Hepatocellular carcinoma(HCC)remains the most common malignancy to threaten public health globally.With advances in artificial intelligence techniques,radiomics for HCC management provides a novel perspective to solve...Hepatocellular carcinoma(HCC)remains the most common malignancy to threaten public health globally.With advances in artificial intelligence techniques,radiomics for HCC management provides a novel perspective to solve unmet needs in clinical settings,and reveals pixel-level radiological information for medical imaging big data,correlating the radiological phenotype with targeted clinical issues.Conventional radiomics pipelines depend on handcrafted engineering features,and further deep learning-based radiomics pipelines are supplemented with deep features calculated via self-learning strategies.During the past decade,radiomics has been widely applied in accurate diagnoses and pathological or biological behavior evaluation,as well as in prognosis prediction.In this review,we systematically introduce the main pipelines of artificial intelligence-based radiomics and their efficacy in the clinical studies of HCC.展开更多
基金This study has received funding by the National Key Research and Development Program of China under Grant 2017YFA0700401 and 2021YFC2500402Ministry of Science and Technology of China under Grant No.2017YFA0205200+2 种基金National Natural Science Foundation of China under Grant No.82001917,81930053,82090052,82090051,82093219055,81227901,92159202 and 81527805Beijing Natural Science Foundation under Grant No.L192061the Project of High-Level Talents Team Introduction in Zhuhai City。
文摘Hepatocellular carcinoma(HCC)remains the most common malignancy to threaten public health globally.With advances in artificial intelligence techniques,radiomics for HCC management provides a novel perspective to solve unmet needs in clinical settings,and reveals pixel-level radiological information for medical imaging big data,correlating the radiological phenotype with targeted clinical issues.Conventional radiomics pipelines depend on handcrafted engineering features,and further deep learning-based radiomics pipelines are supplemented with deep features calculated via self-learning strategies.During the past decade,radiomics has been widely applied in accurate diagnoses and pathological or biological behavior evaluation,as well as in prognosis prediction.In this review,we systematically introduce the main pipelines of artificial intelligence-based radiomics and their efficacy in the clinical studies of HCC.
