Fang Shaobo, Yang Yanyu, Xu Nan, Tu Yun, Yin Zhenzhen, Zhang Yu, Liu Yajie, Duan Zhiqing, Liu Wenyu, Wang Shaowu
Department of Radiology, The Second Hospital, Dalian Medical University, Dalian, China.
Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
J Magn Reson Imaging. 2022 May;55(5):1357-1375. doi: 10.1002/jmri.27954. Epub 2021 Oct 12.
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.
在过去二十年中，人们付出了巨大努力来开发非侵入性方法，以确定肿瘤分级或预测生物学行为的替代指标，辅助早期治疗决策，并提供预后信息。新型成像工具的发展，如扩散加权成像、扩散峰度成像、灌注成像和磁共振波谱，为软组织肉瘤的诊断提供了助力。人工智能是一种用于研究和模拟人类思维及能力的新技术，它可以从医学图像中高通量地提取和分析高级定量图像特征，以深入表征肿瘤组织的空间异质性。本文综述了目前用于预测软组织肉瘤组织病理学分级的成像方式，并强调了每种方式的优缺点。证据级别：5 技术效能：2级。
