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.

Noninvasive Molecular Subtyping of Pediatric Low-Grade Glioma with Self-Supervised Transfer Learning.

Purpose To develop and externally test a scan-to-prediction deep learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pediatric low-grade glioma. Materials and Methods This retrospective study included two pediatric low-grade glioma datasets with linked genomic and diagnostic T2-weighted MRI data of patients: Dana-Farber/Boston Children's Hospital (development dataset, n = 214 [113 (52.8%) male; 104 (48.6%) BRAF wild type, 60 (28.0%) BRAF fusion, and 50 (23.4%) BRAF V600E]) and the Children's Brain Tumor Network (external testing, n = 112 [55 (49.1%) male; 35 (31.2%) BRAF wild type, 60 (53.6%) BRAF fusion, and 17 (15.2%) BRAF V600E]). A deep learning pipeline was developed to classify BRAF mutational status (BRAF wild type vs BRAF fusion vs BRAF V600E) via a two-stage process: (a) three-dimensional tumor segmentation and extraction of axial tumor images and (b) section-wise, deep learning-based classification of mutational status. Knowledge-transfer and self-supervised approaches were investigated to prevent model overfitting, with a primary end point of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, a novel metric, center of mass distance, was developed to quantify the model attention around the tumor. Results A combination of transfer learning from a pretrained medical imaging-specific network and self-supervised label cross-training (TransferX) coupled with consensus logic yielded the highest classification performance with an AUC of 0.82 (95% CI: 0.72, 0.91), 0.87 (95% CI: 0.61, 0.97), and 0.85 (95% CI: 0.66, 0.95) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively, on internal testing. On external testing, the pipeline yielded an AUC of 0.72 (95% CI: 0.64, 0.86), 0.78 (95% CI: 0.61, 0.89), and 0.72 (95% CI: 0.64, 0.88) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively. Conclusion Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pediatric low-grade glioma mutational status prediction in a limited data scenario. Keywords: Pediatrics, MRI, CNS, Brain/Brain Stem, Oncology, Feature Detection, Diagnosis, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024.

DNA methylation landscapes in DIPG reveal methylome variability that can be modified pharmacologically.

Background: Diffuse intrinsic pontine glioma (DIPG) is a uniformly lethal brainstem tumor of childhood, driven by histone H3 K27M mutation and resultant epigenetic dysregulation. Epigenomic analyses of DIPG have shown global loss of repressive chromatin marks accompanied by DNA hypomethylation. However, studies providing a static view of the epigenome do not adequately capture the regulatory underpinnings of DIPG cellular heterogeneity and plasticity. Methods: To address this, we performed whole-genome bisulfite sequencing on a large panel of primary DIPG specimens and applied a novel framework for analysis of DNA methylation variability, permitting the derivation of comprehensive genome-wide DNA methylation potential energy landscapes that capture intrinsic epigenetic variation. Results: We show that DIPG has a markedly disordered epigenome with increasingly stochastic DNA methylation at genes regulating pluripotency and developmental identity, potentially enabling cells to sample diverse transcriptional programs and differentiation states. The DIPG epigenetic landscape was responsive to treatment with the hypomethylating agent decitabine, which produced genome-wide demethylation and reduced the stochasticity of DNA methylation at active enhancers and bivalent promoters. Decitabine treatment elicited changes in gene expression, including upregulation of immune signaling such as the interferon response, STING, and MHC class I expression, and sensitized cells to the effects of histone deacetylase inhibition. Conclusions: This study provides a resource for understanding the epigenetic instability that underlies DIPG heterogeneity. It suggests the application of epigenetic therapies to constrain the range of epigenetic states available to DIPG cells, as well as the use of decitabine in priming for immune-based therapies.

AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classification.

SUMMARY: With the increasing rates of exome and whole genome sequencing, the ability to classify large sets of germline sequencing variants using up-to-date American College of Medical Genetics-Association for Molecular Pathology (ACMG-AMP) criteria is crucial. Here, we present Automated Germline Variant Pathogenicity (AutoGVP), a tool that integrates germline variant pathogenicity annotations from ClinVar and sequence variant classifications from a modified version of InterVar (PVS1 strength adjustments, removal of PP5/BP6). This tool facilitates large-scale, clinically focused classification of germline sequence variants in a research setting. AVAILABILITY AND IMPLEMENTATION: AutoGVP is an open source dockerized workflow implemented in R and freely available on GitHub at https://github.com/diskin-lab-chop/AutoGVP.

A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.

Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities to adult glioma, it is increasingly recognized as a molecularly distinct, yet highly heterogeneous disease. In this study, we longitudinally profiled a molecularly diverse cohort of 16 pHGG patients before and after standard therapy through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of the tumor microenvironment during progression following treatment. We found that the canonical neoplastic cell phenotypes of adult glioblastoma are insufficient to capture the range of tumor cell states in a pediatric cohort and observed differential tumor-myeloid interactions between malignant cell states. We identified key transcriptional regulators of pHGG cell states and did not observe the marked proneural to mesenchymal shift characteristic of adult glioblastoma. We showed that essential neuromodulators and the interferon response are upregulated post-therapy along with an increase in non-neoplastic oligodendrocytes. Through in vitro pharmacological perturbation, we demonstrated novel malignant cell-intrinsic targets. This multiomic atlas of longitudinal pHGG captures the key features of therapy response that support distinction from its adult counterpart and suggests therapeutic strategies which are targeted to pediatric gliomas.

SNOT-22 subdomain outcomes following treatment for sinonasal malignancy: A prospective, multicenter study.

BACKGROUND: Patients with sinonasal malignancy (SNM) present with significant sinonasal quality of life (QOL) impairment. Global sinonasal QOL as measured by the 22-item Sinonasal Outcomes Test (SNOT-22) has been shown to improve with treatment. This study aims to characterize SNOT-22 subdomain outcomes in SNM. METHODS: Patients diagnosed with SNM were prospectively enrolled in a multi-center patient registry. SNOT-22 scores were collected at the time of diagnosis and through the post-treatment period for up to 5 years. Multivariable regression analysis was used to identify drivers of variation in SNOT-22 subdomains. RESULTS: Note that 234 patients were reviewed, with a mean follow-up of 22 months (3 months-64 months). Rhinologic, psychological, and sleep subdomains significantly improved versus baseline (all p < 0.05). Subanalysis of 40 patients with follow-up at all timepoints showed statistically significant improvement in rhinologic, extra-nasal, psychological, and sleep subdomains, with minimal clinically important difference met between 2 and 5 years in sleep and psychological subdomains. Adjuvant chemoradiation was associated with worse outcomes in rhinologic (adjusted odds ratio (5.22 [1.69-8.66])), extra-nasal (2.21 [0.22-4.17]) and ear/facial (5.53 [2.10-8.91]) subdomains. Pterygopalatine fossa involvement was associated with worse outcomes in rhinologic (3.22 [0.54-5.93]) and ear/facial (2.97 [0.32-5.65]) subdomains. Positive margins (5.74 [2.17-9.29]) and surgical approach-combined versus endoscopic (3.41 [0.78-6.05])-were associated with worse psychological outcomes. Adjuvant radiation (2.28 [0.18-4.40]) was associated with worse sleep outcomes. CONCLUSIONS: Sinonasal QOL improvements associated with treatment of SNM are driven by rhinologic, extra-nasal, psychological, and sleep subdomains.

Predictive factors for decreased baseline quality of life in patients with sinonasal malignancies.

BACKGROUND: The impact of sinonasal malignancies (SNMs) on quality of life (QOL) at presentation is poorly understood. The Sinonasal Outcome Test (SNOT-22) and University of Washington Quality of Life (UWQOL) are validated QOL instruments with distinctive subdomains. This study aims to identify factors impacting pretreatment QOL in SNM patients to personalize multidisciplinary management and counseling. METHODS: Patients with previously untreated SNMs were prospectively enrolled (2015-2022) in a multicenter observational study. Baseline pretreatment QOL instruments (SNOT-22, UWQOL) were obtained along with demographics, comorbidities, histopathology/staging, tumor involvement, and symptoms. Multivariable regression models identified factors associated with reduced baseline QOL. RESULTS: Among 204 patients, presenting baseline QOL was significantly reduced. Multivariable regression showed worse total SNOT-22 QOL in patients with skull base erosion (p = 0.02). SNOT-rhinologic QOL was worse in women (p = 0.009), patients with epistaxis (p = 0.036), and industrial exposure (p = 0.005). SNOT extranasal QOL was worse in patients with industrial exposure (p = 0.016); worse SNOT ear/facial QOL if perineural invasion (PNI) (p = 0.027). Squamous cell carcinoma pathology (p = 0.037), palate involvement (p = 0.012), and pain (p = 0.017) were associated with worse SNOT sleep QOL scores. SNOT psychological subdomain scores were significantly worse in patients with palate lesions (p = 0.022), skull base erosion (p = 0.025), and T1 staging (p = 0.023). Low QOL was more likely in the presence of PNI on UW health (p = 0.019) and orbital erosion on UW overall (p = 0.03). UW social QOL was worse if palatal involvement (p = 0.023) or PNI (p = 0.005). CONCLUSIONS: Our findings demonstrate a negative impact on baseline QOL in patients with SNMs and suggest sex-specific and symptom-related lower QOL scores, with minimal histopathology association. Anatomical tumor involvement may be more reflective of QOL than T-staging, as orbital and skull base erosion, PNI, and palate lesions are significantly associated with reduced baseline QOL.

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.

AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classification.

With the increasing rates of exome and whole genome sequencing, the ability to classify large sets of germline sequencing variants using up-to-date American College of Medical Genetics - Association for Molecular Pathology (ACMG-AMP) criteria is crucial. Here, we present Automated Germline Variant Pathogenicity (AutoGVP), a tool that integrates germline variant pathogenicity annotations from ClinVar and sequence variant classifications from a modified version of InterVar (PVS1 strength adjustments, removal of PP5/BP6). This tool facilitates large-scale, clinically-focused classification of germline sequence variants in a research setting.

A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network.

Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the wealth of radiology imaging data collected through standard care, however, has significantly lagged other domains. Such data is ripe for use with predictive analytics such as artificial intelligence (AI) methods, which require large datasets. To address this unmet need, we provide a multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric MRI exams acquired through routine care for 1,526 brain tumor patients, as part of the Children's Brain Tumor Network. This includes longitudinal MRIs across various cancer diagnoses, with associated patient-level clinical information, digital pathology slides, as well as tissue genotype and omics data. To facilitate downstream analysis, treatment-naïve images for 370 subjects were processed and released through the NCI Childhood Cancer Data Initiative via the Cancer Data Service. Through ongoing efforts to continuously build these imaging repositories, our aim is to accelerate discovery and translational AI models with real-world data, to ultimately empower precision medicine for children.

Automated temporalis muscle quantification and growth charts for children through adulthood.

Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its unknown normal growth trajectory and reference ranges and lack of standardized measurement. Here, we develop an automated deep learning pipeline to accurately measure temporalis muscle thickness (iTMT) from routine brain magnetic resonance imaging (MRI). We apply iTMT to 23,876 MRIs of healthy subjects, ages 4 through 35, and generate sex-specific iTMT normal growth charts with percentiles. We find that iTMT was associated with specific physiologic traits, including caloric intake, physical activity, sex hormone levels, and presence of malignancy. We validate iTMT across multiple demographic groups and in children with brain tumors and demonstrate feasibility for individualized longitudinal monitoring. The iTMT pipeline provides unprecedented insights into temporalis muscle growth during human development and enables the use of LMM tracking to inform clinical decision-making.

Radio-pathomic approaches in pediatric neuro-oncology: Opportunities and challenges.

With medical software platforms moving to cloud environments with scalable storage and computing, the translation of predictive artificial intelligence (AI) models to aid in clinical decision-making and facilitate personalized medicine for cancer patients is becoming a reality. Medical imaging, namely radiologic and histologic images, has immense analytical potential in neuro-oncology, and models utilizing integrated radiomic and pathomic data may yield a synergistic effect and provide a new modality for precision medicine. At the same time, the ability to harness multi-modal data is met with challenges in aggregating data across medical departments and institutions, as well as significant complexity in modeling the phenotypic and genotypic heterogeneity of pediatric brain tumors. In this paper, we review recent pathomic and integrated pathomic, radiomic, and genomic studies with clinical applications. We discuss current challenges limiting translational research on pediatric brain tumors and outline technical and analytical solutions. Overall, we propose that to empower the potential residing in radio-pathomics, systemic changes in cross-discipline data management and end-to-end software platforms to handle multi-modal data sets are needed, in addition to embracing modern AI-powered approaches. These changes can improve the performance of predictive models, and ultimately the ability to advance brain cancer treatments and patient outcomes through the development of such models.

Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal.

International cancer registries make real-world genomic and clinical data available, but their joint analysis remains a challenge. AACR Project GENIE, an international cancer registry collecting data from 19 cancer centers, makes data from >130,000 patients publicly available through the cBioPortal for Cancer Genomics (https://genie.cbioportal.org). For 25,000 patients, additional real-world longitudinal clinical data, including treatment and outcome data, are being collected by the AACR Project GENIE Biopharma Collaborative using the PRISSMM data curation model. Several thousand of these cases are now also available in cBioPortal. We have significantly enhanced the functionalities of cBioPortal to support the visualization and analysis of this rich clinico-genomic linked dataset, as well as datasets generated by other centers and consortia. Examples of these enhancements include (i) visualization of the longitudinal clinical and genomic data at the patient level, including timelines for diagnoses, treatments, and outcomes; (ii) the ability to select samples based on treatment status, facilitating a comparison of molecular and clinical attributes between samples before and after a specific treatment; and (iii) survival analysis estimates based on individual treatment regimens received. Together, these features provide cBioPortal users with a toolkit to interactively investigate complex clinico-genomic data to generate hypotheses and make discoveries about the impact of specific genomic variants on prognosis and therapeutic sensitivities in cancer. SIGNIFICANCE: Enhanced cBioPortal features allow clinicians and researchers to effectively investigate longitudinal clinico-genomic data from patients with cancer, which will improve exploration of data from the AACR Project GENIE Biopharma Collaborative and similar datasets.

Transcriptional immunogenomic analysis reveals distinct immunological clusters in paediatric nervous system tumours.

BACKGROUND: Cancer immunotherapies including immune checkpoint inhibitors and Chimeric Antigen Receptor (CAR) T-cell therapy have shown variable response rates in paediatric patients highlighting the need to establish robust biomarkers for patient selection. While the tumour microenvironment in adults has been widely studied to delineate determinants of immune response, the immune composition of paediatric solid tumours remains relatively uncharacterized calling for investigations to identify potential immune biomarkers. METHODS: To inform immunotherapy approaches in paediatric cancers with embryonal origin, we performed an immunogenomic analysis of RNA-seq data from 925 treatment-naïve paediatric nervous system tumours (pedNST) spanning 12 cancer types from three publicly available data sets. RESULTS: Within pedNST, we uncovered four broad immune clusters: Paediatric Inflamed (10%), Myeloid Predominant (30%), Immune Neutral (43%) and Immune Desert (17%). We validated these clusters using immunohistochemistry, methylation immune inference and segmentation analysis of tissue images. We report shared biology of these immune clusters within and across cancer types, and characterization of specific immune cell frequencies as well as T- and B-cell repertoires. We found no associations between immune infiltration levels and tumour mutational burden, although molecular cancer entities were enriched within specific immune clusters. CONCLUSIONS: Given the heterogeneity of immune infiltration within pedNST, our findings suggest personalized immunogenomic profiling is needed to guide selection of immunotherapeutic strategies.

Radiomics for characterization of the glioma immune microenvironment.

Increasing evidence suggests that besides mutational and molecular alterations, the immune component of the tumor microenvironment also substantially impacts tumor behavior and complicates treatment response, particularly to immunotherapies. Although the standard method for characterizing tumor immune profile is through performing integrated genomic analysis on tissue biopsies, the dynamic change in the immune composition of the tumor microenvironment makes this approach not feasible, especially for brain tumors. Radiomics is a rapidly growing field that uses advanced imaging techniques and computational algorithms to extract numerous quantitative features from medical images. Recent advances in machine learning methods are facilitating biological validation of radiomic signatures and allowing them to "mine" for a variety of significant correlates, including genetic, immunologic, and histologic data. Radiomics has the potential to be used as a non-invasive approach to predict the presence and density of immune cells within the microenvironment, as well as to assess the expression of immune-related genes and pathways. This information can be essential for patient stratification, informing treatment decisions and predicting patients' response to immunotherapies. This is particularly important for tumors with difficult surgical access such as gliomas. In this review, we provide an overview of the glioma microenvironment, describe novel approaches for clustering patients based on their tumor immune profile, and discuss the latest progress on utilization of radiomics for immune profiling of glioma based on current literature.

A metagenomic analysis of the virome of inverted papilloma and squamous cell carcinoma.

INTRODUCTION: Inverted papilloma (IP) is a sinonasal tumor with a well-known potential for malignant transformation. The role of human papillomavirus (HPV) in its pathogenesis has been controversial. The purpose of this study was to determine the virome associated with IP, with progression to carcinoma in situ (CIS), and invasive carcinoma. METHODS: To determine the HPV-specific types, a metagenomics assay that contains 62,886 probes targeting viral genomes in a microarray format was used. The platform screens DNA and RNA from fixed tissues from eight controls, 16 IP without dysplasia, five IP with CIS, and 13 IP-associated squamous cell carcinoma (IPSCC). Paired with next-generation sequencing, 48 types of HPV with 857 region-specific probes were interrogated against the tumors. RESULTS: The prevalence of HPV-16 was 14%, 42%, 70%, and 73% in control tissue, IP without dysplasia, IP with CIS, and IPSCC, respectively. The prevalence of HPV-18 had a similar progressive increase in prevalence, with 14%, 27%, 67%, and 74%, respectively. The assay allowed region-specific analysis, which identified the only oncogenic HPV-18 E6 to be statistically significant when compared with control tissue. The prevalence of HPV-18 E6 was 0% in control tissue, 25% in IP without dysplasia, 60% in IP with CIS, and 77% in IPSCC. CONCLUSIONS: There are over 200 HPV types that infect human epithelial cells, of which only a few are known to be high-risk. Our study demonstrated a trend of increasing prevalence of HPV-18 E6 that correlated with histologic severity, which is novel and supports a potential role for HPV in the pathogenesis of IP.

Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study.

Background: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients ( n = 215 internal and n = 29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training ( n = 151), validation ( n = 43), and withheld internal test ( n = 21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results: Dice similarity score (median ± SD) was 0.91 ± 0.10/0.88 ± 0.16 for the whole tumor, 0.73 ± 0.27/0.84 ± 0.29 for ET, 0.79 ± 19/0.74 ± 0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98 ± 0.02 for brain tissue in both internal/external test sets. Conclusions: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements.

Long-term quality of life after treatment in sinonasal malignancy: A prospective, multicenter study.

BACKGROUND: Quality of life (QOL) for individuals with sinonasal malignancy (SNM) is significantly under-studied, yet it is critical for counseling and may impact treatment. In this study we evaluated how patient, treatment, and disease factors impact sinonasal-specific and generalized QOL using validated metrics in a large cohort over a 5-year posttreatment time frame. METHODS: Patients with SNM who underwent definitive treatment with curative intent were enrolled in a prospective, multisite, longitudinal observational study. QOL was assessed using the 22-item Sino-Nasal Outcome Test (SNOT-22) and University of Washington Quality of Life Questionnaire (UWQOL) instruments at pretreatment baseline and multiple follow-ups through 5 years posttreatment. Multivariable modeling was used to determine demographic, disease, and treatment factors associated with disease-specific and generalized physical and social/emotional function QOL. RESULTS: One hundred ninety-four patients with SNM were analyzed. All QOL indices were impaired at pretreatment baseline and improved after treatment. SNOT-22 scores improved 3 months and UWQOL scores improved 6 to 9 months posttreatment. Patients who underwent open compared with endoscopic tumor resection had worse generalized QOL (p < 0.001), adjusted for factors including T stage. Pterygopalatine fossa (PPF) involvement was associated with worse QOL (SNOT-22, p < 0.001; UWQOL Physical dimension, p = 0.02). Adjuvant radiation was associated with worse disease-specific QOL (p = 0.03). Neck dissection was associated with worse generalized physical function QOL (p = 0.01). Positive margins were associated with worse generalized social/emotional function QOL (p = 0.01). CONCLUSION: Disease-specific and generalized QOL is impaired at baseline in patients with SNM and improves after treatment. Endoscopic resection is associated with better QOL. PPF involvement, adjuvant radiation, neck dissection, and positive margins were associated with worse QOL posttreatment.

Intracranial Cannula Implantation for Serial Locoregional Chimeric Antigen Receptor (CAR) T Cell Infusions in Mice.

Pediatric CNS tumors are responsible for the majority of cancer-related deaths in children and have poor prognoses, despite advancements in chemotherapy and radiotherapy. As many tumors lack efficacious treatments, there is a crucial need to develop more promising therapeutic options, such as immunotherapies; the use of chimeric antigen receptor (CAR) T cell therapy directed against CNS tumors is of particular interest. Cell surface targets such as B7-H3, IL13RA2, and the disialoganglioside GD2 are highly expressed on the surface of several pediatric and adult CNS tumors, raising the opportunity to use CAR T cell therapy against these and other surface targets. To evaluate the repeated locoregional delivery of CAR T cells in preclinical murine models, an indwelling catheter system that recapitulates indwelling catheters currently being used in human clinical trials was established. Unlike stereotactic delivery, the indwelling catheter system allows for repeated dosing without the use of multiple surgeries. This protocol describes the intratumoral placement of a fixed guide cannula that has been used to successfully test serial CAR T cell infusions in orthotopic murine models of pediatric brain tumors. Following orthotopic injection and engraftment of the tumor cells in mice, intratumoral placement of a fixed guide cannula is completed on a stereotactic apparatus and secured with screws and acrylic resin. Treatment cannulas are then inserted through the fixed guide cannula for repeated CAR T cell delivery. Stereotactic placement of the guide cannula can be adjusted to deliver CAR T cells directly into the lateral ventricle or other locations in the brain. This platform offers a reliable mechanism for the preclinical testing of repeated intracranial infusions of CAR T cells and other novel therapeutics for these devastating pediatric tumors.