Faculty Bibliography
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246
A Radiologist's Guide to IDH-Wildtype Glioblastoma for Efficient Communication With Clinicians: Part II-Essential Information on Post-Treatment Imaging
Owing to recent advancements in various postoperative treatment modalities, such as radiation, chemotherapy, antiangiogenic treatment, and immunotherapy, the radiological and clinical assessment of patients with isocitrate dehydrogenase-wildtype glioblastoma using post-treatment imaging has become increasingly challenging. This review highlights the challenges in differentiating treatment-related changes such as pseudoprogression, radiation necrosis, and pseudoresponse from true tumor progression and aims to serve as a guideline for efficient communication with clinicians for optimal management of patients with post-treatment imaging.
PMID: 40015559
ISSN: 2005-8330
CID: 5801252
A Radiologist's Guide to IDH-Wildtype Glioblastoma for Efficient Communication With Clinicians: Part I-Essential Information on Preoperative and Immediate Postoperative Imaging
The paradigm of isocitrate dehydrogenase (IDH)-wildtype glioblastoma is rapidly evolving, reflecting clinical, pathological, and imaging advancements. Thus, it remains challenging for radiologists, even those who are dedicated to neuro-oncology imaging, to keep pace with this rapidly progressing field and provide useful and updated information to clinicians. Based on current knowledge, radiologists can play a significant role in managing patients with IDH-wildtype glioblastoma by providing accurate preoperative diagnosis as well as preoperative and postoperative treatment planning including accurate delineation of the residual tumor. Through active communication with clinicians, extending far beyond the confines of the radiology reading room, radiologists can impact clinical decision making. This Part 1 review provides an overview about the neuropathological diagnosis of glioblastoma to understand the past, present, and upcoming revisions of the World Health Organization classification. The imaging findings that are noteworthy for radiologists while communicating with clinicians on preoperative and immediate postoperative imaging of IDH-wildtype glioblastomas will be summarized.
PMCID:11865903
PMID: 39999966
ISSN: 2005-8330
CID: 5800792
Proceedings of SPIE (The International Society for Optical Engineering). 2025:13410.DOI: 10.1117/12.3049148
Data Harmonization with StyleTransfer-GANs: Enhancing Non-Invasive IDH Classification in Brain Tumors
Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in brain gliomas. Accurate non-invasive determination of IDH mutation status is crucial for effective therapy and prognosis. However, the variability in imaging protocols across institutions hinders the reliability of deep learning (DL) models used for IDH classification. To address data heterogeneity, a StyleTransfer-GAN (
PMCID:12588573
PMID: 41200077
ISSN: 0277-786x
CID: 5960302
Proceedings of SPIE (The International Society for Optical Engineering). 2025:13411.DOI: 10.1117/12.3049114
Mitigating Data Scarcity in the Classification of Glioma Molecular Subtypes: The Power of Generative Imaging
Isocitrate dehydrogenase (IDH) mutation status is a critical prognostic indicator in glioma patients. Numerous studies have focused on developing non-invasive methodologies to classify IDH status using pre-operative MRI scans. However, the challenge lies in data scarcity and class imbalance in IDH mutations. This study explores generative AI methods to augment training data and enhance IDH classification accuracy. We developed a 3D conditional latent diffusion model (LDM) for generating 3D multi-contrast brain tumor MRI data (128 × 128 × 64 with a voxel spacing of 1.5 × 1.5 × 2.0 mm) with whole tumor mask and IDH mutation status as conditions. The LDM comprises a 3D autoencoder for perceptual compression and a conditional 3D diffusion model (DM) for generating multi-contrast synthetic samples guided by tumor masks and the IDH mutation status. We incorporated two types of attention modules within the denoising UNet of the LDM to capture the semantic class-dependent data distribution driven by the provided whole tumor mask and IDH status. The LDM was trained using two brain tumor datasets: The Cancer Genome Atlas dataset and an internal dataset from the University of Texas Southwestern Medical Center. The synthetic images generated by the LDM were then used to train IDH classification models, which were subsequently tested on real brain tumor data comprising 327 mutated and 1,394 wild-type cases from the University of California San Francisco Preoperative Diffuse Glioma MRI dataset, the Erasmus Glioma Database, the University of Pennsylvania glioblastoma, and two held-out internal datasets. The IDH classification models, trained on synthetic images and tested on real data, achieved an excellent overall classification accuracy of 94.02%. This approach has the potential to be extended to other molecular markers where data scarcity presents a challenge.
PMCID:12541906
PMID: 41132899
ISSN: 0277-786x
CID: 5957312
A comprehensive multicenter analysis of clinical, molecular, and imaging characteristics and outcomes of H3 K27-altered diffuse midline glioma in adults
OBJECTIVE:The objective was to comprehensively investigate the clinical, molecular, and imaging characteristics and outcomes of H3 K27-altered diffuse midline glioma (DMG) in adults. METHODS:Retrospective chart and imaging reviews were performed in 111 adult patients with H3 K27-altered DMG from two tertiary institutions. Clinical, molecular, imaging, and survival characteristics were analyzed. Characteristics were compared between adult and 365 pediatric patients from a previous multicenter meta-analysis dataset. Cox analyses were performed to determine predictors of overall survival (OS) in adult patients. RESULTS:The median (range) age of adult patients was 40 (18-75) years, and 64 males and 47 females were included. Adults had a higher male proportion (57.7% vs 45.3%, p = 0.023), lower proportion of histological grade 4 (41.4% vs 74.0%, p < 0.001), and different tumor locations (p < 0.001) compared with pediatric patients; adults commonly showed a thalamus location (41.5%) followed by the spinal cord (27.0%), whereas pediatric patients predominantly showed a pons location (64.9%). The OS of adults was longer than that of pediatric patients (30.3 vs 12.0 months, p < 0.001, log-rank test). Older age at diagnosis (HR 0.96, p = 0.001), histologically lower grade (HR 0.25, p = 0.003), and gross-total resection of nonenhancing tumor (HR 0.15, p = 0.003) were independent favorable prognostic factors. CONCLUSIONS:Adult patients with H3 K27-altered DMG showed distinct clinical, histological, and imaging characteristics compared to pediatric counterparts, with a significantly better prognosis. The authors' results suggest that aggressive surgery should be pursued when deemed feasible for better survival outcomes.
PMID: 39793011
ISSN: 1933-0693
CID: 5805342
International journal of radiation oncology biology physics. 2025:121(1):241-249.DOI: 10.1016/j.ijrobp.2024.07.2318
Extended nnU-Net for Brain Metastasis Detection and Segmentation in Contrast-Enhanced Magnetic Resonance Imaging With a Large Multi-Institutional Data Set
PURPOSE/OBJECTIVE:The purpose of this study was to investigate an extended self-adapting nnU-Net framework for detecting and segmenting brain metastases (BM) on magnetic resonance imaging (MRI). METHODS AND MATERIALS/METHODS:Six different nnU-Net systems with adaptive data sampling, adaptive Dice loss, or different patch/batch sizes were trained and tested for detecting and segmenting intraparenchymal BM with a size ≥2 mm on 3 Dimensional (3D) post-Gd T1-weighted MRI volumes using 2092 patients from 7 institutions (1712, 195, and 185 patients for training, validation, and testing, respectively). Gross tumor volumes of BM delineated by physicians for stereotactic radiosurgery were collected retrospectively and curated at each institute. Additional centralized data curation was carried out to create gross tumor volumes of uncontoured BM by 2 radiologists to improve the accuracy of ground truth. The training data set was augmented with synthetic BMs of 1025 MRI volumes using a 3D generative pipeline. BM detection was evaluated by lesion-level sensitivity and false-positive (FP) rate. BM segmentation was assessed by lesion-level Dice similarity coefficient, 95-percentile Hausdorff distance, and average Hausdorff distance (HD). The performances were assessed across different BM sizes. Additional testing was performed using a second data set of 206 patients. RESULTS:. Mean values of Dice similarity coefficient, 95-percentile Hausdorff distance, and average HD of all detected BMs were 0.758, 1.45, and 0.23 mm, respectively. Performances on the second testing data set achieved a sensitivity of 0.907 at an FP rate of 0.57 ± 0.85 for all BM sizes, and an average HD of 0.33 mm for all detected BM. CONCLUSIONS:Our proposed extension of the self-configuring nnU-Net framework substantially improved small BM detection sensitivity while maintaining a controlled FP rate. Clinical utility of the extended nnU-Net model for assisting early BM detection and stereotactic radiosurgery planning will be investigated.
PMID: 39059508
ISSN: 1879-355x
CID: 5696192
Bridging the clinical gap: Confidence informed IDH prediction in brain gliomas using MRI and deep learning
BACKGROUND/UNASSIGNED:The isocitrate dehydrogenase (IDH) mutation status is a key molecular marker in diagnosing and treating brain tumors. Currently, it is determined via invasive tissue biopsy. Recent advances in deep learning (DL) have offered promising non-invasive alternatives for determining IDH status. However, their clinical translation is hindered by a significant gap between DL predictions and their clinical applicability. The limited transparency of many DL-networks and inadequate evaluation metrics hinders trust and adoption, as clinicians require clear and validated insights for determining IDH status. These challenges highlight the need for robust validation and measures of predictive reliability to make DL-predictions clinically actionable. METHODS/UNASSIGNED:We developed a unique approach for non-invasive prediction of IDH status using MRI. We combine a voxel-wise-segmentation network(MC-net) with Bayesian logistic regression (BLR) to provide an IDH status and estimate confidence scores. We utilized a comprehensive dataset of 2,481 glioma cases from eight institutions. RESULTS/UNASSIGNED:Our framework(MC-net + BLR) demonstrated robust performance achieving 96.4% and 95.1% classification accuracies on diverse databases, with an AUC of 0.98. The BLR was implemented exclusively on held-out test data, ensuring that the derived confidence scores are independent of the training or validation phases. The derived confidence scores showed a low Brier score of 0.0125, highlighting its superior calibration and uncertainty quantification. CONCLUSION/UNASSIGNED:The developed framework provides an IDH status and a confidence score, offering clinicians an additional layer of assurance in prediction reliability. It bridges the gap between high-performing DL models and their clinical applicability by addressing the challenges in prediction reliability. Our framework is a significant advancement in non-invasive determination of IDH-status and confidence-informed therapeutic decision-making in neuro-oncology.
PMCID:12365901
PMID: 40842645
ISSN: 2632-2498
CID: 5909342
Correction: Revisiting gliomatosis cerebri in adult-type diffuse gliomas: a comprehensive imaging, genomic and clinical analysis
PMID: 39501387
ISSN: 2051-5960
CID: 5766772
Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements
The development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after therapy, and differentiation of true disease progression from treatment-related changes, which is a considerable challenge based on current clinical care in neuro-oncology. Furthermore, promising future directions, including the use of AI for automated response assessment in neuro-oncology, are discussed.
PMID: 39481414
ISSN: 1474-5488
CID: 5747322
Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice
Technological advancements have enabled the extended investigation, development, and application of computational approaches in various domains, including health care. A burgeoning number of diagnostic, predictive, prognostic, and monitoring biomarkers are continuously being explored to improve clinical decision making in neuro-oncology. These advancements describe the increasing incorporation of artificial intelligence (AI) algorithms, including the use of radiomics. However, the broad applicability and clinical translation of AI are restricted by concerns about generalisability, reproducibility, scalability, and validation. This Policy Review intends to serve as the leading resource of recommendations for the standardisation and good clinical practice of AI approaches in health care, particularly in neuro-oncology. To this end, we investigate the repeatability, reproducibility, and stability of AI in response assessment in neuro-oncology in studies on factors affecting such computational approaches, and in publicly available open-source data and computational software tools facilitating these goals. The pathway for standardisation and validation of these approaches is discussed with the view of trustworthy AI enabling the next generation of clinical trials. We conclude with an outlook on the future of AI-enabled neuro-oncology.
PMID: 39481415
ISSN: 1474-5488
CID: 5747332
