Scholars@Duke
Kyle Jon Lafata

Kyle Jon Lafata

Thaddeus V. Samulski Associate Professor of Radiation Oncology
Radiation Oncology
kyle.lafata@duke.edu
Radiation Physics, Box 3295 DUMC, Durham, NC 27710
Radiation Physics, Box 3295 DUMC, Durham, NC 27710

Selected Publications

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

Journal Article Int J Radiat Oncol Biol Phys · October 1, 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

Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics

Journal Article Biocybernetics and Biomedical Engineering · July 1, 2024 For individuals with Type-1 diabetes mellitus, accurate prediction of future blood glucose values is crucial to aid its regulation with insulin administration, tailored to the individual's specific needs. The authors propose a novel approach for the integr ... 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

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

Artificial intelligence across oncology specialties: Current applications and emerging tools

Journal Article BMJ Oncology · January 17, 2024 Oncology is becoming increasingly personalised through advancements in precision in diagnostics and therapeutics, with more and more data available on both ends to create individualised plans. The depth and breadth of data are outpacing our natural ability ... Full text Cite

Virtual NLST: Towards Replicating National Lung Screening Trial

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2024 Virtual Imaging Trials, known as VITs, provide a computational substitute for clinical trials. These traditional trials tend to be sluggish, costly, and frequently deficient in definitive evidence, all the while subjecting participants to ionizing radiatio ... Full text Cite

Inference Serving System for Stable Diffusion as a Service

Conference Proceeding - 2024 IEEE Cloud Summit, Cloud Summit 2024 · January 1, 2024 We present a model-less, privacy-preserving, low-latency inference framework to satisfy user-defined System-Level Objectives (SLO) for Stable Diffusion as a Service (SDaaS). Developers of Stable Diffusion (SD) models register their trained models on our pr ... Full text Cite

Long-term, automated stool monitoring using a novel smart toilet: A feasibility study

Journal Article Neurogastroenterology and Motility · January 1, 2024 Background: Patients' report of bowel movement consistency is unreliable. We demonstrate the feasibility of long-term automated stool image data collection using a novel Smart Toilet and evaluate a deterministic computer-vision analytic approach to assess ... Full text Cite

Digital staining in optical microscopy using deep learning - a review

Journal Article PhotoniX · December 1, 2023 Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology. Despite this role as gold-standard, staining pro ... Full text Cite

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

Conference Med Phys · August 2023 PURPOSE: To 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. METHODS: By hypothesizing that d ... Full text Link to item Cite

A Faster Prostate MRI: Comparing a Novel Denoised, Single-Average T2 Sequence to the Conventional Multiaverage T2 Sequence Regarding Lesion Detection and PI-RADS Score Assessment.

Journal Article J Magn Reson Imaging · August 2023 BACKGROUND: The T2 w sequence is a standard component of a prostate MRI examination; however, it is time-consuming, requiring multiple signal averages to achieve acceptable image quality. PURPOSE/HYPOTHESIS: To determine whether a denoised, single-average ... Full text Link to item Cite

Exploratory analysis of mesenteric-portal axis CT radiomic features for survival prediction of patients with pancreatic ductal adenocarcinoma.

Journal Article Eur Radiol · August 2023 OBJECTIVE: To develop and evaluate task-based radiomic features extracted from the mesenteric-portal axis for prediction of survival and response to neoadjuvant therapy in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: Consecutive patients ... Full text Link to item Cite

Towards optimal deep fusion of imaging and clinical data via a model-based description of fusion quality.

Journal Article Med Phys · June 2023 BACKGROUND: Due to intrinsic differences in data formatting, data structure, and underlying semantic information, the integration of imaging data with clinical data can be non-trivial. Optimal integration requires robust data fusion, that is, the process o ... Full text Link to item Cite

Systematic Analysis of Common Factors Impacting Deep Learning Model Generalizability in Liver Segmentation.

Journal Article Radiol Artif Intell · May 2023 PURPOSE: To investigate the effect of training data type on generalizability of deep learning liver segmentation models. MATERIALS AND METHODS: This Health Insurance Portability and Accountability Act-compliant retrospective study included 860 MRI and CT a ... Full text Link to item Cite

Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies.

Journal Article Kidney360 · May 1, 2023 KEY POINTS: Computational image analysis allows for the extraction of new information from whole-slide images with potential clinical relevance. Peritubular capillary (PTC) density is decreased in areas of interstitial fibrosis and tubular atrophy when mea ... Full text Link to item Cite

Comparing Survival Outcomes of Patients With LI-RADS-M Hepatocellular Carcinomas and Intrahepatic Cholangiocarcinomas.

Journal Article J Magn Reson Imaging · January 2023 BACKGROUND: There is a sparsity of data evaluating outcomes of patients with Liver Imaging Reporting and Data System (LI-RADS) (LR)-M lesions. PURPOSE: To compare overall survival (OS) and progression free survival (PFS) between hepatocellular carcinoma (H ... Full text Link to item Cite

CT-derived body composition measurements as predictors for neoadjuvant treatment tolerance and survival in gastroesophageal adenocarcinoma.

Journal Article Abdom Radiol (NY) · January 2023 PURPOSE: Treatment for gastroesophageal adenocarcinomas can result in significant morbidity and mortality. The purpose of this study is to supplement methods for choosing treatment strategy by assessing the relationship between CT-derived body composition, ... Full text Link to item Cite

Decoding the Encoder

Conference Conference Proceedings - IEEE SOUTHEASTCON · January 1, 2023 Autoencoders are used in a variety of safety-critical applications. Uncertainty quantification is a key component to bolster the trustworthiness of such models. With the growing complexity of the autoencoder design and the dataset they are trained on, ther ... Full text Cite

Automatic quality control in computed tomography volumes segmentation using a small set of XCAT as reference images

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2023 Deep learning methods have performed superiorly to segment organs of interest from Computed Tomography images than traditional methods. However, the trained models do not generalize well at the inference phase, and manual validation and correction are not ... Full text Cite

Privacy-preserving Job Scheduler for GPU Sharing

Conference Proceedings - 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing Workshops, CCGridW 2023 · January 1, 2023 Machine learning (ML) training jobs are resource intensive. High infrastructure costs of computing clusters encourage multi-tenancy in GPU resources. This invites a scheduling problem in assigning multiple ML training jobs on a single GPU while minimizing ... Full text Cite

Job Recommendation Service for GPU Sharing in Kubernetes

Conference Proceedings - 2023 IEEE Cloud Summit, Cloud Summit 2023 · January 1, 2023 Cloud infrastructures encourage the multi-tenancy of hardware resources. User-defined Machine Learning (ML) training jobs are offloaded to the cloud for efficient training. State-of-the-art resource schedulers do not preserve user privacy by accessing sens ... 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

Blood Glucose Prediction for Type-1 Diabetics using Deep Reinforcement Learning

Conference Proceedings - 2023 IEEE International Conference on Digital Health, ICDH 2023 · January 1, 2023 An accurate prediction of blood glucose levels for individuals affected with type-1 diabetes mellitus helps to regulate blood glucose through specific insulin delivery. In our work, we propose the design of a densely-connected encoder-decoder network in co ... Full text 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

Prognostic Model for Intracranial Progression after Stereotactic Radiosurgery: A Multicenter Validation Study.

Journal Article Cancers (Basel) · October 22, 2022 Stereotactic radiosurgery (SRS) is a standard of care for many patients with brain metastases. To optimize post-SRS surveillance, this study aimed to validate a previously published nomogram predicting post-SRS intracranial progression (IP). We identified ... Full text Link to item Cite

Radiomics: a primer on high-throughput image phenotyping.

Journal Article Abdom Radiol (NY) · September 2022 Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve ... Full text Link to item Cite

Modifying LI-RADS on Gadoxetate Disodium-Enhanced MRI: A Secondary Analysis of a Prospective Observational Study.

Journal Article J Magn Reson Imaging · August 2022 BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS) is widely used for diagnosing hepatocellular carcinoma (HCC), however, with unsatisfactory sensitivity, complex ancillary features, and inadequate integration with gadoxetate disodium (EOB)- ... Full text Link to item Cite

Can radiomic analysis of a single-phase dual-energy CT improve the diagnostic accuracy of differentiating enhancing from non-enhancing small renal lesions?

Journal Article Acta Radiol · June 2022 BACKGROUND: The value of dual-energy computed tomography (DECT)-based radiomics in renal lesions is unknown. PURPOSE: To develop DECT-based radiomic models and assess their incremental values in comparison to conventional measurements for differentiating e ... 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

Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden.

Journal Article Tomography · March 10, 2022 The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrast-enhancement can differentiate tumors based on lymphocyte burden. High mutational load transplant soft tissue sarcomas were initiated in ... Full text Open Access Link to item Cite

Data-Driven Modification of the LI-RADS Major Feature System on Gadoxetate Disodium-Enhanced MRI: Toward Better Sensitivity and Simplicity.

Journal Article J Magn Reson Imaging · February 2022 BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS) is widely accepted as a reliable diagnostic scheme for hepatocellular carcinoma (HCC) in at-risk patients. However, its application is hampered by substantial complexity and suboptimal diagn ... Full text Link to item Cite

The Role of Machine Learning in Cardiovascular Pathology.

Journal Article Can J Cardiol · February 2022 Machine learning has seen slow but steady uptake in diagnostic pathology over the past decade to assess digital whole-slide images. Machine learning tools have incredible potential to standardise, and likely even improve, histopathologic diagnoses, but the ... 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

Spectral micro-CT and nanoradiomic analysis for classification of tumors based on lymphocytic burden in cancer therapy studies

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2022 The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrast-enhancement can differentiate tumors based on tumor-infiltrating lymphocyte (TIL) burden. High mutational load transplant soft tissue sa ... Full text Cite

CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study.

Journal Article Radiology · December 2021 Background Current imaging methods for prediction of complete margin resection (R0) in patients with pancreatic ductal adenocarcinoma (PDAC) are not reliable. Purpose To investigate whether tumor-related and perivascular CT radiomic features improve preope ... Full text Open Access Link to item Cite

Deep learning segmentation of glomeruli on kidney donor frozen sections.

Journal Article J Med Imaging (Bellingham) · November 2021 Purpose: Recent advances in computational image analysis offer the opportunity to develop automatic quantification of histologic parameters as aid tools for practicing pathologists. We aim to develop deep learning (DL) models to quantify nonsclerotic and s ... Full text Link to item Cite

Week 4 Liver Fat Reduction on MRI as an Early Predictor of Treatment Response in Participants with Nonalcoholic Steatohepatitis.

Journal Article Radiology · August 2021 Background Pharmacologic treatment of nonalcoholic steatohepatitis (NASH) is long term in nature; thus, early noninvasive treatment response assessment is important for therapeutic decision making. Purpose To investigate potential early predictors of the 1 ... Full text Link to item Cite

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

Journal Article Med Phys · July 2021 PURPOSE: This 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 radiomic ex ... Full text Link to item Cite

A generative adversarial network (GAN)-based technique for synthesizing realistic respiratory motion in the extended cardiac-torso (XCAT) phantoms.

Journal Article Phys Med Biol · May 31, 2021 Objective. Synthesize realistic and controllable respiratory motions in the extended cardiac-torso (XCAT) phantoms by developing a generative adversarial network (GAN)-based deep learning technique.Methods. A motion generation model was developed using bic ... 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

Digital pathology and computational image analysis in nephropathology.

Journal Article Nat Rev Nephrol · November 2020 The emergence of digital pathology - an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis - is changing the pathology ecosystem. In par ... Full text Link to item Cite

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

Journal Article Quant Imaging Med Surg · October 2020 BACKGROUND: To develop a high-efficiency pulmonary nodule computer-aided detection (CAD) method for localization and diameter estimation. METHODS: The developed CAD method centralizes a novel convolutional neural network (CNN) algorithm, You Only Look Once ... Full text Link to item Cite

Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN).

Journal Article Phys Med Biol · March 19, 2020 Develop a machine learning-based method to generate multi-contrast anatomical textures in the 4D extended cardiac-torso (XCAT) phantom for more realistic imaging simulations. As a pilot study, we synthesize CT and CBCT textures in the chest region. For tra ... 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

Conference Frontiers in oncology · January 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 ... 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

Identification of Radiomic Biomarkers for Patients with Locally Advanced Lung Cancer

Conference International Journal of Radiation Oncology*Biology*Physics · September 2019 Full text Cite

An Exploratory Radiomics Approach to Quantifying Pulmonary Function in CT Images.

Journal Article Sci Rep · August 8, 2019 Contemporary medical imaging is becoming increasingly more quantitative. The emerging field of radiomics is a leading example. By translating unstructured data (i.e., images) into structured data (i.e., imaging features), radiomics can potentially characte ... Full text Open Access Link to item Cite

Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy.

Journal Article Phys Med Biol · January 8, 2019 The purpose of this work was to investigate the potential relationship between radiomic features extracted from pre-treatment x-ray CT images and clinical outcomes following stereotactic body radiation therapy (SBRT) for non-small-cell lung cancer (NSCLC). ... Full text Open Access Link to item Cite

Data clustering based on Langevin annealing with a self-consistent potential

Journal Article Quarterly of Applied Mathematics · January 1, 2019 This paper introduces a novel data clustering algorithm based on Langevin dynamics, where the associated potential is constructed directly from the data. To introduce a self-consistent potential, we adopt the potential model from the established Quantum Cl ... Full text Open Access Cite

Dynamic Changes in Circulating Tumor DNA During Chemoradiation for Locally Advanced Lung Cancer.

Journal Article Adv Radiat Oncol · 2019 PURPOSE: Concurrent chemoradiation therapy (CRT) is the principal treatment modality for locally advanced lung cancer. Cell death due to CRT leads to the release of cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA) into the bloodstream, but the kinet ... Full text 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

Stereotactic body radiation therapy versus sublobar resection for stage I NSCLC.

Journal Article Lung Cancer · November 2018 PURPOSE: To compare sublobar resection and stereotactic body radiation therapy (SBRT) in patients with stage I non-small cell lung cancer (NSCLC). METHODS: Patients undergoing sublobar resection or SBRT for stage I NSCLC from 2007 to 2014 at Duke Universit ... Full text Link to item Cite

Sensitivity of Radiomic Features to Acquisition Noise and Respiratory Motion

Conference International Journal of Radiation Oncology*Biology*Physics · October 2017 Full text Cite

Effects of Motion on Radiomics Analysis of Thoracic Cancers.

Journal Article Int J Radiat Oncol Biol Phys · May 1, 2017 Full text Link to item Cite

SU-D-204-01: A Methodology Based On Machine Learning and Quantum Clustering to Predict Lung SBRT Dosimetric Endpoints From Patient Specific Anatomic Features.

Conference Med Phys · June 2016 PURPOSE: To develop a data-mining methodology based on quantum clustering and machine learning to predict expected dosimetric endpoints for lung SBRT applications based on patient-specific anatomic features. METHODS: Ninety-three patients who received lung ... Full text Link to item Cite

SU-G-JeP3-01: A Method to Quantify Lung SBRT Target Localization Accuracy Based On Digitally Reconstructed Fluoroscopy.

Conference Med Phys · June 2016 PURPOSE: To develop a methodology based on digitally-reconstructed-fluoroscopy (DRF) to quantitatively assess target localization accuracy of lung SBRT, and to evaluate using both a dynamic digital phantom and a patient dataset. METHODS: For each treatment ... Full text Link to item Cite

SU-F-T-10: Validation of ELP Dosimetry Using PRESAGE Dosimeter: Feasibility Test and Practical Considerations.

Conference Med Phys · June 2016 PURPOSE: To validate the use of a PRESAGE dosimeter as a method to quantitatively measure dose distributions of injectable brachytherapy based on elastin-like polypeptide (ELP) nanoparticles. PRESAGE is a solid, transparent polyurethane-based dosimeter who ... Full text Link to item Cite

MO-FG-BRA-01: Development of An Image-Guided Dosimetric Planning System for Injectable Brachytherapy Using ELP Nanoparticles.

Conference Med Phys · June 2015 PURPOSE: To develop, validate, and evaluate a methodology for determining dosimetry for intratumoral injections of elastin-like-polypeptide (ELP) brachytherapy nanoparticles. These organic-polymer-based nanoparticles are injectable, biodegradable, and gene ... Full text Link to item Cite

A simple technique for the generation of institution-specific nomograms for permanent prostate cancer brachytherapy.

Journal Article J Contemp Brachytherapy · October 2014 PURPOSE: Nomograms once had a vital role in prostate brachytherapy practice. Although some of their functions have been assumed by computerized dosimetry, many programs still find them useful to determine the number and strength of seeds to be ordered in a ... Full text Link to item Cite

SU-E-J-192: Verification of 4D-MRI Internal Target Volume Using Cine MRI.

Conference Med Phys · June 2014 PURPOSE: To investigate the accuracy of 4D-MRI in determining the Internal Target Volume (ITV) used in radiation oncology treatment planning of liver cancers. Cine MRI is used as the standard baseline in establishing the feasibility and accuracy of 4D-MRI ... Full text Link to item Cite

Early pulmonary toxicity following lung stereotactic body radiation therapy delivered in consecutive daily fractions.

Journal Article Radiother Oncol · May 2011 BACKGROUND AND PURPOSE: Identify the incidence of early pulmonary toxicity in a cohort of patients treated with lung stereotactic body radiation therapy (SBRT) on consecutive treatment days. MATERIAL AND METHODS: A total of 88 lesions in 84 patients were t ... Full text Link to item Cite