Lung Cancer

A deep learning approach to radiation dose estimation.

Currently methods for predicting absorbed dose after administering a radiopharmaceutical are rather ...

Deep DoseNet: a deep neural network for accurate dosimetric transformation between different spatial resolutions and/or different dose calculation algorithms for precision radiation therapy.

The purpose of this work is to introduce a novel deep learning strategy to obtain highly accurate do...

User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions.

PURPOSE: Precision cancer medicine is dependent on accurate prediction of disease and treatment outc...

Machine learning to predict early recurrence after oesophageal cancer surgery.

BACKGROUND: Early cancer recurrence after oesophagectomy is a common problem, with an incidence of 2...

Artificial Intelligence and CT Image Reconstruction: Potential of a New Era in Radiation Dose Reduction.

Overlooked pitfalls in multi-class machine learning classification in radiation oncology and how to avoid them.

In radiation oncology, Machine Learning classification publications are typically related to two out...

Quantitative Thermal Imaging Biomarkers to Detect Acute Skin Toxicity From Breast Radiation Therapy Using Supervised Machine Learning.

PURPOSE: Radiation-induced dermatitis is a common side effect of breast radiation therapy (RT). Curr...

A fast deep learning approach for beam orientation optimization for prostate cancer treated with intensity-modulated radiation therapy.

PURPOSE: Beam orientation selection, whether manual or protocol-based, is the current clinical stand...

Real-time prediction of tumor motion using a dynamic neural network.

Radiation dose delivery into the thoracic and abdomen cavities during radiotherapy treatment is a ch...

Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose-volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy.

PURPOSE: We propose a novel domain-specific loss, which is a differentiable loss function based on t...

Cardiac substructure segmentation with deep learning for improved cardiac sparing.

PURPOSE: Radiation dose to cardiac substructures is related to radiation-induced heart disease. Howe...

Presence of pyrroloquinazoline alkaloid in attenuates inflammatory response through the downregulation of pro-inflammatory mediators in LPS stimulated RAW 264.7 macrophages.

Inflammation is the primary response caused due to harmful stimuli which are followed by the increa...

Discontinuation of Proton Pump Inhibitors in Patients With Chronic Kidney Disease.

Proton pump inhibitors (PPIs) are commonly used medications and are historically well tolerated. Re...

Contrast enhanced oesophageal avoidance for stereotactic body radiotherapy: Barium vs. Gastrografin.

INTRODUCTION: SABR may facilitate treatment in a greater proportion of locally-advanced NSCLC patien...

Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy.

Accurately predicting treatment outcome is crucial for creating personalized treatment plans and fol...

Development of a robot-assisted ultrasound-guided radiation therapy (USgRT).

PURPOSE: Radiation treatment is improved by the use of image-guided workflows. This work pursues the...

Hollow Alginate-Poly-L-Lysine-Alginate Microspheres Promoted an Epithelial-Mesenchymal Transition in Human Colon Adenocarcinoma Cells.

Today, there is an urgent need to develop a three-dimentional culture systems mimicking native con...

Evaluation of complexity and deliverability of prostate cancer treatment plans designed with a knowledge-based VMAT planning technique.

PURPOSE: Knowledge-based planning (KBP) techniques have been reported to improve plan quality, effic...

A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.

OBJECTIVE: To develop a deep learning-based artificial intelligence (AI) scheme for predicting the l...

Predictive models for patients with lung carcinomas to identify EGFR mutation status via an artificial neural network based on multiple clinical information.

PURPOSE: Epidermal growth factor receptor (EGFR) mutation testing has several limitations. Therefore...

Twin Robotic X-Ray System for 3D Cone-Beam CT of the Wrist: An Evaluation of Image Quality and Radiation Dose.

The purpose of this study was to assess image quality and radiation dose of a novel twin robotic x-...