Lung Cancer

Prior information-based high-resolution tomography image reconstruction from a single digitally reconstructed radiograph.

Tomography images are essential for clinical diagnosis and trauma surgery, allowing doctors to understand the internal information of patients in more detail. Since the large amount of x-ray radiation from the continuous imaging during the process of computed tomography scanning can cause serious harm to the human body, reconstructing tomographic images from sparse views becomes a potential soluti...

Robot-assisted thoracic surgery is effective for right upper lobectomy in cases with a rare variation pattern of the pulmonary veins.

A deep learning model (FociRad) for automated detection of γ-H2AX foci and radiation dose estimation.

DNA double-strand breaks (DSBs) are the most lethal form of damage to cells from irradiation. γ-H2AX (phosphorylated form of H2AX histone variant) has...

The Impact of Postoperative Renal Function Recovery after Laparoscopic and Robot-Assisted Partial Nephrectomy in Patients with Renal Cell Carcinoma.

Background and objectives: This study aimed to evaluate the association between warm ischemic time (WIT) and postoperative renal function using Trifec...

Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network.

Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagno...

The Value of Artificial Intelligence Film Reading System Based on Deep Learning in the Diagnosis of Non-Small-Cell Lung Cancer and the Significance of Efficacy Monitoring: A Retrospective, Clinical, Nonrandomized, Controlled Study.

OBJECTIVE: To explore the value of artificial intelligence (AI) film reading system based on deep learning in the diagnosis of non-small-cell lung can...

Learning-based occupational x-ray scatter estimation.

During x-ray-guided interventional procedures, the medical staff is exposed to scattered ionizing radiation caused by the patient. To increase the sta...

A hybrid deep learning model for forecasting lymphocyte depletion during radiation therapy.

PURPOSE: Recent studies have shown that severe depletion of the absolute lymphocyte count (ALC) induced by radiation therapy (RT) has been associated ...

Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma.

PURPOSE: Radiation dermatitis (RD) is a common, unpleasant side effect of patients receiving radiation therapy. In clinical practice, the severity of ...

Head and neck synthetic CT generated from ultra-low-dose cone-beam CT following Image Gently Protocol using deep neural network.

PURPOSE: Image guidance is used to improve the accuracy of radiation therapy delivery but results in increased dose to patients. This is of particular...

Radiation and iodine dose reduced thoraco-abdomino-pelvic dual-energy CT at 40 keV reconstructed with deep learning image reconstruction.

OBJECTIVE: To evaluate the feasibility of a simultaneous reduction of radiation and iodine doses in dual-energy thoraco-abdomino-pelvic CT reconstruct...

Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis.

BACKGROUND: Accurate and non-invasive diagnosis of pancreatic ductal adenocarcinoma (PDAC) and chronic pancreatitis (CP) can avoid unnecessary punctur...

Switching to a NRTI-free 2 drug regimen (2DR) -a sub-analysis of the 48 weeks DUALIS study on metabolic and renal changes.

Switching from a three-drug regimen (3DR: boosted darunavir [bDRV] and two nucleoside reverse transcriptase inhibitors [NRTIs]) to a two-drug regimen...

Is robotic lobectomy cheaper? A micro-cost analysis.

Higher capital costs and operating room costs associated with Lobectomy via Robot Assisted Thoracic Surgery (RATS) have previously been suggested as t...

Accelerate treatment planning process using deep learning generated fluence maps for cervical cancer radiation therapy.

PURPOSE: This study aims to develop a deep learning method that skips the time-consuming inverse optimization process for automatic generation of mach...

Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance, epidemiology, and end results analysis.

BACKGROUND: We collected information on patients with rectal adenocarcinoma in the United States from the Surveillance, Epidemiology, and EndResults (...

Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks.

In radiation oncology, predicting patient risk stratification allows specialization of therapy intensification as well as selecting between systemic a...

Radiation Dose Reduction for 80-kVp Pediatric CT Using Deep Learning-Based Reconstruction: A Clinical and Phantom Study.

Deep learning-based reconstruction (DLR) may facilitate CT radiation dose reduction, but a paucity of literature has compared lower-dose DLR images w...

Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors.

Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging...

Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process.

BACKGROUND: Radiation treatment is considered an effective and the most common treatment option for prostate cancer. The treatment planning process re...