AIMC Topic: Rectal Neoplasms

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Machine Learning-based Analysis of Rectal Cancer MRI Radiomics for Prediction of Metachronous Liver Metastasis.

Academic radiology Jan 30, 2019
RATIONALE AND OBJECTIVES: To use machine learning-based magnetic resonance imaging radiomics to predict metachronous liver metastases (MLM) in patients with rectal cancer.
Middle Aged ROC Curve Liver Neoplasms Retrospective Studies Female Male Rectum Rectal Neoplasms Neoplasm Metastasis Adenocarcinoma Support Vector Machine Magnetic Resonance Imaging Humans Algorithms
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A study of positioning orientation effect on segmentation accuracy using convolutional neural networks for rectal cancer.

Journal of applied clinical medical physics Nov 12, 2018
PURPOSE: Convolutional neural networks (CNN) have greatly improved medical image segmentation. A robust model requires training data can represent the entire dataset. One of the differing characteristics comes from variability in patient positioning ...
Humans Radiotherapy, Intensity-Modulated Patient Positioning Supine Position Prone Position Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Neural Networks, Computer Rectal Neoplasms
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Towards a modular decision support system for radiomics: A case study on rectal cancer.

Artificial intelligence in medicine Oct 4, 2018
Following the personalized medicine paradigm, there is a growing interest in medical agents capable of predicting the effect of therapies on patients, by exploiting the amount of data that is now available for each patient. In disciplines like oncolo...
Humans Machine Learning Rectal Neoplasms Decision Making, Computer-Assisted Precision Medicine ROC Curve Image Processing, Computer-Assisted
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Cascaded atrous convolution and spatial pyramid pooling for more accurate tumor target segmentation for rectal cancer radiotherapy.

Physics in medicine and biology Sep 17, 2018
Convolutional neural networks (CNNs) have become the state-of-the-art method for medical segmentation. However, repeated pooling and striding operations reduce the feature resolution, causing loss of detailed information. Additionally, tumors of diff...
Rectal Neoplasms Tomography, X-Ray Computed Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans Neural Networks, Computer
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The radiation oncology ontology (ROO): Publishing linked data in radiation oncology using semantic web and ontology techniques.

Medical physics Aug 24, 2018
PURPOSE: Personalized medicine is expected to yield improved health outcomes. Data mining over massive volumes of patients' clinical data is an appealing, low-cost and noninvasive approach toward personalization. Machine learning algorithms could be ...
Rectal Neoplasms Biological Ontologies Clinical Trials as Topic Semantic Web Humans Radiation Oncology Publications
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Technical Note: A deep learning-based autosegmentation of rectal tumors in MR images.

Medical physics May 3, 2018
PURPOSE: Manual contouring of gross tumor volumes (GTV) is a crucial and time-consuming process in rectum cancer radiotherapy. This study aims to develop a simple deep learning-based autosegmentation algorithm to segment rectal tumors on T2-weighted ...
Image Interpretation, Computer-Assisted Pattern Recognition, Automated Neural Networks, Computer Machine Learning Magnetic Resonance Imaging Rectum Rectal Neoplasms Tumor Burden Humans
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Fully automated searching for the optimal VMAT jaw settings based on Eclipse Scripting Application Programming Interface (ESAPI) and RapidPlan knowledge-based planning.

Journal of applied clinical medical physics Mar 25, 2018
PURPOSE: Eclipse treatment planning system has not been able to optimize the jaw positions for Volumetric Modulated Arc Therapy (VMAT). The arbitrary and planner-dependent jaw placements define the maximum field size within which multi-leaf-collimato...
Humans Algorithms Jaw Rectal Neoplasms Jaw Relation Record Radiotherapy Dosage Knowledge Bases Radiotherapy, Intensity-Modulated Organs at Risk Patient Care Planning Radiotherapy Planning, Computer-Assisted
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Surgery: Robotic assistance confers no benefit in rectal cancer surgery.

Nature reviews. Gastroenterology & hepatology Nov 8, 2017
Robotic Surgical Procedures Humans Rectal Neoplasms Laparotomy Robotics Laparoscopy
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Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks.

Medical physics Oct 28, 2017
PURPOSE: Delineation of the clinical target volume (CTV) and organs at risk (OARs) is very important for radiotherapy but is time-consuming and prone to inter-observer variation. Here, we proposed a novel deep dilated convolutional neural network (DD...
Machine Learning Tomography, X-Ray Computed Time Factors Humans Radiotherapy Planning, Computer-Assisted Neural Networks, Computer Image Processing, Computer-Assisted Automation Rectal Neoplasms Organs at Risk
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Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR.

Scientific reports Jul 13, 2017
Multiparametric Magnetic Resonance Imaging (MRI) can provide detailed information of the physical characteristics of rectum tumours. Several investigations suggest that volumetric analyses on anatomical and functional MRI contain clinically valuable ...
Automation Male Machine Learning Image Processing, Computer-Assisted Humans Female Middle Aged Magnetic Resonance Imaging Neural Networks, Computer Aged Deep Learning Rectal Neoplasms
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