Oncology/Hematology

Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning.

We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx of the ternary classification, compared with a conventional method (hand-crafted imaging feature plus machine learning), (ii) the effectiveness of tran...

Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation.

Automated whole breast ultrasound (ABUS) has been widely used as a screening modality for examination of breast abnormalities. Reviewing hundreds of slices produced by ABUS, however, is time consuming. Therefore, in this paper, a fast and effective computer-aided detection system based on 3-D convolutional neural networks (CNNs) and prioritized candidate aggregation is proposed to accelerate this ...

Classification of Tumor Epithelium and Stroma by Exploiting Image Features Learned by Deep Convolutional Neural Networks.

The tumor-stroma ratio (TSR) reflected on hematoxylin and eosin (H&E)-stained histological images is a potential prognostic factor for survival. Autom...

Immunomarker Support Vector Machine Classifier for Prediction of Gastric Cancer Survival and Adjuvant Chemotherapeutic Benefit.

Current tumor-node-metastasis (TNM) staging system cannot provide adequate information for prediction of prognosis and chemotherapeutic benefits. We ...

Abdominal, multi-organ, auto-contouring method for online adaptive magnetic resonance guided radiotherapy: An intelligent, multi-level fusion approach.

BACKGROUND: Manual contouring remains the most laborious task in radiation therapy planning and is a major barrier to implementing routine Magnetic Re...

OC-2-KB: integrating crowdsourcing into an obesity and cancer knowledge base curation system.

BACKGROUND: There is strong scientific evidence linking obesity and overweight to the risk of various cancers and to cancer survivorship. Nevertheless...

On differentiation between vasogenic edema and non-enhancing tumor in high-grade glioma patients using a support vector machine classifier based upon pre and post-surgery MRI images.

PURPOSE: High grade gliomas (HGGs) are infiltrative in nature. Differentiation between vasogenic edema and non-contrast enhancing tumor is difficult a...

Medical breast ultrasound image segmentation by machine learning.

Breast cancer is the most commonly diagnosed cancer, which alone accounts for 30% all new cancer diagnoses for women, posing a threat to women's healt...

Contour-Seed Pairs Learning-Based Framework for Simultaneously Detecting and Segmenting Various Overlapping Cells/Nuclei in Microscopy Images.

In this paper, we propose a novel contour-seed pairs learning-based framework for robust and automated cell/nucleus segmentation. Automated granular o...

Kernel-Based Microfluidic Constriction Assay for Tumor Sample Identification.

A high-throughput multiconstriction microfluidic channels device can distinguish human breast cancer cell lines (MDA-MB-231, HCC-1806, MCF-7) from imm...

Hepatoid Variant of Yolk Sac Tumor of Both Ovaries with Widespread Intraabdominal and Lung Metastasis: A Case Report.

Hepatoid variant of yolk sac tumor of ovary is an unusual tumor with an aggressive behavior. It is usually observed in young females, presents with ab...

A review of statistical and machine learning methods for modeling cancer risk using structured clinical data.

Advancements are constantly being made in oncology, improving prevention and treatment of cancers. To help reduce the impact and deadliness of cancers...

Explaining the dynamics of tumor aggressiveness: At the crossroads between biology, artificial intelligence and complex systems.

Facing metastasis is the most pressing challenge of cancer research. In this review, we discuss recent advances in understanding phenotypic plasticity...

Framework of Computer Aided Diagnosis Systems for Cancer Classification Based on Medical Images.

Early detection of cancer can increase patients' survivability and treatment options. Medical images such as Mammogram, Ultrasound, Magnetic Resonance...

Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.

BACKGROUND AND PURPOSE: Chest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We util...

Modeling the covariates effects on the hazard function by piecewise exponential artificial neural networks: an application to a controlled clinical trial on renal carcinoma.

BACKGROUND: In exploring the time course of a disease to support or generate biological hypotheses, the shape of the hazard function provides relevant...

Drug response prediction by ensemble learning and drug-induced gene expression signatures.

Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Rec...

Studies on the effect of a nutritious vegetable, on HbSS blood.

Medicinal plants have been used traditionally in Africa, especially Nigeria, in the management of sickle cell disorder (SCD) whose treatment has been ...

Assessment of Functional Phosphatidylinositol 3-Kinase Pathway Activity in Cancer Tissue Using Forkhead Box-O Target Gene Expression in a Knowledge-Based Computational Model.

The phosphatidylinositol 3-kinase (PI3K) pathway is commonly activated in cancer. Tumors are potentially sensitive to PI3K pathway inhibitors, but rel...

Development and Application of a Machine Learning Approach to Assess Short-term Mortality Risk Among Patients With Cancer Starting Chemotherapy.

IMPORTANCE: Patients with cancer who die soon after starting chemotherapy incur costs of treatment without the benefits. Accurately predicting mortali...