A5061222665- Radiomics and Machine Learning in Medical Imaging
- Lung Cancer Diagnosis and Treatment
- Advanced Radiotherapy Techniques
- Head and Neck Cancer Studies
- Brain Metastases and Treatment
- Radiation Therapy and Dosimetry
- Glioma Diagnosis and Treatment
- Cutaneous Melanoma Detection and Management
- Nonmelanoma Skin Cancer Studies
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Gastric Cancer Management and Outcomes
- Ultrasound and Hyperthermia Applications
- Artificial Intelligence in Healthcare and Education
- Colorectal and Anal Carcinomas
- Dermatology and Skin Diseases
- Nail Diseases and Treatments
- Allergic Rhinitis and Sensitization
- Contact Dermatitis and Allergies
- Childhood Cancer Survivors' Quality of Life
- Patient-Provider Communication in Healthcare
- Cell Image Analysis Techniques
- Medical Imaging Techniques and Applications
- Meningioma and schwannoma management
- Cutaneous lymphoproliferative disorders research
- Metastasis and carcinoma case studies
Duke Medical Center
2017-2024
Duke University Hospital
2017-2024
Duke University
2017-2023
Durham VA Health Care System
2023
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). Seventy patients who received SBRT stage-1 NSCLC were retrospectively identified. tumor contoured on free-breathing images, which 43 quantitative collectively capture morphology, intensity, fine-texture, coarse-texture. Treatment failure...
Radiation therapy plays an essential role in the treatment of locally advanced lung cancer, but it inevitably leads to incidental and unnecessary dose critical organs, including lung, heart, esophagus. Numerous radiation dose-volumetric parameters have been associated with increased risk morbidity mortality. The purpose present study is quantify differences normal tissue exposure intensity modulated (IMRT) compared 3-dimensional conformal (3D-CRT).Twenty-four consecutive patients cancer...
Objective To develop a Multi-Feature-Combined (MFC) model for proof-of-concept in predicting local failure (LR) NSCLC patients after surgery or SBRT using pre-treatment CT images. This MFC combines handcrafted radiomic features, deep and patient demographic information an integrated machine learning workflow. Methods The comprised three key steps. (1) Extraction of 92 features from the GTV segmented on (2) 512 pre-trained U-Net encoder. (3) extracted along with 4 (i.e., gender, age, tumor...
Objective. Dose calculation in lung stereotactic body radiation therapy (SBRT) is challenging due to the low density of lungs and small volumes. Here we assess uncertainties associated with tissue heterogeneities using different dose algorithms quantify potential associations local failure for SBRT.Approach. 164 SBRT plans were used. The original prepared Pencil Beam Convolution (PBC, n = 8) or Anisotropic Analytical Algorithm (AAA, 156). Each plan was recalculated AcurosXB (AXB) leaving all...
Our purpose was to describe preliminary dosimetric and clinical results of a recumbent total skin electron beam therapy (TSEBT) technique compare this conventional standing TSEBT technique.A customized treatment platform with recessed side wheels constructed commissioned for patients be treated in position. Dosimetric information collected new addition that cohort contemporaneously using the method. Dose delivery outcomes were compared techniques.Between 2017 2019, 27 (n = 13) or 14) at our...