Lung Cancer

Phytochemical analysis and evaluation of the antioxidant and antiproliferative effects of Tucumã oil nanocapsules in breast adenocarcinoma cells (MCF-7).

In this work was to develop an inedited nanocapsule with tucumã oil (). The oil presents of phytosterols (squalene and β-sitosterol), --beta-carotene, acids oleic and palmitic. Antioxidant activity showed a good performance in DPPH and ABTS assays. The nanocapsules were prepared and demonstrated in their characterization particle size (206 ± 0.69 nm). The cytogenotoxicity evaluation was performed ...

Assessment of Polyethylene Glycol Hydrogel Spacer and Its Effect on Rectal Radiation Dose in Prostate Cancer Patients Receiving Proton Beam Radiation Therapy.

PURPOSE: To assess the efficacy of placing a polyethylene glycol (PEG) spacing hydrogel in patients undergoing proton beam radiation therapy for prostate cancer. This study also aims to assess the effect on rectal radiation dose of prostate-rectum separation in various anatomic planes.

A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies.

BACKGROUND: Predicting lung adenocarcinoma (LUAD) risk is crucial in determining further treatment strategies. Molecular biomarkers may improve risk s...

Profiling of Exosomal Biomarkers for Accurate Cancer Identification: Combining DNA-PAINT with Machine- Learning-Based Classification.

Exosomes are endosome-derived vesicles enriched in body fluids such as urine, blood, and saliva. So far, they have been recognized as potential biomar...

Spatial Radiation Dose Influence on Xerostomia Recovery and Its Comparison to Acute Incidence in Patients With Head and Neck Cancer.

PURPOSE: Radiation-induced xerostomia is one of the most prevalent symptoms during and after head and neck cancer radiation therapy (RT). We aimed to ...

DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Network.

This paper introduces a deep neural network based method, i.e., DeepOrganNet, to generate and visualize fully high-fidelity 3D / 4D organ geometric mo...

An investigation of quantitative accuracy for deep learning based denoising in oncological PET.

Reducing radiation dose is important for PET imaging. However, reducing injection doses causes increased image noise and low signal-to-noise ratio (SN...

Precision Surgical Therapy for Adenocarcinoma of the Esophagus and Esophagogastric Junction.

INTRODUCTION: To facilitate the initial clinical decision regarding whether to use esophagectomy alone or neoadjuvant therapy in surgical care for ind...

Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes.

BACKGROUND: The aim of this study was to investigate the influence of convolution kernel and iterative reconstruction on the diagnostic performance of...

Fully automatic catheter segmentation in MRI with 3D convolutional neural networks: application to MRI-guided gynecologic brachytherapy.

External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-...

Machine learning applications in prostate cancer magnetic resonance imaging.

With this review, we aimed to provide a synopsis of recently proposed applications of machine learning (ML) in radiology focusing on prostate magnetic...

MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method.

Magnetic resonance imaging (MRI) has been widely used in combination with computed tomography (CT) radiation therapy because MRI improves the accuracy...

Incorporating imaging information from deep neural network layers into image guided radiation therapy (IGRT).

BACKGROUND AND PURPOSE: To investigate a novel markerless prostate localization strategy using a pre-trained deep learning model to interpret routine ...

Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer.

PURPOSE: To explore the feasibility and performance of machine learning-based radiomics classifier to predict the cell proliferation(Ki-67)in non-smal...

An image-based deep learning framework for individualizing radiotherapy dose.

BACKGROUND: Radiotherapy continues to be delivered uniformly without consideration of individual tumor characteristics. To advance toward more precise...

Clinical Documentation and Patient Care Using Artificial Intelligence in Radiation Oncology.

Detailed clinical documentation is required in the patient-facing specialty of radiation oncology. The burden of clinical documentation has increased ...

Automatic gas detection in prostate cancer patients during image-guided radiation therapy using a deep convolutional neural network.

PURPOSE: The detection of intestinal/rectal gas is very important during image-guided radiation therapy (IGRT) of prostate cancer patients because int...

Modelling disease risk for amyloid A (AA) amyloidosis in non-human primates using machine learning.

Amyloid A (AA) amyloidosis is found in humans and non-human primates, but quantifying disease risk prior to clinical symptoms is challenging. We appl...

Markerless Pancreatic Tumor Target Localization Enabled By Deep Learning.

PURPOSE: Deep learning is an emerging technique that allows us to capture imaging information beyond the visually recognizable level of a human being....

A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial.

PURPOSE: Xerostomia commonly occurs in patients who undergo head and neck radiation therapy and can seriously affect patients' quality of life. In thi...