Radiomics has the potential to play a pivotal role in oncological translational imaging, particularly in cancer detection, prognosis prediction and response to therapy evaluation. To date, several studies established Radiomics as a useful tool in oncologic imaging, able to support clinicians in practicing evidence-based medicine, uniquely tailored to each patient and tumor. Mineable data, extracted from medical images could be combined with clinical and survival parameters to develop models useful for the clinicians in cancer patients’ assessment. As such, adding Radiomics to traditional subjective imaging may provide a quantitative and extensive cancer evaluation reflecting histologic architecture. In this Part II, we present an overview o...
With the development of functional imaging modalities we now have the ability to study the microenvi...
Radiomics - the high-throughput computation of quantitative image features extracted from medical im...
The growing complexity and volume of clinical data and the associated decision-making processes in o...
Radiomics has been playing a pivotal role in oncological translational imaging, particularly in canc...
Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional...
Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional...
Background and Objective: Specific treatment for each patient based on their clinical data is one of...
International audienceMedical image processing and analysis (also known as Radiomics) is arapidly gr...
Abstract Radiomics is a process of extraction and analysis of quantitative features from diagnostic ...
Radiomics is an emerging field of medical diagnostics that combines data science and medical imaging...
Recent advances in machine learning and artificial intelligence technology have ensured automated ev...
Introduction. Over the last decade, the field of medical imaging experienced an exponential growth, ...
Quantitative extraction of high-dimensional mineable data from medical images is a process known as ...
International audienceRadiomics is defined as the extraction of a large quantity of quantitative ima...
Lung cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical i...
With the development of functional imaging modalities we now have the ability to study the microenvi...
Radiomics - the high-throughput computation of quantitative image features extracted from medical im...
The growing complexity and volume of clinical data and the associated decision-making processes in o...
Radiomics has been playing a pivotal role in oncological translational imaging, particularly in canc...
Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional...
Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional...
Background and Objective: Specific treatment for each patient based on their clinical data is one of...
International audienceMedical image processing and analysis (also known as Radiomics) is arapidly gr...
Abstract Radiomics is a process of extraction and analysis of quantitative features from diagnostic ...
Radiomics is an emerging field of medical diagnostics that combines data science and medical imaging...
Recent advances in machine learning and artificial intelligence technology have ensured automated ev...
Introduction. Over the last decade, the field of medical imaging experienced an exponential growth, ...
Quantitative extraction of high-dimensional mineable data from medical images is a process known as ...
International audienceRadiomics is defined as the extraction of a large quantity of quantitative ima...
Lung cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical i...
With the development of functional imaging modalities we now have the ability to study the microenvi...
Radiomics - the high-throughput computation of quantitative image features extracted from medical im...
The growing complexity and volume of clinical data and the associated decision-making processes in o...