Oncology/Hematology

An ensemble learning approach for brain cancer detection exploiting radiomic features.

BACKGROUND AND OBJECTIVE: The brain cancer is one of the most aggressive tumour: the 70% of the patients diagnosed with this malignant cancer will not survive. Early detection of brain tumours can be fundamental to increase survival rates. The brain cancers are classified into four different grades (i.e., I, II, III and IV) according to how normal or abnormal the brain cells look. The following wo...

Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring.

INTRODUCTION: Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN radiotherapy and for investigating the relationships between radiation dose to OARs and radiation-induced side effects. The automatic contouring algorithms that are currently in clinical use, such as atlas-based contouring (ABAS), leave room for improvement. The aim of this study was to use a comprehensive ...

Effect of Radiation Doses to the Heart on Survival for Stereotactic Ablative Radiotherapy for Early-stage Non-Small-cell Lung Cancer: An Artificial Neural Network Approach.

INTRODUCTION: The cardiac radiation dose is an important predictor of cardiac toxicity and overall survival (OS) for patients with locally advanced no...

Preparing for Artificial Intelligence: Systems-Level Implications for the Medical Imaging and Radiation Therapy Professions.

Innovations in artificial intelligence (AI) are driving a new industrial revolution, and as a result, the medical radiation sciences is experiencing t...

A Machine Learning-Based Prediction Platform for P-Glycoprotein Modulators and Its Validation by Molecular Docking.

P-glycoprotein (P-gp) is an important determinant of multidrug resistance (MDR) because its overexpression is associated with increased efflux of vari...

[Basis and perspectives of artificial intelligence in radiation therapy].

Artificial intelligence is a highly polysemic term. In computer science, with the objective of being able to solve totally new problems in new context...

Antiproliferative, neurotoxic, genotoxic and mutagenic effects of toxic cyanobacterial extracts.

Cyanobacteria are the rich resource of various secondary metabolites including toxins with broad pharmaceutical significance. The aim of this work was...

Radiomics in stratification of pancreatic cystic lesions: Machine learning in action.

Pancreatic cystic lesions (PCLs) are well-known precursors of pancreatic cancer. Their diagnosis can be challenging as their behavior varies from beni...

A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging.

BACKGROUND: To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighte...

Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.

PURPOSE: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease followin...

A Super-Learner Model for Tumor Motion Prediction and Management in Radiation Therapy: Development and Feasibility Evaluation.

In cancer radiation therapy, large tumor motion due to respiration can lead to uncertainties in tumor target delineation and treatment delivery, thus ...

Artificial intelligence applications for pediatric oncology imaging.

Machine learning algorithms can help to improve the accuracy and efficiency of cancer diagnosis, selection of personalized therapies and prediction of...

An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine.

Super-resolution, which is one of the trend issues of recent times, increases the resolution of the images to higher levels. Increasing the resolution...

Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation.

Liver and hepatic tumor segmentation remains a challenging problem in Computer Tomography (CT) images analysis due to its shape variation and vague bo...

Outpatient Anesthesia Facilitates Stereotactic Body Radiation Therapy for Early Stage Lung Cancer Patients With Advanced Cognitive Impairments.

PURPOSE: To report on the use of outpatient anesthesia (OPA) facilitating delivery of stereotactic body radiation therapy (SBRT) in patients with seve...

Classification and Diagnosis of Thyroid Carcinoma Using Reinforcement Residual Network with Visual Attention Mechanisms in Ultrasound Images.

How to differentiate thyroid cancer nodules from a large number of benign nodules is always a challenging subject for clinicians. This paper proposes ...

Re: Lorenzo Marconi, Thomas Stonier, Rafael Tourinho-Barbosa, et al. Robot-assisted Radical Prostatectomy After Focal Therapy: Oncological, Functional Outcomes and Predictors of Recurrence. Eur Urol 2019;76:27-30: Salvage Robotic Radical Prostatectomy for Recurrent Prostate Cancer After Focal Therapy.

Artificial Intelligence and the Medical Radiation Profession: How Our Advocacy Must Inform Future Practice.

There is no escaping the fact that academics are devoting unrelenting attention to the impact artificial intelligence will have on health care. Radiol...

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal.

Differential gene expression analysis is an important technique for understanding disease states. The machine learning algorithm CorEx has shown utili...

The Use of Transdermal Estrogen in Castrate-resistant, Steroid-refractory Prostate Cancer.

BACKGROUND: Androgen-deprivation therapy is the mainstay of treatment for metastatic prostate cancer. Corticosteroids and estrogens are also useful ag...