AIMC Topic: Neoplasm Staging
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Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Oct 22, 2019
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 th...
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.
Clinical lung cancer
Oct 21, 2019
INTRODUCTION: The cardiac radiation dose is an important predictor of cardiac toxicity and overall survival (OS) for patients with locally advanced non-small-cell lung cancer (NSCLC). However, radiation-induced cardiac toxicity among patients with ea...
Early Esophageal Cancer: A Gastroenterologist's Disease.
Digestive diseases and sciences
Aug 30, 2019
Traditionally, early esophageal cancer (i.e., cancer limited to the mucosa or superficial submucosa) was managed surgically; the gastroenterologist's role was primarily to diagnose the tumor. Over the last decade, advances in endoscopic imaging, abla...
Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study.
Scientific reports
Aug 29, 2019
Medical images such as magnetic resonance (MR) imaging provide valuable information for cancer detection, diagnosis, and prognosis. In addition to the anatomical information these images provide, machine learning can identify texture features from th...
Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network.
Endoscopy
Aug 23, 2019
BACKGROUND: Visual inspection, lesion detection, and differentiation between malignant and benign features are key aspects of an endoscopist's role. The use of machine learning for the recognition and differentiation of images has been increasingly a...
Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA.
BMC cancer
Aug 23, 2019
BACKGROUND: Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion...
Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes.
Thoracic cancer
Aug 19, 2019
BACKGROUND: The aim of this study was to investigate the influence of convolution kernel and iterative reconstruction on the diagnostic performance of radiomics and deep learning (DL) in lung adenocarcinomas.
An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis.
Nature medicine
Aug 12, 2019
The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessments demonstrate considerable variability and many regions of the world lack access to trained pathol...
A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk.
Breast cancer research : BCR
Jul 29, 2019
BACKGROUND: Breast ductal carcinoma in situ (DCIS) represent approximately 20% of screen-detected breast cancers. The overall risk for DCIS patients treated with breast-conserving surgery stems almost exclusively from local recurrence. Although a mas...
Recognition of early and late stages of bladder cancer using metabolites and machine learning.
Metabolomics : Official journal of the Metabolomic Society
Jun 20, 2019
INTRODUCTION: Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and ...
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