Lung Cancer

Optimization based trajectory planning for real-time 6DoF robotic patient motion compensation systems.

PURPOSE: Robotic stabilization of a therapeutic radiation beam with respect to a dynamically moving tumor target can be accomplished either by moving the radiation source, the patient, or both. As the treatment beam is on during this process, the primary goal is to minimize exposure of normal tissue to radiation as much as possible when moving the target back to the desired position. Due to the co...

Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks.

PURPOSE: To investigate the use and efficiency of 3-D deep learning, fully convolutional networks (DFCN) for simultaneous tumor cosegmentation on dual-modality nonsmall cell lung cancer (NSCLC) and positron emission tomography (PET)-computed tomography (CT) images.

Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer.

OBJECTIVE: To develop different radiomic models based on the magnetic resonance imaging (MRI) radiomic features and machine learning methods to predic...

Design and synthesis of new phthalazine-based derivatives as potential EGFR inhibitors for the treatment of hepatocellular carcinoma.

Searching for new leads in the battle of cancer will never ends, we herein disclose the design and synthesis of new phthalazine derivatives and their ...

Intensity-modulated radiation therapy of anal squamous cell carcinoma: Relationship between delineation quality and regional recurrence.

BACKGROUND AND PURPOSE: Intensity-modulated radiation therapy (IMRT) is currently indicated to treat anal squamous cell carcinoma (ASCC). Conformal do...

Predictors of Nodal and Metastatic Failure in Early Stage Non-small-cell Lung Cancer After Stereotactic Body Radiation Therapy.

INTRODUCTION/BACKGROUND: Many patients with early stage non-small-cell lung cancer (ES-NSCLC) undergoing stereotactic body radiation therapy (SBRT) de...

Automated data extraction and ensemble methods for predictive modeling of breast cancer outcomes after radiation therapy.

PURPOSE: The purpose of this study was to compare the effectiveness of ensemble methods (e.g., random forests) and single-model methods (e.g., logisti...

Cycle-consistent adversarial denoising network for multiphase coronary CT angiography.

PURPOSE: In multiphase coronary CT angiography (CTA), a series of CT images are taken at different levels of radiation dose during the examination. Al...

MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images.

The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the imp...

Ultra-Low-Dose Neck CT With Low-Dose Contrast Material for Preoperative Staging of Thyroid Cancer: Image Quality and Diagnostic Performance.

OBJECTIVE: Although CT has been used as a complementary diagnostic method for the preoperative diagnosis of thyroid cancer, it has the shortcomings of...

Protective effect of polysaccharide from Sophora japonica L. flower buds against UVB radiation in a human keratinocyte cell line (HaCaT cells).

Natured botanical extract has attracted considerable attention recently in the field of skin anti-ultraviolet (UV) radiation. As a medicinal herb, Sop...

Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma.

Robot-assisted intravertebral augmentation corrects local kyphosis more effectively than a conventional fluoroscopy-guided technique.

OBJECTIVEIntravertebral augmentation (IVA) is a reliable minimally invasive technique for treating Magerl type A vertebral body fractures. However, po...

Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study.

BACKGROUND: Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. Thi...

3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis.

Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent...

Machine Learning Methods Uncover Radiomorphologic Dose Patterns in Salivary Glands that Predict Xerostomia in Patients with Head and Neck Cancer.

PURPOSE: Patients with head-and-neck cancer (HNC) may experience xerostomia after radiation therapy (RT), which leads to compromised quality of life. ...

High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in various domains.

PURPOSE: Sparsely sampled computed tomography (CT) has been attracting attention as a technique that can reduce the high radiation dose of conventiona...

Increases in Serial Pretreatment F-FDG PET-CT Metrics Predict Survival in Early Stage Non-Small Cell Lung Cancer Treated With Stereotactic Ablative Radiation Therapy.

PURPOSE: Quantitative changes in positron emission tomography with computed tomography imaging metrics over serial scans may be predictive biomarkers....

Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies.

The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) pat...

Chronic exposure to cadmium is associated with a marked reduction in glomerular filtration rate.

BACKGROUND: Urinary 20-hydroxyeicosatetraenoic acid (20-HETE) has been associated with hypertension in women with elevated urinary cadmium (Cd) excret...