Brain Cancer

Predicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach.

The broad possibilities offered by microfluidic devices in relation to massive data monitoring and acquisition open the door to the use of deep learning technologies in a very promising field: cell culture monitoring. In this work, we develop a methodology for parameter identification in cell culture from fluorescence images using Convolutional Neural Networks (CNN). We apply this methodology to t...

Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer.

Machine learning (ML) holds great promise for impacting healthcare delivery; however, to date most methods are tested in 'simulated' environments that cannot recapitulate factors influencing real-world clinical practice. We prospectively deployed and evaluated a random forest algorithm for therapeutic curative-intent radiation therapy (RT) treatment planning for prostate cancer in a blinded, head-...

An assessment of contamination pickup on ground robotic vehicles for nuclear surveying application.

Ground robotic vehicles are often deployed to inspect areas where radioactive floor contamination is a prominent risk. However, the accuracy of detect...

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer.

In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has become one of the important diseases that endange...

Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy.

During the radiotherapy treatment of patients with lung cancer, the radiation delivered to healthy tissue around the tumor needs to be minimized, whic...

Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics.

Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of dif...

Moving Forward in the Next Decade: Radiation Oncology Sciences for Patient-Centered Cancer Care.

In a time of rapid advances in science and technology, the opportunities for radiation oncology are undergoing transformational change. The linkage be...

Deep-learning-based image registration and automatic segmentation of organs-at-risk in cone-beam CT scans from high-dose radiation treatment of pancreatic cancer.

PURPOSE: Accurate deformable registration between computed tomography (CT) and cone-beam CT (CBCT) images of pancreatic cancer patients treated with h...

Knowledge-infused Global-Local Data Fusion for Spatial Predictive Modeling in Precision Medicine.

The automated capability of generating spatial prediction for a variable of interest is desirable in various science and engineering domains. Take Pre...

Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.

Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance im...

Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review.

Advances in artificial intelligence-based methods have led to the development and publication of numerous systems for auto-segmentation in radiotherap...

Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.

Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitiv...

Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI.

Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distin...

Generative adversarial network for glioblastoma ensures morphologic variations and improves diagnostic model for isocitrate dehydrogenase mutant type.

Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is ...

A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy.

BACKGROUND AND PURPOSE: Delineating organs at risk (OARs) on computed tomography (CT) images is an essential step in radiation therapy; however, it is...

Providing an accurate global model for monthly solar radiation forecasting using artificial intelligence based on air quality index and meteorological data of different cities worldwide.

This study aims to present an exact model for predicting solar radiation worldwide through a general model. In this study, mean monthly global solar r...

Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.

AIM: To investigate machine learning based models combining clinical, radiomic, and molecular information to distinguish between early true progressio...

Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks.

PURPOSE: Surgery is the predominant treatment modality of human glioma but suffers difficulty on clearly identifying tumor boundaries in clinic. Conve...

Neuroevolution-Based Adaptive Antenna Array Beamforming Scheme to Improve the V2V Communication Performance at Intersections.

The opportunistic exchange of information between vehicles can significantly contribute to reducing the occurrence of accidents and mitigating their d...

Radiomics analysis on CT images for prediction of radiation-induced kidney damage by machine learning models.

INTRODUCTION: We aimed to assess the power of radiomic features based on computed tomography to predict risk of chronic kidney disease in patients und...