Brain Cancer

Model Experimental Study of Man-Machine Interactive Robot-Assisted Craniotomy.

To evaluate the feasibility, safety, and accuracy of the new man-machine interactive robotic system in model experiment. The implantation of the 8 to 10 bone screws over the skull model obtained from real patient's digital imaging and communications in medicine (DICOM), three-dimensional spiral computed tomography (CT) scans were taken. The end of the robotic arm was replaced with standard parts (...

Dual-responsive biohybrid neutrobots for active target delivery.

Swimming biohybrid microsized robots (e.g., bacteria- or sperm-driven microrobots) with self-propelling and navigating capabilities have become an exciting field of research, thanks to their controllable locomotion in hard-to-reach areas of the body for noninvasive drug delivery and treatment. However, current cell-based microrobots are susceptible to immune attack and clearance upon entering the ...

Predicting Discharge Disposition Following Meningioma Resection Using a Multi-Institutional Natural Language Processing Model.

BACKGROUND: Machine learning (ML)-based predictive models are increasingly common in neurosurgery, but typically require large databases of discrete v...

Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks.

The aim of this study was to develop an automated segmentation approach for small gross tumor volumes (GTVs) in 3D planning computed tomography (CT) i...

Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics.

BACKGROUND: Glioma prognosis depends on isocitrate dehydrogenase (IDH) mutation status. We aimed to predict the IDH status of gliomas from preoperativ...

IDH glioma radiogenomics in the era of deep learning.

Feasibility Study on Automatic Interpretation of Radiation Dose Using Deep Learning Technique for Dicentric Chromosome Assay.

The interpretation of radiation dose is an important procedure for both radiological operators and persons who are exposed to background or artificial...

Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers.

BACKGROUND: Pathological diagnosis of glioma subtypes is essential for treatment planning and prognosis. Standard histological diagnosis of glioma is ...

Deep learning based analysis of sentiment dynamics in online cancer community forums: An experience.

Online health communities (OHC) provide various opportunities for patients with chronic or life-threatening illnesses, especially for cancer patients ...

Differentiation of rare brain tumors through unsupervised machine learning: Clinical significance of in-depth methylation and copy number profiling illustrated through an unusual case of IDH wildtype glioblastoma.

Methylation profiling has become a mainstay in brain tumor diagnostics since the introduction of the first publicly available classification tool by t...

CT Dosimetry: What Has Been Achieved and What Remains to Be Done.

Radiation dose in computed tomography (CT) has become a hot topic due to an upward trend in the number of CT procedures worldwide and the relatively h...

Dual residual convolutional neural network (DRCNN) for low-dose CT imaging.

The excessive radiation doses in the application of computed tomography (CT) technology pose a threat to the health of patients. However, applying a l...

[A Case of Locally Advanced Rectal Cancer Treated by Robot Assisted Intersphincteric Resection after Neoadjuvant Chemotherapy].

We present a case of locally advanced rectal cancer(LARC)treated by robot assisted intersphincteric resection(ISR)and lateral lymph node dissection(LL...

Prediction of Radiation Pneumonitis With Machine Learning in Stage III Lung Cancer: A Pilot Study.

BACKGROUND: Radiation pneumonitis (RP) is a dose-limiting toxicity in lung cancer radiotherapy (RT). As risk factors in the development of RP, patient...

The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.

With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of ...

[A Study on Radiation Dermatitis Grading Support System Based on Deep Learning by Hybrid Generation Method].

PURPOSE: Radiation dermatitis is one of the most common adverse events in patients undergoing radiotherapy. However, the objective evaluation of this ...

[Automation of Damage Detection and Damage Area Measurement of X-ray Protective Clothing Using Deep Learning].

PURPOSE: Damage to shielding sheets on X-ray protective clothing may be a cause of increased radiation exposure. To prevent increased radiation exposu...

Prediction of survival rate and effect of drugs on cancer patients with somatic mutations of genes: An AI-based approach.

The causal role of somatic mutation and its interrelationship with gene expression profile during tumor development has already been observed, which p...

Comparison between atlas and convolutional neural network based automatic segmentation of multiple organs at risk in non-small cell lung cancer.

Delineation of organs at risk (OARs) is important but time consuming for radiotherapy planning. Automatic segmentation of OARs based on convolutional ...

A Brain Tumor Segmentation Framework Based on Outlier Detection Using One-Class Support Vector Machine.

Accurate segmentation of brain tumors is a challenging task and also a crucial step in diagnosis and treatment planning for cancer patients. Magnetic ...