Brain Cancer

Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis.

OBJECTIVEGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common intracranial pathologies encountered by neurosurgeons. They often may have similar radiological findings, making diagnosis difficult without surgical biopsy; however, management is quite different between these two entities. Recently, predictive analytics, including machine learning (ML), have garnered atten...

Prediction of Response to Stereotactic Radiosurgery for Brain Metastases Using Convolutional Neural Networks.

BACKGROUND: It is unclear whether radiomic phenotypes of brain metastases (BM) are related to radiation therapy prognosis. This study assessed whether a convolutional neural network (CNN)-based radiomics model which learned computer tomography (CT) image features with minimal preprocessing, could predict early response of BM to radiosurgery.

Degradation of textile dyes Remazol Yellow Gold and reactive Turquoise: optimization, toxicity and modeling by artificial neural networks.

In this work, the degradation of Remazol Yellow Gold RNL-150% and Reactive Turquoise Q-G125 were investigated using AOP: photolysis, UV/HO, Fenton and...

Deep Learning and Multi-Sensor Fusion for Glioma Classification Using Multistream 2D Convolutional Networks.

This paper addresses issues of brain tumor, glioma, grading from multi-sensor images. Different types of scanners (or sensors) like enhanced T1-MRI, T...

Automatic Delineation of the Clinical Target Volume in Rectal Cancer for Radiation Therapy using Three-dimensional Fully Convolutional Neural Networks.

Accurate, robust, and fast delineation of the clinical target volume (CTV) for the use in radiotherapy of rectal cancer (RC) is highly sought-after. C...

A Fully Convolutional Deep Neural Network for Lung Tumor Boundary Tracking in MRI.

Delineation of lung tumor from adjacent tissue from a series of magnetic resonance images (MRI) poses many difficulties due to the image similarities ...

3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network.

Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health ris...

MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks.

Glioblastoma Multiforme (GBM), a malignant brain tumor, is among the most lethal of all cancers. Temozolomide is the primary chemotherapy treatment fo...

Radiation Oncology Terminology Linker: A Step Towards a Linked Data Knowledge Base.

Performing image feature extraction in radiation oncology is often dependent on the organ and tumor delineations provided by clinical staff. These del...

Low dose CT reconstruction via L1 norm dictionary learning using alternating minimization algorithm and balancing principle.

Excessive radiation exposure in computed tomography (CT) scans increases the chance of developing cancer and has become a major clinical concern. Rece...

[A Software Platform of Treatment Planning System for Radiotherapy Snake Robot].

A software platform integrated of radiation treatment planning system and navigation system has been developed for a newly-developed snake robot. This...

Robot-assisted Extracranial Stereotactic Radiotherapy of Adrenal Metastases in Oligometastatic Non-small Cell Lung Cancer.

AIM: The aim of this study was to evaluate the efficacy and toxicity of stereotactic body radiation therapy (SBRT) in the treatment of patients with a...

Identification of human flap endonuclease 1 (FEN1) inhibitors using a machine learning based consensus virtual screening.

Human Flap endonuclease1 (FEN1) is an enzyme that is indispensable for DNA replication and repair processes and inhibition of its Flap cleavage activi...

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative param...

Endoscopic endonasal transsphenoidal surgery using the iArmS operation support robot: initial experience in 43 patients.

Objective The intelligent arm-support system, iArmS, which follows the surgeon's arm and automatically fixes it at an adequate position, was developed...

Current applications of robotics in spine surgery: a systematic review of the literature.

OBJECTIVE Surgical robotics has demonstrated utility across the spectrum of surgery. Robotics in spine surgery, however, remains in its infancy. Here,...

Evaluation of robot-guided minimally invasive implantation of 2067 pedicle screws.

Objective Recent studies have investigated the role of spinal image guidance for pedicle screw placement. Many authors have observed an elevated place...

Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high-grade glioma.

RATIONALE: Timely detection of pseudoprogression (PSP) is crucial for the management of patients with high-grade glioma (HGG) but remains difficult. T...

Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.

Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is va...

Comparison of Intravitreal Bevacizumab and Intravitreal Diclofenac in the Treatment of Diabetic Macular Edema: a 6-month Follow-up.

The aim of this study was to compare the effect of intravitreal diclofenac, a non-steroidal anti-inflammatory drug (NSAID), with that of bevacizumab, ...