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.

Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach

Scientific Reports · 2024 · 23 citations

• Computer Science
• Artificial Intelligence
• Computational biology

Glioblastoma is a highly heterogeneous disease, with variations observed at both phenotypical and molecular levels. Personalized therapies would be facilitated by non-invasive in vivo approaches for characterizing this heterogeneity. In this study, we developed unsupervised joint machine learning between radiomic and genomic data, thereby identifying distinct glioblastoma subtypes. A retrospective cohort of 571 IDH-wildtype glioblastoma patients were included in the study, and pre-operative multi-parametric MRI scans and targeted next-generation sequencing (NGS) data were collected. L21-norm minimization was used to select a subset of 12 radiomic features from the MRI scans, and 13 key driver genes from the five main signal pathways most affected in glioblastoma were selected from the genomic data. Subtypes were identified using a joint learning approach called Anchor-based Partial Multi-modal Clustering on both radiomic and genomic modalities. Kaplan-Meier analysis identified three distinct glioblastoma subtypes: high-risk, medium-risk, and low-risk, based on overall survival outcome (p < 0.05, log-rank test; Hazard Ratio = 1.64, 95% CI 1.17-2.31, Cox proportional hazard model on high-risk and low-risk subtypes). The three subtypes displayed different phenotypical and molecular characteristics in terms of imaging histogram, co-occurrence of genes, and correlation between the two modalities. Our findings demonstrate the synergistic value of integrated radiomic signatures and molecular characteristics for glioblastoma subtyping. Joint learning on both modalities can aid in better understanding the molecular basis of phenotypical signatures of glioblastoma, and provide insights into the biological underpinnings of tumor formation and progression.

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.

Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma

Scientific Reports · 2022 · 86 citations

• Computer Science
• Artificial Intelligence
• Machine Learning

Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS < 6 months) vs all, and low-risk (OS ≥ 18 months) vs all. The trained radiomic model was independently tested in the replication cohort (n = 112) and a patient-wise survival prediction index was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed area under the ROC curves (AUCs) of 0.78 (95% CI 0.70-0.85)/0.75 (95% CI 0.64-0.79) and 0.75 (95% CI 0.65-0.84)/0.63 (95% CI 0.52-0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95% CI 0.6-0.7) for clinical data improving to 0.75 (95% CI 0.72-0.79) for the combination of all omics. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM.

Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine

Cancers · 2021 · 74 citations

• Computer Science
• Medicine
• Medical physics

Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.