Lung Cancer

Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center.

BACKGROUND: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer variability are main issues in manual OAR delineation, mainly in the head and neck (H & N) district. Deep-learning based auto-segmentation is a promising strategy to improve OARs contouring in radiotherapy departments. A comparison of deep-learning-gen...

A deep image-to-image network organ segmentation algorithm for radiation treatment planning: principles and evaluation.

BACKGROUND: We describe and evaluate a deep network algorithm which automatically contours organs at risk in the thorax and pelvis on computed tomography (CT) images for radiation treatment planning.

The evaluation of the reduction of radiation dose via deep learning-based reconstruction for cadaveric human lung CT images.

To compare the quality of CT images of the lung reconstructed using deep learning-based reconstruction (True Fidelity Image: TFI ™; GE Healthcare) to ...

Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners.

PURPOSE: Attenuation correction is a critically important step in data correction in positron emission tomography (PET) image formation. The current s...

Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.

The use of deep learning (DL) to improve cone-beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistica...

Emphysema Quantification Using Ultra-Low-Dose Chest CT: Efficacy of Deep Learning-Based Image Reconstruction.

Background and Objectives: Although reducing the radiation dose level is important during diagnostic computed tomography (CT) applications, effective ...

Digital skills of therapeutic radiographers/radiation therapists - Document analysis for a European educational curriculum.

INTRODUCTION: It is estimated that around 50% of cancer patients require Radiotherapy (RT) at some point during their treatment, hence Therapeutic Rad...

The Effect of Salvage Radiation Therapy on Survival, Functional Outcomes, and Quality of Life in Men with Persistent Prostate-specific Antigen After Robot-Assisted Radical Prostatectomy: Which Patient Benefits More?

PURPOSE: The aim of this study was to evaluate the effect of salvage radiation therapy (sRT) on survival, functional outcomes, and quality of life in ...

A deep learning and Monte Carlo based framework for bioluminescence imaging center of mass-guided glioblastoma targeting.

Bioluminescence imaging (BLI) is a valuable tool for non-invasive monitoring of glioblastoma multiforme (GBM) tumor-bearing small animals without incu...

Histopathologic Basis for a Chest CT Deep Learning Survival Prediction Model in Patients with Lung Adenocarcinoma.

Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung a...

Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Patterns in Lung Adenocarcinomas on Computed Tomography.

BACKGROUND: High-grade adenocarcinoma subtypes (micropapillary and solid) treated with sublobar resection have an unfavorable prognosis compared with ...

Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT.

PURPOSE: Although surgery is the primary treatment for lung cancer, some patients experience recurrence at a certain rate. If postoperative recurrence...

Robotic right colectomy with robotic-sewn anastomosis: a pilot case series.

The background of this study is to assess the feasibility, clinical utility and safety of intra-corporeal robotic-sewn anastomosis (ICrA) in completel...

Can deep learning improve image quality of low-dose CT: a prospective study in interstitial lung disease.

OBJECTIVES: To investigate whether deep learning reconstruction (DLR) could keep image quality and reduce radiation dose in interstitial lung disease ...

A new deep learning technique reveals the exclusive functional contributions of individual cancer mutations.

Cancers are caused by genomic alterations that may be inherited, induced by environmental carcinogens, or caused due to random replication errors. Pos...

Eliminating CT radiation for clinical PET examination using deep learning.

Clinical PET/CT examinations rely on CT modality for anatomical localization and attenuation correction of the PET data. However, the use of CT signif...

Accuracy of two deep learning-based reconstruction methods compared with an adaptive statistical iterative reconstruction method for solid and ground-glass nodule volumetry on low-dose and ultra-low-dose chest computed tomography: A phantom study.

No published studies have evaluated the accuracy of volumetric measurement of solid nodules and ground-glass nodules on low-dose or ultra-low-dose che...

A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area.

We present a protocol which implements deep learning-based identification of the lung adenocarcinoma category with high accuracy and generalizability,...

Deep learning based analysis of microstructured materials for thermal radiation control.

Microstructured materials that can selectively control the optical properties are crucial for the development of thermal management systems in aerospa...

Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses.

BACKGROUND : There are several types of pancreatic mass, so it is important to distinguish between them before treatment. Artificial intelligence (AI)...