Brain Cancer

Utilizing Deep Convolutional Generative Adversarial Networks for Automatic Segmentation of Gliomas: An Artificial Intelligence Study.

AIM: To describe a deep convolutional generative adversarial networks (DCGAN) model which learns normal brain MRI from normal subjects than finds distortions such as a glioma from a test subject while performing a segmentation at the same time.

Risk of Developing Breast Reconstruction Complications: A Machine-Learning Nomogram for Individualized Risk Estimation with and without Postmastectomy Radiation Therapy.

BACKGROUND: The purpose of this study was to create a nomogram using machine learning models predicting risk of breast reconstruction complications with or without postmastectomy radiation therapy.

Foundations of Machine Learning-Based Clinical Prediction Modeling: Part IV-A Practical Approach to Binary Classification Problems.

We illustrate the steps required to train and validate a simple, machine learning-based clinical prediction model for any binary outcome, such as, for...

Foundations of Machine Learning-Based Clinical Prediction Modeling: Part V-A Practical Approach to Regression Problems.

This chapter goes through the steps required to train and validate a simple, machine learning-based clinical prediction model for any continuous outco...

Introduction to Deep Learning in Clinical Neuroscience.

The use of deep learning (DL) is rapidly increasing in clinical neuroscience. The term denotes models with multiple sequential layers of learning algo...

Machine Learning-Based Radiomics in Neuro-Oncology.

In the last decades, modern medicine has evolved into a data-centered discipline, generating massive amounts of granular high-dimensional data exceedi...

Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging.

This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinica...

Low-dose CT noise reduction based on local total variation and improved wavelet residual CNN.

BACKGROUND: Low-dose computed tomography (LDCT) is an effective method for reducing radiation exposure. However, reducing radiation dose leads to cons...

An artificial neural network model based on DNA damage response genes to predict outcomes of lower-grade glioma patients.

Although the prognosis of lower-grade glioma (LGG) patients is better than others, outcomes are highly heterogeneous. Isocitrate dehydrogenase (IDH) m...

Robotics, imaging, and artificial intelligence in the catheterisation laboratory.

The catheterisation laboratory today combines diagnosis and therapeutics, through various imaging modalities and a prolific list of interventional too...

Automatic contour segmentation of cervical cancer using artificial intelligence.

In cervical cancer treatment, radiation therapy is selected based on the degree of tumor progression, and radiation oncologists are required to deline...

Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression.

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disor...

Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients.

Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studie...

Highly accurate diagnosis of papillary thyroid carcinomas based on personalized pathways coupled with machine learning.

Thyroid nodules are neoplasms commonly found among adults, with papillary thyroid carcinoma (PTC) being the most prevalent malignancy. However, curren...

Artificial Intelligence in Diagnostic Imaging and Radiation Therapy.

Artificial intelligence (AI) is a rapidly growing profession that shows continued promise for diagnostic imaging in various disciplines, including der...

3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks.

To overcome the computational burden of processing three-dimensional (3D) medical scans and the lack of spatial information in two-dimensional (2D) me...

THE SAFETY AND EFFICACY OF ROBOT-ASSISTED STEREOTACTIC BIOPSY FOR BRAIN GLIOMA: EARLIEST INSTITUTIONAL EXPERIENCES AND EVALUATION OF LITERATURE.

Robot-assisted brain tumor biopsy is becoming one of the most important innovative technologies in neurosurgical practice. The idea behind its engagem...

Challenges in Building of Deep Learning Models for Glioblastoma Segmentation: Evidence from Clinical Data.

In this article, we compare the performance of a state-of-the-art segmentation network (UNet) on two different glioblastoma (GB) segmentation datasets...

The potential for reduced radiation dose from deep learning-based CT image reconstruction: A comparison with filtered back projection and hybrid iterative reconstruction using a phantom.

The purpose of this phantom study is to compare radiation dose and image quality of abdominal computed tomography (CT) scanned with different tube vol...

Robotic Radical Cystectomy Outcomes after Intervention for Prostate Cancer.

We evaluated patients who underwent treatment for prostate cancer and then subsequent robot-assisted radical cystectomy (RARC). Our objective was to ...