AIMC Topic: Retrospective Studies

Clear Filters Showing 9801 to 9810 of 9989 articles

Applied Informatics Decision Support Tool for Mortality Predictions in Patients With Cancer.

JCO clinical cancer informatics
PURPOSE: With rapidly evolving treatment options in cancer, the complexity in the clinical decision-making process for oncologists represents a growing challenge magnified by oncologists' disposition of intuition-based assessment of treatment risks a...

A Decision-Support Tool for Renal Mass Classification.

Journal of digital imaging
We investigate the viability of statistical relational machine learning algorithms for the task of identifying malignancy of renal masses using radiomics-based imaging features. Features characterizing the texture, signal intensity, and other relevan...

Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset.

Journal of digital imaging
The aim of this study is to evaluate the role of convolutional neural network (CNN) in predicting axillary lymph node metastasis, using a breast MRI dataset. An institutional review board (IRB)-approved retrospective review of our database from 1/201...

[Survival Analysis of Stage I Non-small Cell Lung Cancer Patients Treated with 
Da Vinci Robot-assisted Thoracic Surgery].

Zhongguo fei ai za zhi = Chinese journal of lung cancer
BACKGROUND: Da Vinci robotic surgery system is widely used in department of thoracic surgery. The aim of this study is to investigate the treatment outcome of stage I non-small cell lung cancer (NSCLC) via da Vinci Surgical System.

Support vector machine-based assessment of the T-wave morphology improves long QT syndrome diagnosis.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
AIMS: Diagnosing long QT syndrome (LQTS) is challenging due to a considerable overlap of the QTc-interval between LQTS patients and healthy controls. The aim of this study was to investigate the added value of T-wave morphology markers obtained from ...

Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.

Neurosurgical focus
OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion exposes patients to risks of thromboembolic complications and rupture. A priori assessment of FD trea...

A machine learning approach to predict early outcomes after pituitary adenoma surgery.

Neurosurgical focus
OBJECTIVEPituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when usin...

Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging.

Neurosurgical focus
OBJECTIVEPrognostication and surgical planning for WHO grade I versus grade II meningioma requires thoughtful decision-making based on radiographic evidence, among other factors. Although conventional statistical models such as logistic regression ar...

Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine.

Investigative radiology
OBJECTIVES: The aims of this study were, first, to evaluate a deep learning-based, automatic glioblastoma (GB) tumor segmentation algorithm on clinical routine data from multiple centers and compare the results to a ground truth, manual expert segmen...