Literature DB >> 29730736

Are we at a crossroads or a plateau? Radiomics and machine learning in abdominal oncology imaging.

Ronald M Summers1.   

Abstract

Advances in radiomics and machine learning have driven a technology boom in the automated analysis of radiology images. For the past several years, expectations have been nearly boundless for these new technologies to revolutionize radiology image analysis and interpretation. In this editorial, I compare the expectations with the realities with particular attention to applications in abdominal oncology imaging. I explore whether these technologies will leave us at a crossroads to an exciting future or to a sustained plateau and disillusionment.

Keywords:  Computer-aided detection; Image segmentation; Machine learning; Radiomics

Year:  2019        PMID: 29730736     DOI: 10.1007/s00261-018-1613-1

Source DB:  PubMed          Journal:  Abdom Radiol (NY)


  3 in total

1.  A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer.

Authors:  Yiying Zhang; Kan He; Yan Guo; Xiangchun Liu; Qi Yang; Chunyu Zhang; Yunming Xie; Shengnan Mu; Yu Guo; Yu Fu; Huimao Zhang
Journal:  Front Oncol       Date:  2020-04-07       Impact factor: 6.244

2.  A Multiparametric Fusion Radiomics Signature Based on Contrast-Enhanced MRI for Predicting Early Recurrence of Hepatocellular Carcinoma.

Authors:  Wencui Li; Hongru Shen; Lizhu Han; Jiaxin Liu; Bohan Xiao; Xubin Li; Zhaoxiang Ye
Journal:  J Oncol       Date:  2022-09-28       Impact factor: 4.501

3.  A Novel Multimodal Radiomics Model for Predicting Prognosis of Resected Hepatocellular Carcinoma.

Authors:  Ying He; Bin Hu; Chengzhan Zhu; Wenjian Xu; Yaqiong Ge; Xiwei Hao; Bingzi Dong; Xin Chen; Qian Dong; Xianjun Zhou
Journal:  Front Oncol       Date:  2022-03-07       Impact factor: 6.244

  3 in total

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