Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs

American Journal of Neuroradiology · 2024 · 4 citations

• Medicine
• Neuroscience
• Cognitive science

BACKGROUND AND PURPOSE: Privacy concerns, such as identifiable facial features within brain scans, have hindered the availability of pediatric neuroimaging data sets for research. Consequently, pediatric neuroscience research lags adult counterparts, particularly in rare disease and under-represented populations. The removal of face regions (image defacing) can mitigate this; however, existing defacing tools often fail with pediatric cases and diverse image types, leaving a critical gap in data accessibility. Given recent National Institutes of Health data sharing mandates, novel solutions are a critical need. MATERIALS AND METHODS: To develop an artificial intelligence (AI)-powered tool for automatic defacing of pediatric brain MRIs, deep learning methodologies (nnU-Net) were used by using a large, diverse multi-institutional data set of clinical radiology images. This included multiparametric MRIs (T1-weighted [T1W], T1W-contrast-enhanced, T2-weighted [T2W], T2W-FLAIR) with 976 total images from 208 patients with brain tumor (Children's Brain Tumor Network, CBTN) and 36 clinical control patients (Scans with Limited Imaging Pathology, SLIP) ranging in age from 7 days to 21 years old. RESULTS: < .0001). CONCLUSIONS: The defacing model demonstrates efficacy in removing facial regions across multiple MRI types and exhibits minimal impact on downstream research usage. A software package with the trained model is freely provided for wider use and further development (pediatric-auto-defacer; https://github.com/d3b-center/pediatric-auto-defacer-public). By offering a solution tailored to pediatric cases and multiple MRI sequences, this defacing tool will expedite research efforts and promote broader adoption of data sharing practices within the neuroscience community.

HGG-32. PNOC008: A PILOT TRIAL TESTING THE CLINICAL BENEFIT OF USING MOLECULAR PROFILING TO DETERMINE AN INDIVIDUALIZED TREATMENT PLAN IN CHILDREN AND YOUNG ADULTS WITH NEWLY DIAGNOSED HIGH-GRADE GLIOMA (EXCLUDING DIFFUSE INTRINSIC PONTINE GLIOMA)

Neuro-Oncology · 2024 · 2 citations

• Computer Science
• Medicine
• Oncology

Abstract BACKGROUND Children and young adults diagnosed with high-grade glioma (HGG) face extremely poor prognoses. Despite multiple clinical trials testing new treatments in this population, a survival advantage has yet to be achieved. Herein we assessed, in a single-arm, multi-center pilot trial, the feasibility of molecular profiling of primary HGG tumor tissue to create an individualized treatment plan with up to four FDA approved medications. METHODS Patients aged &amp;lt;21 years with newly diagnosed, localized, hemispheric HGG (Stratum A) or midline HGG (non-DIPG; Stratum B) were eligible. Tumor tissue underwent comprehensive molecular profiling (targeted gene panel, whole exome, and whole transcriptome sequencing). Based on detailed review of the molecular data by a dedicated tumor board, an individualized treatment plan that combined up to four FDA approved drugs was recommended. Circulating tumor DNA (ctDNA), imaging, and quality of life (QOL) measures were collected at multiple timepoints. RESULTS Fifty-five patients enrolled between 2018 and 2023 (median age 11 years [range 2-20], n=31 female, n=29 Stratum A). The most common integrated diagnoses included H3K27-altered diffuse midline glioma (n=17), H3/IDH-wildtype diffuse pediatric-type HGG (n=16), and H3G34-mutant diffuse hemispheric glioma (n=12). Median overall survival (OS) from the time of study enrollment was 26.5 months in Stratum A (lower 95% CI: 18.7) and 21.7 months in Stratum B (lower 95% CI: 16.8), with a median follow-up of 35.4 months for all patients (lower 95% CI: 32.5). The most common grade 3 or 4 treatment-related adverse events were decreased neutrophils (n=28), decreased platelets (n=22), and decreased white blood cells (n=16). As of December 2023, seven patients remain on therapy. Central imaging, ctDNA, and QOL analyses are underway. CONCLUSIONS A personalized treatment recommendation for children and young adults with HGG based on comprehensive transcriptomic and genomic analysis is feasible. Current survival data are encouraging, and molecular subgroup analyses are ongoing.

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

Neuro-Oncology Advances · 2023 · 50 citations

• Computer Science
• Artificial Intelligence
• Medicine

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: = 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.

The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution

Cell · 2020 · 581 citations

• Biology
• Computational biology
• Bioinformatics

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.