| Literature DB >> 34637568 |
Shaobo Fang1, Yanyu Yang1, Nan Xu1, Yun Tu2, Zhenzhen Yin1, Yu Zhang3, Yajie Liu1, Zhiqing Duan1, Wenyu Liu1, Shaowu Wang1.
Abstract
Over the past two decades, considerable efforts have been made to develop non-invasive methods for determining tumor grade or surrogates for predicting the biological behavior, aiding early treatment decisions, and providing prognostic information. The development of new imaging tools, such as diffusion-weighted imaging, diffusion kurtosis imaging, perfusion imaging, and magnetic resonance spectroscopy have provided leverage in the diagnosis of soft tissue sarcomas. Artificial intelligence is a new technology used to study and simulate human thinking and abilities, which can extract and analyze advanced and quantitative image features from medical images with high throughput for an in-depth characterization of the spatial heterogeneity of tumor tissues. This article reviews the current imaging modalities used to predict the histopathological grade of soft tissue sarcomas and highlights the advantages and limitations of each modality. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.Entities:
Keywords: functional magnetic resonance imaging; histopathological grade; radiomics; soft tissue sarcomas; structural magnetic resonance imaging
Mesh:
Year: 2021 PMID: 34637568 DOI: 10.1002/jmri.27954
Source DB: PubMed Journal: J Magn Reson Imaging ISSN: 1053-1807 Impact factor: 4.813