Patterns of brain metastases response to immunotherapy with pembrolizumab.

PURPOSE: Central nervous system (CNS) metastases from lung cancers and melanoma, significantly contribute to morbidity and mortality. Despite advances in local therapies, there is a need for effective systemic treatments. Pembrolizumab, a PD-1 inhibitor, has shown promise for some patients with untreated brain metastases from melanoma and non-small cell lung cancer (NSCLC). This study aims to analyze the response of brain metastasis to pembrolizumab and associate characteristics like size and location with treatment outcome. METHODS: This retrospective study used imaging data from a phase II trial of pembrolizumab in melanoma or NSCLC patients with untreated brain metastases. MRI evaluations were conducted at 2 month intervals, with each brain metastasis treated as a distinct tumor for response assessment, based on modified RECIST criteria (maximum 5 lesions, 5 mm target lesions). RESULTS: Of 130 individual target metastases (> 5 mm), in 65 patients with NSCLC (90 metastases) and Melanoma (40 metastases), 32 (24.6%) demonstrated complete resolution, 24 (18.5%) had partial resolution, 32 (24.6%) were SD and 42 (32.3%) demonstrated PD. Those smaller than 10 mm were more likely to show complete resolution (p = 0.0218), while those ≥ 10 mm were more likely to have PR. There was no significant association between size, number or location (supratentorial vs. infratentorial) and lesion progression. The median time to metastatic lesion progression in the brain was 5.7-7 weeks. CONCLUSION: Pembrolizumab is effective in brain metastases from NSCLC and melanoma, showing response (CR + PR) in 43% and progression (PD) in 32% of metastases. With the median time to CNS progression of 5.7-7 weeks, careful radiographic monitoring is essential to guide timely local treatment decisions.

Comparison of whole-body DW-MRI with 2-[18F]FDG PET for staging and treatment monitoring of children with Langerhans cell histiocytosis.

PURPOSE: To assess and compare the diagnostic accuracy of whole-body (WB) DW-MRI with 2-[18F]FDG PET for staging and treatment monitoring of children with Langerhans cell histiocytosis (LCH). METHODS: Twenty-three children with LCH underwent 2-[18F]FDG PET and WB DW-MRI at baseline. Two nuclear medicine physicians and two radiologists independently assessed presence/absence of tumors in 8 anatomical areas. Sixteen children also performed 2-[18F]FDG PET and WB DW-MRI at follow-up. One radiologist and one nuclear medicine physician revised follow-up scans and collected changes in tumor apparent diffusion (ADC) and standardized uptake values (SUV) before and after therapy in all detectable lesions. 2-[18F]FDG PET results were considered the standard of reference for tumor detection and evaluation of treatment response according to Lugano criteria. Sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy of WB DW-MRI at baseline were calculated, and the 95% confidence intervals were estimated by using the Clopper-Pearson (exact) method; changes in tumor SUVs and ADC were compared using a Mann-Whitney U test. Agreement between reviewers was assessed with a Cohen's weighted kappa coefficient. Analyses were conducted using SAS software version 9.4. RESULTS: Agreement between reviewers was perfect (kappa coefficient = 1) for all analyzed regions but spine and neck (kappa coefficient = 0.89 and 0.83, respectively) for 2-[18F]FDG PET images, and abdomen and pelvis (kappa coefficient = 0.65 and 0.88, respectively) for WB DW-MRI. Sensitivity and specificity were 95.5% and 100% for WB DW-MRI compared to 2-[18F]FDG PET. Pre to post-treatment changes in SUVratio and ADCmean were inversely correlated for all lesions (r: -0.27, p = 0·06) and significantly different between responders and non-responders to chemotherapy (p = 0.0006 and p = 0·003 for SUVratio and ADCmean, respectively). CONCLUSION: Our study showed that WB DW-MRI has similar accuracy to 2-[18F]FDG PET for staging and treatment monitoring of LCH in children. While 2-[18F]FDG PET remains an approved radiological examination for assessing metabolically active disease, WB DW-MRI could be considered as an alternative approach without radiation exposure. The combination of both modalities might have advantages over either approach alone.

Spectrum of qualitative and quantitative imaging of pilomyxoid, intermediate pilomyxoid and pilocytic astrocytomas in relation to their genetic alterations.

PURPOSE: Pilomyxoid astrocytomas (PMA) are pediatric brain tumors predominantly located in the suprasellar region, third ventricle and posterior fossa, which are considered to be more clinically aggressive than pilocytic astrocytomas (PA). Another entity, intermediate pilomyxoid tumors (IPT), exists within the spectrum of pilocytic/pilomyxoid astrocytomas. The 2021 WHO CNS classification refrained from assigning grade 1 or 2 status to PMA, thereby reflecting the need to further elucidate their clinical and imaging characteristics. METHODS: We included a total of 15 patients with PMA, IPT and suprasellar PA. We retrospectively evaluated immunohistochemistry, imaging findings and diffusion characteristics within these tumors as well as whole exome sequencing for three of the cases. RESULTS: 87% of the tumors were supratentorial with 11 cases suprasellar in location, 1 case located in the frontal white matter and 1 in the hippocampus. 6 cases demonstrated intraventricular extension. ADC values were higher in PMA and IPT than PA. 3 cases demonstrated KIAA1549-BRAF-fusion, 2 had BRAF[Formula: see text]-mutation and 6 were BRAF-wildtype. All cases had recurrence/progression on follow-up. CONCLUSION: PMA and IPT do not demonstrate aggressive imaging characteristics in respect to their diffusion imaging with ADC values being higher than PA. Lack of BRAF-alteration in PMA corresponded to atypical location of tumors with atypical driver mutations and mechanisms.

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.

Incidence and characteristics of metastatic intracranial lesions in stage III and IV melanoma: a single institute retrospective analysis.

INTRODUCTION: The study aimed to describe the brain metastases (BM) incidence, at diagnosis and follow-up, in patients initially presenting with stage III or IV melanoma and characterize their metastatic brain lesions. We also sought to describe the association of common genetic mutations and immunotherapy with BM development in advanced melanoma. METHODS: Using our institution's tumor registry, we identified patients with initial diagnoses of stage III and stage IV melanoma. In this cohort, we obtained BM incidence at diagnosis and follow-up, characterized the metastatic brain lesions and primary tumor's genetic profile. RESULTS: During the follow-up period, 22.9% of patients with an initial diagnosis of stage III developed BM. In this cohort, the median time for BM occurrence was 20 months; [95% CI (14-29)]. Likewise, 37.7% of patients with Stage IV melanoma presented with BM at the time of diagnosis, and 22.7% of remaining patients developed BM at follow-up over a median duration of 6 months [95% CI (4-11)]. Therefore, suggesting an overall incidence of 51.9% in stage IV melanoma. Next, we observed that the incidence of BM development during the follow-up period significantly decreased from 2012 to 2017 (p < 0.001). Lastly, we found a significantly higher frequency of mutational BRAF in the primary tumor of patients with BM (68.7% vs. 31.2%; p = 0.02). CONCLUSIONS: While the overall incidence of BM remains high, the decreasing incidence of BM over the follow-up period is promising. Similar BM incidence in patients with an initial diagnosis of stage III or stage IV warrants appropriate imaging surveillance regimen for stage III patients.

Type of bony involvement predicts genomic subgroup in sphenoid wing meningiomas.

PURPOSE: As sphenoid wing meningiomas (SWMs) are associated with varying degrees of bony involvement, we sought to understand potential relationships between genomic subgroup and this feature. METHODS: Patients treated at Yale-New Haven Hospital for SWM were reviewed. Genomic subgroup was determined via whole exome sequencing, while the extent of bony involvement was radiographically classified as no bone invasion (Type I), hyperostosis only (Type II), tumor invasion only (Type III), or both hyperostosis and tumor invasion (Type IV). Among additional clinical variables collected, a subset of tumors was identified as spheno-orbital meningiomas (SOMs). Machine-learning approaches were used to predict genomic subgroups based on pre-operative clinical features. RESULTS: Among 64 SWMs, 53% had Type-II, 9% had Type-III, and 14% had Type-IV bone involvement; nine SOMs were identified. Tumors with invasion (i.e., Type III or IV) were more likely to be WHO grade II (p: 0.028). Additionally, tumors with invasion were nearly 30 times more likely to harbor NF2 mutations (OR 27.6; p: 0.004), while hyperostosis only were over 4 times more likely to have a TRAF7 mutation (OR 4.5; p: 0.023). SOMs were a significant predictor of underlying TRAF7 mutation (OR 10.21; p: 0.004). CONCLUSIONS: SWMs with invasion into bone tend to be higher grade and are more likely to be NF2 mutated, while SOMs and those with hyperostosis are associated with TRAF7 variants. Pre-operative prediction of molecular subtypes based on radiographic bony characteristics may have significant biological and clinical implications based on known recurrence patterns associated with genomic drivers and grade.

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.

Case 245: Erdheim-Chester Disease.

History A 53-year-old man experienced headache and double vision that progressed over 1 year. After a traumatic fall, he was hospitalized, and proptosis was identified at physical examination. Laboratory tests were remarkable for leukocytosis. Hematocrit level, thyroid stimulating hormone level, autoimmune antibody level, erythrocyte sedimentation rate, and C-reactive protein level were normal. Computed tomography (CT) of the head revealed bilateral intraconal masses, for which magnetic resonance (MR) imaging of the orbits was subsequently performed ( Fig 1 ). CT imaging of the chest and abdomen ( Fig 2 ) revealed periaortic and retroperitoneal stranding. Perinephric biopsy was performed, and a diagnosis of immunoglobulin G4 (IgG4)-related disease was made based on identification of a few plasma cells per high-power field that were positive for IgG4. Orbital biopsy was then performed, but the results were inconclusive for IgG4-related disease. The patient was discharged and given steroid therapy for presumed IgG4-related disease. [Figure: see text][Figure: see text][Figure: see text][Figure: see text][Figure: see text][Figure: see text] Several months later, the patient returned to our institution with progressive symptoms despite ongoing steroid treatment. His case was reviewed by several specialists to develop alternative treatments for IgG4-related disease. After review of the available images, a neuroradiology fellow (M.D.M.) performed history taking and a physical examination and subsequently recommended radiography of the lower extremities ( Fig 3 ). [Figure: see text][Figure: see text].

Apparent diffusion coefficient and pituitary macroadenomas: pre-operative assessment of tumor atypia.

RATIONALE AND OBJECTIVES: Pituitary macroadenomas are predominantly benign intracranial neoplasms that can be locally aggressive with invasion of adjacent structures. Biomarkers of aggressive behavior have been identified in the pathology literature, including the proliferative marker MIB-1. In the radiology literature, diffusion weighted imaging and low ADC values provide similar markers of aggressive behavior in brain tumors. The purpose of this study was to determine if there is a correlation between ADC and MIB-1 in pituitary macroadenomas. MATERIALS AND METHODS: A retrospective review of diffusion imaging and immunohistochemical characteristics of pituitary macroadenomas was performed. The ADC ratio and specimen Ki-67 (MIB-1) indices were measured. Linear regression analysis of normalized ADC values and MIB-1 indices was used to compare these parameters. RESULTS: There were 17 patients with available ADC maps and MIB-1 indices. Local invasion was confirmed by imaging and intraoperative visualization in 11 patients. The mean ADC ratio for the invasive group was 0.68, with a mean MIB-1 index of 2.21 %. In the noninvasive group, the mean ADC ratio was 1.05, with a mean MIB-1 index of 0.9 %. Linear regression analysis of normalized ADC values versus MIB-1 demonstrates a negative correlation, with a linear slope significantly different from zero (p = 0.003, correlation coefficient of 0.77, and r squared = 0.59). CONCLUSION: We determine a strong correlation of low ADC values and MIB-1, demonstrating the potential of diffusion imaging as a possible biomarker for atypical, proliferative adenomas, which may ultimately affect the surgical approach and postoperative management.

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.

Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and seminars focused on the present impact and future directions of artificial intelligence (AI) in radiology. These conversations have collected viewpoints from multidisciplinary experts in radiology, medical imaging, and machine learning on the current clinical penetration of AI technology in radiology, and how it is impacted by trust, reproducibility, explainability, and accountability. The collective points-both practical and philosophical-define the cultural changes for radiologists and AI scientists working together and describe the challenges ahead for AI technologies to meet broad approval. This article presents the perspectives of experts from MICCAI and RSNA on the clinical, cultural, computational, and regulatory considerations-coupled with recommended reading materials-essential to adopt AI technology successfully in radiology and more generally in clinical practice. The report emphasizes the importance of collaboration to improve clinical deployment and highlights the need to integrate clinical and medical imaging data and introduces strategies to ensure smooth and incentivized integration. ©RSNA, 2024.

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.

Impact of 18F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers.

We aimed to investigate the effects of 18F-FDG PET voxel intensity normalization on radiomic features of oropharyngeal squamous cell carcinoma (OPSCC) and machine learning-generated radiomic biomarkers. Methods: We extracted 1,037 18F-FDG PET radiomic features quantifying the shape, intensity, and texture of 430 OPSCC primary tumors. The reproducibility of individual features across 3 intensity-normalized images (body-weight SUV, reference tissue activity ratio to lentiform nucleus of brain and cerebellum) and the raw PET data was assessed using an intraclass correlation coefficient (ICC). We investigated the effects of intensity normalization on the features' utility in predicting the human papillomavirus (HPV) status of OPSCCs in univariate logistic regression, receiver-operating-characteristic analysis, and extreme-gradient-boosting (XGBoost) machine-learning classifiers. Results: Of 1,037 features, a high (ICC ≥ 0.90), medium (0.90 > ICC ≥ 0.75), and low (ICC < 0.75) degree of reproducibility across normalization methods was attained in 356 (34.3%), 608 (58.6%), and 73 (7%) features, respectively. In univariate analysis, features from the PET normalized to the lentiform nucleus had the strongest association with HPV status, with 865 of 1,037 (83.4%) significant features after multiple testing corrections and a median area under the receiver-operating-characteristic curve (AUC) of 0.65 (interquartile range, 0.62-0.68). Similar tendencies were observed in XGBoost models, with the lentiform nucleus-normalized model achieving the numerically highest average AUC of 0.72 (SD, 0.07) in the cross validation within the training cohort. The model generalized well to the validation cohorts, attaining an AUC of 0.73 (95% CI, 0.60-0.85) in independent validation and 0.76 (95% CI, 0.58-0.95) in external validation. The AUCs of the XGBoost models were not significantly different. Conclusion: Only one third of the features demonstrated a high degree of reproducibility across intensity-normalization techniques, making uniform normalization a prerequisite for interindividual comparability of radiomic markers. The choice of normalization technique may affect the radiomic features' predictive value with respect to HPV. Our results show trends that normalization to the lentiform nucleus may improve model performance, although more evidence is needed to draw a firm conclusion.

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.

Towards Consistency in Pediatric Brain Tumor Measurements: Challenges, Solutions, and the Role of AI-Based Segmentation.

MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Several response assessment guidelines have been set forth by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working groups in different tumor histologies; however, the visual delineation of tumor components using MRIs is not always straightforward, and complexities not currently addressed by these criteria can introduce inter- and intra-observer variability in manual assessments. Differentiation of non-enhancing tumor from peritumoral edema, mild enhancement from absence of enhancement, and various cystic components can be challenging; particularly given a lack of sufficient and uniform imaging protocols in clinical practice. Automated tumor segmentation with artificial intelligence (AI) may be able to provide more objective delineations, but rely on accurate and consistent training data created manually (ground truth). Herein, this paper reviews existing challenges and potential solutions to identifying and defining subregions of pediatric brain tumors (PBTs) that are not explicitly addressed by current guidelines. The goal is to assert the importance of defining and adopting criteria for addressing these challenges, as it will be critical to achieving standardized tumor measurements and reproducible response assessment in PBTs, ultimately leading to more precise outcome metrics and accurate comparisons among clinical studies.

Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation.

Deep-learning methods for auto-segmenting brain images either segment one slice of the image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). Whether one approach is superior for auto-segmenting brain images is not known. We compared these three approaches (3D, 2.5D, and 2D) across three auto-segmentation models (capsule networks, UNets, and nnUNets) to segment brain structures. We used 3430 brain MRIs, acquired in a multi-institutional study, to train and test our models. We used the following performance metrics: segmentation accuracy, performance with limited training data, required computational memory, and computational speed during training and deployment. The 3D, 2.5D, and 2D approaches respectively gave the highest to lowest Dice scores across all models. 3D models maintained higher Dice scores when the training set size was decreased from 3199 MRIs down to 60 MRIs. 3D models converged 20% to 40% faster during training and were 30% to 50% faster during deployment. However, 3D models require 20 times more computational memory compared to 2.5D or 2D models. This study showed that 3D models are more accurate, maintain better performance with limited training data, and are faster to train and deploy. However, 3D models require more computational memory compared to 2.5D or 2D models.

Brain PET Imaging: Approach to Cognitive Impairment and Dementia.

Alzheimer disease (AD) is the most common cause of dementia, accounting for 50% to 60% of cases and affecting nearly 6 million people in the United States. Definitive diagnosis requires either antemortem brain biopsy or postmortem autopsy. However, clinical neuroimaging has been playing a greater role in the diagnosis and management of AD, and several PET tracers approach the sensitivity of tissue diagnosis in identifying AD pathologic condition. This review will focus on the utility of PET imaging in the setting of cognitive impairment, with an emphasis on its role in the diagnosis of AD.