Oncology/Hematology

An approach for deciphering patient-specific variations with application to breast cancer molecular expression profiles.

Several studies have successfully used molecular expression profiling in conjunction with classification techniques for discerning distinct disease groups. However, a majority of these studies do not provide sufficient insights into potential patient-specific variations within the disease groups. Such variations are ubiquitous and manifests across multiple scales with varying resolution. There is ...

Using machine learning to predict radiation pneumonitis in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy.

To develop a patient-specific 'big data' clinical decision tool to predict pneumonitis in stage I non-small cell lung cancer (NSCLC) patients after stereotactic body radiation therapy (SBRT). 61 features were recorded for 201 consecutive patients with stage I NSCLC treated with SBRT, in whom 8 (4.0%) developed radiation pneumonitis. Pneumonitis thresholds were found for each feature individually u...

Breast cancer-associated high-order SNP-SNP interaction of CXCL12/CXCR4-related genes by an improved multifactor dimensionality reduction (MDR-ER).

In association studies, the combined effects of single nucleotide polymorphism (SNP)-SNP interactions and the problem of imbalanced data between cases...

Automatic weighing attribute to retrieve similar lung cancer nodules.

BACKGROUND: Cancer is a disease characterized as an uncontrolled growth of abnormal cells that invades neighboring tissues and destroys them. Lung can...

Round Randomized Learning Vector Quantization for Brain Tumor Imaging.

Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imag...

Receiver operating curves and dose-volume analysis of late toxicity with stereotactic body radiation therapy for prostate cancer.

PURPOSE: The purpose of this study was to evaluate a receiver operating characteristic (ROC) curve method to determine dose thresholds with late genit...

Consistent quantitative gene product expression: #1. Automated identification of regenerating bone marrow cell populations using support vector machines.

Identification and quantification of maturing hematopoietic cell populations in flow cytometry data sets is a complex and sometimes irreproducible ste...

Radiation Dose Estimation by Automated Cytogenetic Biodosimetry.

The dose from ionizing radiation exposure can be interpolated from a calibration curve fit to the frequency of dicentric chromosomes (DCs) at multiple...

Multiclass imbalance learning: Improving classification of pediatric brain tumors from magnetic resonance spectroscopy.

PURPOSE: Classification of pediatric brain tumors from H-magnetic resonance spectroscopy (MRS) can aid diagnosis and management of brain tumors. Howe...

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors ...

Hybrid-Actuating Macrophage-Based Microrobots for Active Cancer Therapy.

Using macrophage recruitment in tumors, we develop active, transportable, cancer theragnostic macrophage-based microrobots as vector to deliver therap...

Feasibility and safety of cavity-directed stereotactic radiosurgery for brain metastases at a high-volume medical center.

OBJECTIVE: Our objective was to report safety and efficacy of stereotactic radiosurgery (SRS) to the surgical bed following resection of brain metasta...

Lung Cancer Detection Using Fuzzy Auto-Seed Cluster Means Morphological Segmentation and SVM Classifier.

An effective fuzzy auto-seed cluster means morphological algorithm developed in this work to segment the lung nodules from the consecutive slices of C...

Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines.

Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is th...

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentat...

Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction.

BACKGROUND AND AIMS: Machine learning techniques for the text mining of cancer-related clinical documents have not been sufficiently explored. Here so...

Clinical results of mean GTV dose optimized robotic guided SBRT for liver metastases.

BACKGROUND: We retrospectively evaluated the efficacy and toxicity of gross tumor volume (GTV) mean-dose-optimized and real-time motion-compensated ro...

Big Data and machine learning in radiation oncology: State of the art and future prospects.

Precision medicine relies on an increasing amount of heterogeneous data. Advances in radiation oncology, through the use of CT Scan, dosimetry and ima...

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis.

Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Th...

Ontology-based collection, representation and analysis of drug-associated neuropathy adverse events.

BACKGROUND: Neuropathy often occurs following drug treatment such as chemotherapy. Severe instances of neuropathy can result in cessation of life-savi...