Lung Cancer

Synthetic cranial MRI from 3D optical surface scans using deep learning for radiation therapy treatment planning.

BACKGROUND: Optical scanning technologies are increasingly being utilised to supplement treatment workflows in radiation oncology, such as surface-guided radiotherapy or 3D printing custom bolus. One limitation of optical scanning devices is the absence of internal anatomical information of the patient being scanned. As a result, conventional radiation therapy treatment planning using this imaging...

Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients.

BACKGROUND: Lung cancer is the leading cause of cancer-related deaths worldwide. The majority of lung cancers are non-small cell lung cancer (NSCLC), accounting for approximately 85% of all lung cancer types. The Cox proportional hazards model (CPH), which is the standard method for survival analysis, has several limitations. The purpose of our study was to improve survival prediction in patients ...

Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium.

The era of high-throughput techniques created big data in the medical field and research disciplines. Machine intelligence (MI) approaches can overcom...

Intrathoracic Robotic-Sewn Anastomosis During Ivor Lewis Esophagectomy for Cancer: Back to Basics?

BACKGROUND: To assess the feasibility, clinical utility, and safety of intrathoracic robotic-sewn esophageal anastomosis (IrEA) during Ivor Lewis esop...

Computer-Assisted Diagnosis of Lymph Node Metastases in Colorectal Cancers Using Transfer Learning With an Ensemble Model.

Screening of lymph node metastases in colorectal cancer (CRC) can be a cumbersome task, but it is amenable to artificial intelligence (AI)-assisted di...

An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation.

The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in brea...

Survival prediction for stage I-IIIA non-small cell lung cancer using deep learning.

BACKGROUND AND PURPOSE: The aim of this study was to develop and evaluate a prediction model for 2-year overall survival (OS) in stage I-IIIA non-smal...

A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer.

BACKGROUND: The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a first-line therapy for non-small cell lung cancer (NSC...

Port-Site Metastasis Identified on Prostate-Specific Membrane Antigen-Targeted 18 F-DCFPyL PET/CT After Robot-Assisted Laparoscopic Radical Prostatectomy.

Port-site metastasis is an extremely rare complication following minimally invasive oncologic surgery for prostate cancer. We present the case of a 74...

Identification of Glucose Transport Modulators In Vitro and Method for Their Deep Learning Neural Network Behavioral Evaluation in Glucose Transporter 1-Deficient Mice.

Metabolic flux augmentation via glucose transport activation may be desirable in glucose transporter 1 (Glut1) deficiency syndrome (G1D) and dementia,...

Effectiveness of mobile robots collecting vital signs and radiation dose rate for patients receiving Iodine-131 radiotherapy: A randomized clinical trial.

OBJECTIVE: Patients receiving radionuclide 131I treatment expose radiation to others, and there was no clinical trial to verify the effectiveness and ...

Predicting N2 lymph node metastasis in presurgical stage I-II non-small cell lung cancer using multiview radiomics and deep learning method.

BACKGROUND: Accurate diagnosis of N2 lymph node status of the resectable stage I-II non-small cell lung cancer (NSCLC) before surgery is crucial, whil...

Prostatic urinary tract visualization with super-resolution deep learning models.

In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been...

Comparison of image quality and pancreatic ductal adenocarcinoma conspicuity between the low-kVp and dual-energy CT reconstructed with deep-learning image reconstruction algorithm.

PURPOSE: To compare the image quality and conspicuity of pancreatic ductal adenocarcinoma (PDAC) between the low-kVp and dual-energy pancreatic protoc...

Raman microspectroscopy and machine learning for use in identifying radiation-induced lung toxicity.

OBJECTIVE: In this work, we explore and develop a method that uses Raman spectroscopy to measure and differentiate radiation induced toxicity in murin...

Comparing Thoracoscopic and Robotic Lobectomy Using a Nationally Representative Database.

BACKGROUND: Studies of robotic lobectomy (Robot-L) have been performed using data from high-volume, specialty centers which may not be generalizable. ...

The Development of Symbolic Expressions for Fire Detection with Symbolic Classifier Using Sensor Fusion Data.

Fire is usually detected with fire detection systems that are used to sense one or more products resulting from the fire such as smoke, heat, infrared...

Detection of Colorectal Adenocarcinoma and Grading Dysplasia on Histopathologic Slides Using Deep Learning.

Colorectal cancer (CRC) is one of the most common types of cancer among men and women. The grading of dysplasia and the detection of adenocarcinoma ar...

The learning curve for single-port transaxillary robotic thyroidectomy (SP-TART): experience through initial 50 cases of lobectomy.

The new da Vinci® single-port (SP) robotic system, which utilizes a smaller incision and work space compared to the previous versions, is suitable for...

Iterative Reconstruction: State-of-the-Art and Future Perspectives.

Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while...