Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging
This article is a comprehensive review of the basic background, technique, and clinical
applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A …
applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A …
[HTML][HTML] Application of radiomics and machine learning in head and neck cancers
With the continuous development of medical image informatics technology, more and more
high-throughput quantitative data could be extracted from digital medical images, which has …
high-throughput quantitative data could be extracted from digital medical images, which has …
Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement
Objectives To evaluate radiomics studies according to radiomics quality score (RQS) and
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or …
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or …
Opportunities and challenges in application of artificial intelligence in pharmacology
Artificial intelligence (AI) is a machine science that can mimic human behaviour like
intelligent analysis of data. AI functions with specialized algorithms and integrates with deep …
intelligent analysis of data. AI functions with specialized algorithms and integrates with deep …
Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis
Purpose To systematically review and evaluate the methodological quality of studies using
radiomics for diagnostic and predictive purposes in patients with intracranial meningioma …
radiomics for diagnostic and predictive purposes in patients with intracranial meningioma …
A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study
Background Prediction of brain invasion pre-operatively rather than postoperatively would
contribute to the selection of surgical techniques, predicting meningioma grading and …
contribute to the selection of surgical techniques, predicting meningioma grading and …
Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results
This study aims to determine how randomly splitting a dataset into training and test sets
affects the estimated performance of a machine learning model and its gap from the test …
affects the estimated performance of a machine learning model and its gap from the test …
A pipeline for the implementation and visualization of explainable machine learning for medical imaging using radiomics features
Machine learning (ML) models have been shown to predict the presence of clinical factors
from medical imaging with remarkable accuracy. However, these complex models can be …
from medical imaging with remarkable accuracy. However, these complex models can be …
Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of …
Purpose Biomedical data frequently contain imbalance characteristics which make
achieving good predictive performance with data-driven machine learning approaches a …
achieving good predictive performance with data-driven machine learning approaches a …
Artificial intelligence in brain tumour surgery—an emerging paradigm
S Williams, H Layard Horsfall, JP Funnell… - Cancers, 2021 - mdpi.com
Simple Summary Artificial intelligence (AI) is the branch of computer science that enables
machines to learn, reason, and problem solve. In recent decades, AI has been developed …
machines to learn, reason, and problem solve. In recent decades, AI has been developed …