Predicting cancer outcomes with radiomics and artificial intelligence in radiology
The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the
application of AI-based cancer imaging analysis to address other, more complex, clinical …
application of AI-based cancer imaging analysis to address other, more complex, clinical …
[HTML][HTML] The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges
Medical imaging can assess the tumor and its environment in their entirety, which makes it
suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in …
suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in …
[HTML][HTML] Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers
Introduction Immunotherapy is regarded as one of the major breakthroughs in cancer
treatment. Despite its success, only a subset of patients responds—urging the quest for …
treatment. Despite its success, only a subset of patients responds—urging the quest for …
Radiomics and radiogenomics in gliomas: a contemporary update
The natural history and treatment landscape of primary brain tumours are complicated by the
varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low …
varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low …
Radiomics in breast cancer classification and prediction
Breast Cancer (BC) is the common form of cancer in women. Its diagnosis and screening are
usually performed through different imaging modalities such as mammography, magnetic …
usually performed through different imaging modalities such as mammography, magnetic …
[HTML][HTML] Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology
Medical image processing and analysis (also known as Radiomics) is a rapidly growing
discipline that maps digital medical images into quantitative data, with the end goal of …
discipline that maps digital medical images into quantitative data, with the end goal of …
Deep learning with convolutional neural network in radiology
Deep learning with a convolutional neural network (CNN) is gaining attention recently for its
high performance in image recognition. Images themselves can be utilized in a learning …
high performance in image recognition. Images themselves can be utilized in a learning …
Beyond imaging: the promise of radiomics
The domain of investigation of radiomics consists of large-scale radiological image analysis
and association with biological or clinical endpoints. The purpose of the present study is to …
and association with biological or clinical endpoints. The purpose of the present study is to …
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 …
Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics
Purpose The aim of this systematic review was to analyse literature on artificial intelligence
(AI) and radiomics, including all medical imaging modalities, for oncological and non …
(AI) and radiomics, including all medical imaging modalities, for oncological and non …