Brain Cancer

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 staff's awareness of the invisible radiation and monitor dose online, computational scatter estimation methods are convenient. However, such methods are usually based on Monte Carlo (MC) simulations, which are inherently computationally expensive. Yet, ...

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 with poor overall survival of patients with many solid tumors. In this paper, we aimed to predict radiation-induced lymphocyte depletion in esophageal cancer patients during the course of RT based on patient characteristics and dosimetric features.

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...

Brain Tumor Classification Using Fine-Tuned GoogLeNet Features and Machine Learning Algorithms: IoMT Enabled CAD System.

In the healthcare research community, Internet of Medical Things (IoMT) is transforming the healthcare system into the world of the future internet. I...

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...

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 2: Spectroscopy, Chemical Exchange Saturation, Multiparametric Imaging, and Radiomics.

OBJECTIVE: To summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and to highlight the latest bench-to-bedsi...

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...

A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction.

Morphological attributes from histopathological images and molecular profiles from genomic data are important information to drive diagnosis, prognosi...

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...

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...

Application of Deep Learning Technology in Glioma.

A common and most basic brain tumor is glioma that is exceptionally dangerous to health of various patients. A glioma segmentation, which is primarily...

Solar radiation and solar energy estimation using ANN and Fuzzy logic concept: A comprehensive and systematic study.

To overcome the need of the world for energy consumption, we have to find some better and stable alternate ways of renewable energy with advanced tech...

A deep learning radiomics analysis for identifying sinus invasion in patients with meningioma before operation using tumor and peritumoral regions.

BACKGROUND: For patients with meningioma, surgical procedures are different because of the status of sinus invasion. However, there is still no suitab...

Brain Tumor Imaging: Applications of Artificial Intelligence.

Artificial intelligence has become a popular field of research with goals of integrating it into the clinical decision-making process. A growing numbe...

Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer.

Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of i...

Automatic contouring of normal tissues with deep learning for preclinical radiation studies.

Delineation of relevant normal tissues is a bottleneck in image-guided precision radiotherapy workflows for small animals. A deep learning (DL) model ...

Functions of RNF Family in the Tumor Microenvironment and Drugs Prediction in Grade II/III Gliomas.

Increasing evidence has demonstrated that RING finger (RNF) proteins played a vital role in cellular and physiological processes and various diseases....

Establishment and External Validation of a Hypoxia-Derived Gene Signature for Robustly Predicting Prognosis and Therapeutic Responses in Glioblastoma Multiforme.

OBJECTIVE: Hypoxia presents a salient feature investigated in most solid tumors that holds key roles in cancer progression, including glioblastoma mul...