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Chunhao Wang

Assistant Professor of Radiation Oncology
Radiation Oncology
04207 Red Zone, Morris Bldg, Duke South, DUMC, Durham, NC 27710

Selected Publications


Radiomics on spatial-temporal manifolds via Fokker-Planck dynamics.

Conference Med Phys · May 2024 BACKGROUND: Delta radiomics is a high-throughput computational technique used to describe quantitative changes in serial, time-series imaging by considering the relative change in radiomic features of images extracted at two distinct time points. Recent wo ... Full text Link to item Cite

Lung Cancer Screening in Clinical Practice: A 5-Year Review of Frequency and Predictors of Lung Cancer in the Screened Population.

Journal Article J Am Coll Radiol · May 2024 PURPOSE: The aims of this study were to evaluate (1) frequency, type, and lung cancer stage in a clinical lung cancer screening (LCS) population and (2) the association between patient characteristics and Lung CT Screening Reporting & Data System (Lung-RAD ... Full text Link to item Cite

Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.

Journal Article Int J Radiat Oncol Biol Phys · April 12, 2024 PURPOSE: To develop a novel deep ensemble learning model for accurate prediction of brain metastasis (BM) local control outcomes after stereotactic radiosurgery (SRS). METHODS AND MATERIALS: A total of 114 brain metastases (BMs) from 82 patients were evalu ... Full text Link to item Cite

Long-Term Intracranial Outcomes With Combination Dual Immune-Checkpoint Blockade and Stereotactic Radiosurgery in Patients With Melanoma and Non-Small Cell Lung Cancer Brain Metastases.

Journal Article Int J Radiat Oncol Biol Phys · April 1, 2024 PURPOSE: The intracranial benefit of offering dual immune-checkpoint inhibition (D-ICPI) with ipilimumab and nivolumab to patients with melanoma or non-small cell lung cancer (NSCLC) receiving stereotactic radiosurgery (SRS) for brain metastases (BMs) is u ... Full text Link to item Cite

Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection.

Journal Article Med Phys · March 2024 BACKGROUND: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In pot ... Full text Link to item Cite

Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma.

Journal Article J Med Imaging (Bellingham) · March 2024 PURPOSE: We aim to interrogate the role of positron emission tomography (PET) image discretization parameters on the prognostic value of radiomic features in patients with oropharyngeal cancer. APPROACH: A prospective clinical trial (NCT01908504) enrolled ... Full text Link to item Cite

Independent Monte Carlo dose calculation identifies single isocenter multi-target radiosurgery targets most likely to fail pre-treatment measurement.

Journal Article J Appl Clin Med Phys · January 30, 2024 PURPOSE: For individual targets of single isocenter multi-target (SIMT) Stereotactic radiosurgery (SRS), we assess dose difference between the treatment planning system (TPS) and independent Monte Carlo (MC), and demonstrate persistence into the pre-treatm ... Full text Link to item Cite

Effects of Ataxia-Telangiectasia Mutated Variants on Radionecrosis and Local Control After Stereotactic Radiation Surgery for Non-Small Cell Lung Cancer Brain Metastases.

Journal Article Adv Radiat Oncol · January 2024 PURPOSE: Genetic variants affecting the radiation response protein ataxia-telangiectasia mutated (ATM) have been associated with increased adverse effects of radiation but also with improved local control after conventional radiation therapy. However, it i ... Full text Link to item Cite

A radiomics-incorporated deep ensemble learning model for multi-parametric MRI-based glioma segmentation.

Journal Article Phys Med Biol · September 13, 2023 Objective.To develop a deep ensemble learning (DEL) model with radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric magnetic resonance imaging (mp-MRI).Approach.This model was developed using 369 glioma pati ... Full text Link to item Cite

Generation, validation, and benchmarking of a commercial independent Monte Carlo calculation beam model for multi-target SRS.

Journal Article Z Med Phys · September 7, 2023 BACKGROUND: Dosimetric validation of single isocenter multi-target radiosurgery plans is difficult due to conditions of electronic disequilibrium and the simultaneous irradiation of multiple off-axis lesions dispersed throughout the volume. Here we report ... Full text Link to item Cite

A neural ordinary differential equation model for visualizing deep neural network behaviors in multi-parametric MRI-based glioma segmentation.

Conference Medical physics · August 2023 PurposeTo develop a neural ordinary differential equation (ODE) model for visualizing deep neural network behavior during multi-parametric MRI-based glioma segmentation as a method to enhance deep learning explainability.MethodsBy hypothe ... Full text Cite

Development of a multi-feature-combined model: proof-of-concept with application to local failure prediction of post-SBRT or surgery early-stage NSCLC patients.

Journal Article Front Oncol · 2023 OBJECTIVE: To develop a Multi-Feature-Combined (MFC) model for proof-of-concept in predicting local failure (LR) in NSCLC patients after surgery or SBRT using pre-treatment CT images. This MFC model combines handcrafted radiomic features, deep radiomic fea ... Full text Link to item Cite

Quantification of lung function on CT images based on pulmonary radiomic filtering.

Journal Article Med Phys · November 2022 PURPOSE: To develop a radiomics filtering technique for characterizing spatial-encoded regional pulmonary ventilation information on lung computed tomography (CT). METHODS: The lung volume was segmented on 46 CT images, and a 3D sliding window kernel was i ... Full text Link to item Cite

Input feature design and its impact on the performance of deep learning models for predicting fluence maps in intensity-modulated radiation therapy.

Journal Article Phys Med Biol · October 21, 2022 Objective. Deep learning (DL) models for fluence map prediction (FMP) have great potential to reduce treatment planning time in intensity-modulated radiation therapy (IMRT) by avoiding the lengthy inverse optimization process. This study aims to improve th ... Full text Link to item Cite

A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images.

Conference Med Phys · May 2022 PURPOSE: To develop a deep learning model design that integrates radiomics analysis for enhanced performance of COVID-19 and non-COVID-19 pneumonia detection using chest x-ray images. METHODS: As a novel radiomics approach, a 2D sliding kernel was implemen ... Full text Link to item Cite

Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy.

Journal Article Med Phys · April 2022 Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radi ... Full text Link to item Cite

Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.

Journal Article Front Oncol · 2022 PURPOSE: To develop a method of biologically guided deep learning for post-radiation 18FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information. METHODS: Based on the classic reaction-diffusion mechanism, a novel bio ... Full text Link to item Cite

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

Journal Article Phys Med Biol · November 26, 2021 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 ... Full text Link to item Cite

Collect Insights of an H&N IMRT Planning AI Agent Through Analyzing Relationships Between Fluence Map Prediction Error and the Corresponding Dosimetric Impacts.

Conference International journal of radiation oncology, biology, physics · November 2021 Purpose/objective(s)With many deep learning (DL) models being developed for clinical applications, it is important to understand their behavior and clinical consequence. This study aims to collect insights of the relationship between fluence map p ... Full text Cite

Radiomics on spatial-temporal manifolds via Fokker-Planck dynamics.

Conference Med Phys · May 2024 BACKGROUND: Delta radiomics is a high-throughput computational technique used to describe quantitative changes in serial, time-series imaging by considering the relative change in radiomic features of images extracted at two distinct time points. Recent wo ... Full text Link to item Cite

Lung Cancer Screening in Clinical Practice: A 5-Year Review of Frequency and Predictors of Lung Cancer in the Screened Population.

Journal Article J Am Coll Radiol · May 2024 PURPOSE: The aims of this study were to evaluate (1) frequency, type, and lung cancer stage in a clinical lung cancer screening (LCS) population and (2) the association between patient characteristics and Lung CT Screening Reporting & Data System (Lung-RAD ... Full text Link to item Cite

Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.

Journal Article Int J Radiat Oncol Biol Phys · April 12, 2024 PURPOSE: To develop a novel deep ensemble learning model for accurate prediction of brain metastasis (BM) local control outcomes after stereotactic radiosurgery (SRS). METHODS AND MATERIALS: A total of 114 brain metastases (BMs) from 82 patients were evalu ... Full text Link to item Cite

Long-Term Intracranial Outcomes With Combination Dual Immune-Checkpoint Blockade and Stereotactic Radiosurgery in Patients With Melanoma and Non-Small Cell Lung Cancer Brain Metastases.

Journal Article Int J Radiat Oncol Biol Phys · April 1, 2024 PURPOSE: The intracranial benefit of offering dual immune-checkpoint inhibition (D-ICPI) with ipilimumab and nivolumab to patients with melanoma or non-small cell lung cancer (NSCLC) receiving stereotactic radiosurgery (SRS) for brain metastases (BMs) is u ... Full text Link to item Cite

Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection.

Journal Article Med Phys · March 2024 BACKGROUND: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In pot ... Full text Link to item Cite

Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma.

Journal Article J Med Imaging (Bellingham) · March 2024 PURPOSE: We aim to interrogate the role of positron emission tomography (PET) image discretization parameters on the prognostic value of radiomic features in patients with oropharyngeal cancer. APPROACH: A prospective clinical trial (NCT01908504) enrolled ... Full text Link to item Cite

Independent Monte Carlo dose calculation identifies single isocenter multi-target radiosurgery targets most likely to fail pre-treatment measurement.

Journal Article J Appl Clin Med Phys · January 30, 2024 PURPOSE: For individual targets of single isocenter multi-target (SIMT) Stereotactic radiosurgery (SRS), we assess dose difference between the treatment planning system (TPS) and independent Monte Carlo (MC), and demonstrate persistence into the pre-treatm ... Full text Link to item Cite

Effects of Ataxia-Telangiectasia Mutated Variants on Radionecrosis and Local Control After Stereotactic Radiation Surgery for Non-Small Cell Lung Cancer Brain Metastases.

Journal Article Adv Radiat Oncol · January 2024 PURPOSE: Genetic variants affecting the radiation response protein ataxia-telangiectasia mutated (ATM) have been associated with increased adverse effects of radiation but also with improved local control after conventional radiation therapy. However, it i ... Full text Link to item Cite

A radiomics-incorporated deep ensemble learning model for multi-parametric MRI-based glioma segmentation.

Journal Article Phys Med Biol · September 13, 2023 Objective.To develop a deep ensemble learning (DEL) model with radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric magnetic resonance imaging (mp-MRI).Approach.This model was developed using 369 glioma pati ... Full text Link to item Cite

Generation, validation, and benchmarking of a commercial independent Monte Carlo calculation beam model for multi-target SRS.

Journal Article Z Med Phys · September 7, 2023 BACKGROUND: Dosimetric validation of single isocenter multi-target radiosurgery plans is difficult due to conditions of electronic disequilibrium and the simultaneous irradiation of multiple off-axis lesions dispersed throughout the volume. Here we report ... Full text Link to item Cite

A neural ordinary differential equation model for visualizing deep neural network behaviors in multi-parametric MRI-based glioma segmentation.

Conference Medical physics · August 2023 PurposeTo develop a neural ordinary differential equation (ODE) model for visualizing deep neural network behavior during multi-parametric MRI-based glioma segmentation as a method to enhance deep learning explainability.MethodsBy hypothe ... Full text Cite

Development of a multi-feature-combined model: proof-of-concept with application to local failure prediction of post-SBRT or surgery early-stage NSCLC patients.

Journal Article Front Oncol · 2023 OBJECTIVE: To develop a Multi-Feature-Combined (MFC) model for proof-of-concept in predicting local failure (LR) in NSCLC patients after surgery or SBRT using pre-treatment CT images. This MFC model combines handcrafted radiomic features, deep radiomic fea ... Full text Link to item Cite

Quantification of lung function on CT images based on pulmonary radiomic filtering.

Journal Article Med Phys · November 2022 PURPOSE: To develop a radiomics filtering technique for characterizing spatial-encoded regional pulmonary ventilation information on lung computed tomography (CT). METHODS: The lung volume was segmented on 46 CT images, and a 3D sliding window kernel was i ... Full text Link to item Cite

Input feature design and its impact on the performance of deep learning models for predicting fluence maps in intensity-modulated radiation therapy.

Journal Article Phys Med Biol · October 21, 2022 Objective. Deep learning (DL) models for fluence map prediction (FMP) have great potential to reduce treatment planning time in intensity-modulated radiation therapy (IMRT) by avoiding the lengthy inverse optimization process. This study aims to improve th ... Full text Link to item Cite

A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images.

Conference Med Phys · May 2022 PURPOSE: To develop a deep learning model design that integrates radiomics analysis for enhanced performance of COVID-19 and non-COVID-19 pneumonia detection using chest x-ray images. METHODS: As a novel radiomics approach, a 2D sliding kernel was implemen ... Full text Link to item Cite

Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy.

Journal Article Med Phys · April 2022 Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radi ... Full text Link to item Cite

Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.

Journal Article Front Oncol · 2022 PURPOSE: To develop a method of biologically guided deep learning for post-radiation 18FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information. METHODS: Based on the classic reaction-diffusion mechanism, a novel bio ... Full text Link to item Cite

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

Journal Article Phys Med Biol · November 26, 2021 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 ... Full text Link to item Cite

Collect Insights of an H&N IMRT Planning AI Agent Through Analyzing Relationships Between Fluence Map Prediction Error and the Corresponding Dosimetric Impacts.

Conference International journal of radiation oncology, biology, physics · November 2021 Purpose/objective(s)With many deep learning (DL) models being developed for clinical applications, it is important to understand their behavior and clinical consequence. This study aims to collect insights of the relationship between fluence map p ... Full text Cite

A Dosimetric Study Comparing Two Beam Arrangement Strategies in Fractionated Thoracic Spine Stereotactic Body Radiotherapy (SBRT) Planning.

Conference International journal of radiation oncology, biology, physics · November 2021 Purpose/objective(s)To compare dosimetric results of two beam arrangement strategies and their robustness to setup uncertainties in fractionated thoracic spine Stereotactic Body Radiotherapy (SBRT) MATERIALS/METHODS: Fifteen patients who received ... Full text Cite

Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers.

Journal Article Medical physics · July 2021 PurposeThis study investigated the prognostic potential of intra-treatment PET radiomics data in patients undergoing definitive (chemo) radiation therapy for oropharyngeal cancer (OPC) on a prospective clinical trial. We hypothesized that the radi ... Full text Cite

An artificial intelligence-driven agent for real-time head-and-neck IMRT plan generation using conditional generative adversarial network (cGAN).

Journal Article Med Phys · June 2021 PURPOSE: To develop an artificial intelligence (AI) agent for fully automated rapid head-and-neck intensity-modulated radiation therapy (IMRT) plan generation without time-consuming dose-volume-based inverse planning. METHODS: This AI agent was trained via ... Full text Link to item Cite

Radiogenomic Analysis of Locally Advanced Lung Cancer Based on CT Imaging and Intratreatment Changes in Cell-Free DNA.

Journal Article Radiol Imaging Cancer · April 2021 The radiologic appearance of locally advanced lung cancer may be linked to molecular changes of the disease during treatment, but characteristics of this phenomenon are poorly understood. Radiomics, liquid biopsy of cell-free DNA (cfDNA), and next-generati ... Full text Link to item Cite

An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy.

Journal Article Int J Radiat Oncol Biol Phys · March 15, 2021 PURPOSE: Pancreas stereotactic body radiation therapy (SBRT) treatment planning requires planners to make sequential, time-consuming interactions with the treatment planning system to reach the optimal dose distribution. We sought to develop a reinforcemen ... Full text Link to item Cite

RETRACTED: An Encoder-Decoder Based Deep Learning AI agent for Spatial Dose Distribution Prediction: A Study of Complex Head-and-Neck IMRT Application

Conference International journal of radiation oncology, biology, physics · November 1, 2020 This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This abstract has been retracted at the request of the authors. After submitting this abstract the authors realized that th ... Full text Cite

Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data.

Journal Article Quantitative imaging in medicine and surgery · October 2020 BackgroundTo develop a high-efficiency pulmonary nodule computer-aided detection (CAD) method for localization and diameter estimation.MethodsThe developed CAD method centralizes a novel convolutional neural network (CNN) algorithm, You O ... Full text Cite

Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning algorithm for real-time prostate treatment planning.

Journal Article Phys Med Biol · September 8, 2020 The purpose of this work was to develop a deep learning (DL) based algorithm, Automatic intensity-modulated radiotherapy (IMRT) Planning via Static Field Fluence Prediction (AIP-SFFP), for automated prostate IMRT planning with real-time planning efficiency ... Full text Link to item Cite

Knowledge-Based Tradeoff Hyperplanes for Head and Neck Treatment Planning.

Journal Article Int J Radiat Oncol Biol Phys · April 1, 2020 PURPOSE: To develop a tradeoff hyperplane model to facilitate tradeoff decision-making before inverse planning. METHODS AND MATERIALS: We propose a model-based approach to determine the tradeoff hyperplanes that allow physicians to navigate the clinically ... Full text Link to item Cite

Digital phantoms for characterizing inconsistencies among radiomics extraction toolboxes.

Journal Article Biomed Phys Eng Express · March 2, 2020 PURPOSE: to develop digital phantoms for characterizing inconsistencies among radiomics extraction methods based on three radiomics toolboxes: CERR (Computational Environment for Radiological Research), IBEX (imaging biomarker explorer), and an in-house ra ... Full text Link to item Cite

Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application.

Journal Article Front Oncol · 2020 PURPOSE: To develop a deep learning-based AI agent, DDD-PIOP (Dose-Distribution-Driven PET Image Outcome Prediction), for predicting 18FDG-PET image outcomes of oropharyngeal cancer (OPC) in response to intensity-modulated radiation therapy (IMRT). METHODS ... Full text Open Access Link to item Cite

Fluence Map Prediction Using Deep Learning Models - Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy.

Journal Article Front Artif Intell · 2020 Purpose: Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a difficult and time-consuming task. In this study, we aim to develop a novel deep learning framework to generate clinical-quality plans by direct prediction of fluence ... Full text Open Access Link to item Cite

A robust deformable image registration enhancement method based on radial basis function.

Journal Article Quant Imaging Med Surg · July 2019 BACKGROUND: To develop and evaluate a robust deformable image registration (DIR) enhancement method based on radial basis function (RBF) expansion. METHODS: To improve DIR accuracy using sparsely available measured displacements, it is crucial to estimate ... Full text Open Access Link to item Cite

A Spatiotemporal-Constrained Sorting Method for Motion-Robust 4D-MRI: A Feasibility Study.

Journal Article Int J Radiat Oncol Biol Phys · March 1, 2019 PURPOSE: To develop a spatiotemporal-constrained sorting technique for motion-robust 4 dimensional-magnetic resonance imaging. METHODS AND MATERIALS: This sorting method implemented 2 new approaches for 4-dimensional imaging: (1) an optimized sparse k-spac ... Full text Link to item Cite

Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study

Conference Technology in Cancer Research and Treatment · January 1, 2019 Knowledge models in radiotherapy capture the relation between patient anatomy and dosimetry to provide treatment planning guidance. When treatment schemes evolve, existing models struggle to predict accurately. We propose a case-based reasoning framework d ... Full text Cite

Spine SBRT With Halcyon™: Plan Quality, Modulation Complexity, Delivery Accuracy, and Speed.

Journal Article Front Oncol · 2019 Purpose: Spine SBRT requires treatment plans with steep dose gradients and tight limits to the cord maximal dose. A new dual-layer staggered 1-cm MLC in Halcyon™ treatment platform has improved leakage, speed, and DLG compared to 120-Millennium (0.5-cm) an ... Full text Open Access Link to item Cite

Knowledge-Based Statistical Inference Method for Plan Quality Quantification.

Journal Article Technol Cancer Res Treat · January 1, 2019 AIM: The aim of the study is to develop a geometrically adaptive and statistically robust plan quality inference method. METHODS AND MATERIALS: We propose a knowledge-based plan quality inference method that references to similar plans in the historical da ... Full text Open Access Link to item Cite

A Collimator Setting Optimization Algorithm for Dual-Arc Volumetric Modulated Arc Therapy in Pancreas Stereotactic Body Radiation Therapy

Conference Technology in Cancer Research and Treatment · January 1, 2019 Purpose: To optimize collimator setting to improve dosimetric quality of pancreas volumetric modulated arc therapy plan for stereotactic body radiation therapy. Materials and Methods: Fifty-five volumetric modulated arc therapy cases in stereotactic body r ... Full text Cite

A Collimator Setting Optimization Algorithm for Dual-Arc Volumetric Modulated Arc Therapy in Pancreas Stereotactic Body Radiation Therapy.

Journal Article Technol Cancer Res Treat · January 1, 2019 PURPOSE: To optimize collimator setting to improve dosimetric quality of pancreas volumetric modulated arc therapy plan for stereotactic body radiation therapy. MATERIALS AND METHODS: Fifty-five volumetric modulated arc therapy cases in stereotactic body r ... Full text Open Access Link to item Cite

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Journal Article Technol Cancer Res Treat · January 1, 2019 Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal t ... Full text Link to item Cite

Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study.

Journal Article Technol Cancer Res Treat · January 1, 2019 Knowledge models in radiotherapy capture the relation between patient anatomy and dosimetry to provide treatment planning guidance. When treatment schemes evolve, existing models struggle to predict accurately. We propose a case-based reasoning framework d ... Full text Open Access Link to item Cite

An investigation of machine learning methods in delta-radiomics feature analysis.

Journal Article PLoS One · 2019 PURPOSE: This study aimed to investigate the effectiveness of using delta-radiomics to predict overall survival (OS) for patients with recurrent malignant gliomas treated by concurrent stereotactic radiosurgery and bevacizumab, and to investigate the effec ... Full text Open Access Link to item Cite

Spatial-temporal variability of radiomic features and its effect on the classification of lung cancer histology.

Journal Article Phys Med Biol · November 8, 2018 The purpose of this research was to study the sensitivity of Computed Tomography (CT) radiomic features to motion blurring and signal-to-noise ratios (SNR), and investigate its downstream effect regarding the classification of non-small cell lung cancer (N ... Full text Open Access Link to item Cite

A Novel method to generate on-board 4D MRI using prior 4D MRI and on-board kV projections from a conventional LINAC for target localization in liver SBRT.

Journal Article Med Phys · July 2018 PURPOSE: On-board MRI can provide superb soft tissue contrast for improving liver SBRT localization. However, the availability of on-board MRI in clinics is extremely limited. On the contrary, on-board kV imaging systems are widely available on radiotherap ... Full text Link to item Cite

Assessment of concurrent stereotactic radiosurgery and bevacizumab treatment of recurrent malignant gliomas using multi-modality MRI imaging and radiomics analysis.

Journal Article J Radiosurg SBRT · 2018 PURPOSE: To assess the response and predict the overall survival (OS) of recurrent malignant gliomas (MG) patients treated with concurrent BVZ/SRS using multi-modality MRI imaging and radiomics analysis.Methods and materials: SRS was delivered in a single ... Link to item Cite

Accelerating volumetric cine MRI (VC-MRI) using undersampling for real-time 3D target localization/tracking in radiation therapy: a feasibility study.

Journal Article Phys Med Biol · December 14, 2017 PURPOSE: To accelerate volumetric cine MRI (VC-MRI) using undersampled 2D-cine MRI to provide real-time 3D guidance for gating/target tracking in radiotherapy. METHODS: 4D-MRI is acquired during patient simulation. One phase of the prior 4D-MRI is selected ... Full text Link to item Cite

Development of a Computerized 4-D MRI Phantom for Liver Motion Study.

Journal Article Technol Cancer Res Treat · December 2017 PURPOSE: To develop a 4-dimensional computerized magnetic resonance imaging phantom with image textures extracted from real patient scans for liver motion studies. METHODS: The proposed phantom was developed based on the current version of 4-dimensional ex ... Full text Link to item Cite

Accelerated Brain DCE-MRI Using Iterative Reconstruction With Total Generalized Variation Penalty for Quantitative Pharmacokinetic Analysis: A Feasibility Study.

Journal Article Technol Cancer Res Treat · August 2017 PURPOSE: To investigate the feasibility of using undersampled k-space data and an iterative image reconstruction method with total generalized variation penalty in the quantitative pharmacokinetic analysis for clinical brain dynamic contrast-enhanced magne ... Full text Link to item Cite

Assessment of Treatment Response With Diffusion-Weighted MRI and Dynamic Contrast-Enhanced MRI in Patients With Early-Stage Breast Cancer Treated With Single-Dose Preoperative Radiotherapy: Initial Results.

Journal Article Technol Cancer Res Treat · October 2016 Single-dose preoperative stereotactic body radiotherapy is a novel radiotherapy technique for the early-stage breast cancer, and the treatment response pattern of this technique needs to be investigated on a quantitative basis. In this work, dynamic contra ... Full text Link to item Cite

Evaluation of the effect of transcytolemmal water exchange analysis for therapeutic response assessment using DCE-MRI: a comparison study.

Journal Article Phys Med Biol · July 7, 2016 This study compares the shutter-speed (SS) and the Tofts models as used in assessing therapeutic response in a longitudinal DCE-MRI experiment. Sixteen nu/nu mice with implanted colorectal adenocarcinoma cell line (LS-174T) were randomly assigned into trea ... Full text Link to item Cite

Dynamic fractal signature dissimilarity analysis for therapeutic response assessment using dynamic contrast-enhanced MRI.

Journal Article Med Phys · March 2016 PURPOSE: To develop a dynamic fractal signature dissimilarity (FSD) method as a novel image texture analysis technique for the quantification of tumor heterogeneity information for better therapeutic response assessment with dynamic contrast-enhanced (DCE) ... Full text Link to item Cite

An efficient calculation method for pharmacokinetic parameters in brain permeability study using dynamic contrast-enhanced MRI.

Journal Article Magn Reson Med · February 2016 PURPOSE: To develop an efficient method for calculating pharmacokinetic (PK) parameters in brain DCE-MRI permeability studies. METHODS: A linear least-squares fitting algorithm based on a derivative expression of the two-compartment PK model was proposed t ... Full text Link to item Cite

Preoperative Single-Fraction Partial Breast Radiation Therapy: A Novel Phase 1, Dose-Escalation Protocol With Radiation Response Biomarkers.

Journal Article Int J Radiat Oncol Biol Phys · July 15, 2015 PURPOSE: Women with biologically favorable early-stage breast cancer are increasingly treated with accelerated partial breast radiation (PBI). However, treatment-related morbidities have been linked to the large postoperative treatment volumes required for ... Full text Link to item Cite