Chunhao Wang

Investigating the impact of cognitive load on drivers using virtual environments and EEG testing

Chunhao Wang, Dezhong Ma, Xiaotong Xiong

The aim of this paper is to investigate the effects that virtual environments and EEG(Electroencephalography) tests have on drivers' cognitive load. Based on the designed virtual driving scenarios, subjects were arranged to complete driving tasks of different difficulty levels. A 16-channel wearable …

cn (code pays fourni par la source)

Identifying predictive factors for radiation necrosis vs local recurrence in biopsy-proven enlarging lesions post-stereotactic radiosurgery for brain metastases

Aden Haskell-Mendoza, Ellery Reason, Joshua Jackson, Chunhao Wang et autres

Background: Stereotactic radiosurgery (SRS) remains the standard of care for brain metastases (BM), offering effective local control with fewer neurocognitive side effects. However, distinguishing radiation necrosis (RN) from local recurrence (LR) is challenging in lesions that enlarge post-SRS. Identifying clinical and treatment-related …

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RADT-49. AGE, INTERVAL FROM SRS TO BIOPSY, AND V12GY AS KEY FACTORS FOR PREDICTING RADIATION NECROSIS VERSUS LOCAL RECURRENCE IN PROGRESSIVE LESIONS FOLLOWING STEREOTACTIC RADIOSURGERY FOR BRAIN METASTASES

Ariel González, Aden Haskell-Mendoza, Ellery Reason, Joshua Jackson et autres

Abstract BACKGROUND Stereotactic radiosurgery (SRS) remains the standard of care for brain metastases (BM), offering effective local control with fewer neurocognitive side effects. However, distinguishing radiation necrosis (RN) from local recurrence (LR) is challenging in lesions that enlarge post-SRS. Identifying clinical and …

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Virtual radiotherapy plan quality education: Perspectives from a global setting

Caroline M. Colbert, D Jacqmin, Jose C. Pichardo, Chunhao Wang et autres

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Development and application of a novel tumor habitat analysis technique based on dynamical modeling

Jack B. Stevens, Jihyeon Je, Breylon A. Riley, Yvonne M. Mowery et autres

BACKGROUND: Oropharyngeal cancer (OPC) exhibits varying responses to chemoradiation therapy, making treatment outcome prediction challenging. Traditional imaging-based methods often fail to capture the spatial heterogeneity within tumors, which influences treatment resistance and disease progression. Advances in modeling techniques allow for more nuanced …

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A Dual Radiomic and Dosiomic Filtering Technique for Locoregional Radiation Pneumonitis Prediction in Breast Cancer Patients

Zhenyu Yang, Qian Chen, Rihui Zhang, F. Guo et autres

Purpose: Radiation pneumonitis (RP) is a serious complication of intensity-modulated radiation therapy (IMRT) for breast cancer patients, underscoring the need for precise and explainable predictive models. This study presents an Explainable Dual-Omics Filtering (EDOF) model that integrates spatially localized dosiomic and radiomic …

RDTA-19 IDENTIFYING PREDICTIVE FACTORS FOR LOCAL RECURRENCE VS. RADIATION NECROSIS IN BIOPSY-PROVEN PROGRESSIVE LESIONS AFTER STEREOTACTIC RADIOSURGERY FOR BRAIN METASTASES

Ariel González, Aden Haskell-Mendoza, Ellery Reason, Joshua Jackson et autres

Abstract BACKGROUND Stereotactic radiosurgery (SRS) remains the standard of care for the initial management of brain metastases (BM), offering excellent local control with fewer neurocognitive side effects reported. However, when radiographic progression occurs post-SRS, distinguishing radiation necrosis (RN) from local recurrence (LR) …

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A Voxel-Wise Uncertainty-Guided Framework for Glioma Segmentation Using Spherical Projection-Based U-Net and Localized Refinement in Multi-Parametric MRI

Zhenyu Yang, Chen Yang, Rihui Zhang, Min‐Bin Chen et autres

Purpose: Accurate segmentation of glioma subregions in multi-parametric MRI (MP-MRI) is essential for diagnosis and treatment planning but remains challenging due to tumor heterogeneity and ambiguous boundaries. This study proposes an uncertainty-guided hybrid framework integrating spherical projection-based 2D modeling with targeted 3D …

A Radiogenomic Deep Ensemble Learning Model for Identifying Radionecrosis Following Brain Metastases (BM) Stereotactic Radiosurgery in Patients With Non-small Cell Lung Cancer BM

Jingtong Zhao, Eugene Vaios, Evan Calabrese, Zhenyu Yang et autres

Purpose Stereotactic radiosurgery (SRS) is widely used for brain metastases (BM), but the risk of radionecrosis poses a challenge in post-SRS management. Given the lack of noninvasive imaging methods for distinguishing radionecrosis from recurrence, we aimed to design a deep ensemble learning …

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BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023

Anahita Fathi Kazerooni, Xinyang Liu, Debanjan Haldar, Zhifan Jiang et autres

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and …

us, gb, in, ca, de, dk, ch (code pays fourni par la source)

Insights of an AI agent via analysis of prediction errors: a case study of fluence map prediction for radiation therapy planning

Chunhao Wang, F Yin, Hunter Stephens, Qiuwen Wu et autres

Purpose. We have previously reported an artificial intelligence (AI) agent that automatically generates intensity-modulated radiation therapy (IMRT) plans via fluence map prediction, by-passing inverse planning. This AI agent achieved clinically comparable quality for prostate cases, but its performance on head-and-neck patients leaves …

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Embedding Radiomics into Vision Transformers for Multimodal Medical Image Classification

Zhenyu Yang, Haiming Zhu, Rihui Zhang, Haipeng Zhang et autres

Background: Deep learning has significantly advanced medical image analysis, with Vision Transformers (ViTs) offering a powerful alternative to convolutional models by modeling long-range dependencies through self-attention. However, ViTs are inherently data-intensive and lack domain-specific inductive biases, limiting their applicability in medical imaging. …