Oncology/Hematology

The Emerging Role of Radiomics in COPD and Lung Cancer.

Medical imaging plays a key role in evaluating and monitoring lung diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer. The application of artificial intelligence in medical imaging has transformed medical images into mineable data, by extracting and correlating quantitative imaging features with patients' outcomes and tumor phenotype - a process termed radiomics. While t...

Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence.

A deep learning (DL) network for 2D-based breast mass segmentation in unenhanced dedicated breast CT images was developed and validated, and its robustness in radiomic feature stability and diagnostic performance compared to manual annotations of multiple radiologists was investigated. 93 mass-like lesions were extensively augmented and used to train the network (n = 58 masses), which was then tes...

Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics.

Given that the biological processes governing the oncogenesis of pancreatic cancers could present useful therapeutic targets, there is a pressing need...

Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks.

Breast ultrasound and computer aided diagnosis (CAD) has been used to classify tumors into benignancy or malignancy. However, conventional CAD softwar...

Segmentation of breast ultrasound image with semantic classification of superpixels.

Breast cancer is a great threat to females. Ultrasound imaging has been applied extensively in diagnosis of breast cancer. Due to the poor image quali...

Robot-assisted radical cystectomy: Review of surgical technique, and perioperative, oncological and functional outcomes.

Since the introduction of robot-assisted radical cystectomy, efforts have been made to meet the standards in terms of perioperative safety and oncolog...

Batch Mode Active Learning on the Riemannian Manifold for Automated Scoring of Nuclear Pleomorphism in Breast Cancer.

Breast cancer is the most prevalent invasive type of cancer among women. The mortality rate of the disease can be reduced considerably through timely ...

Overlooked pitfalls in multi-class machine learning classification in radiation oncology and how to avoid them.

In radiation oncology, Machine Learning classification publications are typically related to two outcome classes, e.g. the presence or absence of dist...

Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma.

OBJECTIVES: In Japan, endoscopic resection (ER) is often used to treat esophageal squamous cell carcinoma (ESCC) when invasion depths are diagnosed as...

Deep pancreas segmentation with uncertain regions of shadowed sets.

Pancreas segmentation is a challenging task in medical image analysis especially for the patients with pancreatic cancer. First, the images often have...

Deep principal dimension encoding for the classification of early neoplasia in Barrett's Esophagus with volumetric laser endomicroscopy.

Barrett cancer is a treatable disease when detected at an early stage. However, current screening protocols are often not effective at finding the dis...

Quantitative Thermal Imaging Biomarkers to Detect Acute Skin Toxicity From Breast Radiation Therapy Using Supervised Machine Learning.

PURPOSE: Radiation-induced dermatitis is a common side effect of breast radiation therapy (RT). Current methods to evaluate breast skin toxicity inclu...

Nodular Thyroid Disease in the Era of Precision Medicine.

Management of thyroid nodules in the era of precision medicine is continuously changing. Neck ultrasound plays a pivotal role in the diagnosis and sev...

Pilot Study to Establish a Novel Five-Gene Biomarker Panel for Predicting Lymph Node Metastasis in Patients With Early Stage Endometrial Cancer.

In the United States and Europe, endometrial endometrioid carcinoma (EEC) is the most prevalent gynecologic malignancy. Lymph node metastasis (LNM) i...

Diagnosis and classification of cancer using hybrid model based on ReliefF and convolutional neural network.

Machine learning and deep learning methods aims to discover patterns out of datasets such as, microarray data and medical data. In recent years, the i...

A fast deep learning approach for beam orientation optimization for prostate cancer treated with intensity-modulated radiation therapy.

PURPOSE: Beam orientation selection, whether manual or protocol-based, is the current clinical standard in radiation therapy treatment planning, but i...

Machine learning with autophagy-related proteins for discriminating renal cell carcinoma subtypes.

Machine learning techniques have been previously applied for classification of tumors based largely on morphological features of tumor cells recognize...

The Promising Connection Between Data Science and Evolutionary Theory in Oncology.

Theoretical and empirical work over the past several decades suggests that oncogenesis and disease progression represents an evolutionary story. Despi...

Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer.

OBJECTIVES: To build a dual-energy CT (DECT)-based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer.

Machine learning can identify newly diagnosed patients with CLL at high risk of infection.

Infections have become the major cause of morbidity and mortality among patients with chronic lymphocytic leukemia (CLL) due to immune dysfunction and...