Brain Cancer

Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network.

Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology. Additionally, these tumors are often diffused and poorly contrasted. Consequently, the segmentation of brain tumor and intratumor subregions using magnetic resonance imaging (MRI) data with minimal human interventions remains a challenging task. In this paper, we present a novel fully automatic segmentati...

Predicting cancer outcomes from histology and genomics using convolutional networks.

Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate h...

Optimization of classification and regression analysis of four monoclonal antibodies from Raman spectra using collaborative machine learning approach.

The use of monoclonal antibodies (mAbs) constitutes one of the most important strategies to treat patients suffering from cancers such as hematologica...

Using Big Data Analytics to Advance Precision Radiation Oncology.

Big clinical data analytics as a primary component of precision medicine is discussed, identifying where these emerging tools fit in the spectrum of g...

Machine Learning on a Genome-wide Association Study to Predict Late Genitourinary Toxicity After Prostate Radiation Therapy.

PURPOSE: Late genitourinary (GU) toxicity after radiation therapy limits the quality of life of prostate cancer survivors; however, efforts to explain...

Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network.

Ultraviolet radiation (UVR) exposure and family history are major associated risk factors for the development of non-melanoma skin cancer (NMSC). The ...

Labeling for Big Data in radiation oncology: The Radiation Oncology Structures ontology.

PURPOSE: Leveraging Electronic Health Records (EHR) and Oncology Information Systems (OIS) has great potential to generate hypotheses for cancer treat...

Changes in systemic vascular endothelial growth factor levels after intravitreal injection of aflibercept in infants with retinopathy of prematurity.

BACKGROUND: To investigate the levels of VEGF in the systemic circulation of patients with type 1 ROP who received intravitreal injections of 1 mg (0....

Outcomes and toxicity from a prospective study of moderately hypofractionated radiation therapy for prostate cancer.

PURPOSE: The purpose of this study is to report the long-term outcomes and toxicity results of a prospective trial of moderately hypofractionated, ima...

Arginylglycylaspartic Acid-Surface-Functionalized Doxorubicin-Loaded Lipid-Core Nanocapsules as a Strategy to Target Alpha(V) Beta(3) Integrin Expressed on Tumor Cells.

Doxorubicin (Dox) clinical use is limited by dose-related cardiomyopathy, becoming more prevalent with increasing cumulative doses. Previously, we dev...

Non-Islet Cell Tumor Hypoglycemia: A Rare Cause of Hypoglycemia in Pulmonary Sarcomatoid Cancer.

Non-islet cell tumor hypoglycemia (NICTH) is a rare paraneoplastic condition caused most commonly by metastatic mesenchymal tumors. A 74-year-old non-...

Chest wall dose reduction using noncoplanar volumetric modulated arc radiation therapy for lung stereotactic ablative radiation therapy.

PURPOSE: Stereotactic ablative radiation therapy (SABR) to lung tumors close to the chest wall can cause rib fractures or chest wall pain. We evaluate...

A novel fully automatic multilevel thresholding technique based on optimized intuitionistic fuzzy sets and tsallis entropy for MR brain tumor image segmentation.

In the present paper, a hybrid multilevel thresholding technique that combines intuitionistic fuzzy sets and tsallis entropy has been proposed for the...

Glioma Survival Prediction with Combined Analysis of In Vivo C-MET PET Features, Ex Vivo Features, and Patient Features by Supervised Machine Learning.

Gliomas are the most common type of tumor in the brain. Although the definite diagnosis is routinely made ex vivo by histopathologic and molecular exa...

Residual Convolutional Neural Network for the Determination of Status in Low- and High-Grade Gliomas from MR Imaging.

Isocitrate dehydrogenase () mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the sta...

First Report of NRG Oncology/Radiation Therapy Oncology Group 0622: A Phase 2 Trial of Samarium-153 Followed by Salvage Prostatic Fossa Irradiation in High-Risk Clinically Nonmetastatic Prostate Cancer After Radical Prostatectomy.

PURPOSE: To investigate the utility of Sm lexidronam (Quadramet) in the setting of men with prostate cancer status post radical prostatectomy who deve...

Deep reinforcement learning for automated radiation adaptation in lung cancer.

PURPOSE: To investigate deep reinforcement learning (DRL) based on historical treatment plans for developing automated radiation adaptation protocols ...

First Ever Use of Proton Stereotactic Body Radiation Therapy Delivered with Curative Intent to Bilateral Synchronous Primary Renal Cell Carcinomas.

Limited therapeutic options exist for inoperable bilateral kidney tumors. We report the first ever use of proton therapy to treat primary renal cell c...

Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.

BACKGROUND: One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause ca...

Identification of potentially oncogenic alterations from tumor-only samples reveals Fanconi anemia pathway mutations in bladder carcinomas.

Cancer is caused by germline and somatic mutations, which can share biological features such as amino acid change. However, integrated germline and so...