these tumors, they also have a diverse imaging appearance without distinguishing features from histone H3 wildtype diffuse gliomas.

SISCOM in children with tuberous sclerosis complex-related epilepsy

Identification of a single epileptogenic focus in patients with tuberous sclerosis complex is a challenge. Noninvasive imaging modalities, including subtraction ictal single-photon emission computed tomography coregistered to magnetic resonance imaging (SISCOM), have been used to determine the dominant epileptogenic focus for surgical resection. We assessed whether complete resection of SISCOM hyperperfusion abnormality correlates with seizure-free outcome in 6 children with tuberous sclerosis complex-related epilepsy. The median seizure onset age was 4 months (range 1 day to 16 months). The age at surgery ranged from 8 months to 13 years. A dominant SISCOM hyperperfusion focus was identified in 5 patients with multiple tubers. SISCOM provided additional localizing information for epilepsy surgery in 3 patients with nonlocalizing or discordant electrophysiologic and neuroimaging findings. At a minimum of 2 years' follow-up, 3 patients were free of seizures overall. Freedom from seizures was associated with complete resection of SISCOM abnormality in 2 patients. These findings demonstrate that SISCOM can be useful in identifying the epileptogenic zone and in guiding the location and extent of epilepsy surgery in children with tuberous sclerosis complex and multifocal abnormalities. In children with tuberous sclerosis complex and intractable epilepsy, complete resection of the SISCOM hyperperfusion abnormality is associated with freedom from seizures.

The putative role of the venous system in the genesis of vascular malformations

Recent clinical and experimental evidence has challenged the traditional concept of the venous system as a “passive” element in the genesis and evolution of intracranial vascular malformations. The authors review the clinical and experimental evidence linking the venous system and its anomalies to the genesis of various intracranial vascular malformations, including dural arteriovenous fistulas, cavernous malformations, parenchymal arteriovenous malformations, and capillary telangiectasia. They also describe the potential significance of different associations of these vascular anomalies.

Mesial temporal sclerosis after posterior reversible encephalopathy syndrome

Neither intrathecal methotrexate nor posterior reversible encephalopathy syndrome has previously been reported to result in mesial temporal sclerosis. Described here is the case of a boy with no risk factors for mesial temporal sclerosis who presented with posterior reversible encephalopathy syndrome and partial complex seizures 8 days after initiation of intrathecal methotrexate for treatment of Burkitt lymphoma, and who ultimately progressed to intractable temporal lobe epilepsy due to left mesial temporal sclerosis.