Radiomics-Based Tumor Phenotyping in Precision Medicine

193.3K

views

380

authors

44

articles

Radiomics-Based Tumor Phenotyping in Precision Medicine

193.3K

views

380

authors

44

articles

Editorial

02 March 2022

Editorial: Radiomics Advances Precision Medicine

Bo Gao

,

Di Dong

,

Huimao Zhang

,

Zaiyi Liu

, 1 more and

Bihong T. Chen

2,800 views

3 citations

Editors

6

Bo Gao

Affiliated Hospital of Guizhou Medical University

Di Dong

Institute of Automation, Chinese Academy of Sciences (CAS)

Zaiyi Liu

Guangdong Provincial People's Hospital

Seyedmehdi Payabvash

School of Medicine, Yale University

Bihong T. Chen

City of Hope National Medical Center

Huimao Zhang

First Affiliated Hospital of Jilin University

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About

Precision medicine has been introduced into routine clinical care with an increasing number of treatments being tailored to patient-specific characteristics. Medical imaging and radiomics can further contribute to precision oncology applications, for example by implementing image-based phenotyping.

Radiomics is a high-throughput quantitative analysis of radiologic images, which noninvasively describes oncologic tissues and tumor phenotypes. The target of radiomics - the imaging phenotype - provides important information including genotype, pathology, the tumor environment, and treatment outcome. It relies on semantic and radiomic features and further combines radiologic features to classify and predict automated analysis methods, like machine learning and deep learning. The application of radiomics in image-based phenotyping holds great promise for improving the discrimination of different imaging phenotypes in cancer, forecasting the response to of possible treatments, and optimizing clinical decisions.

To successfully introduce these methods into clinical care, we should determine how underlying biology-driven patterns are related to the image-based tumor phenotypes. This Research Topic aims to collect new discoveries in the field of radiomics and machine learning in medical imaging of cancer. The Original Research and Review articles should focus on the following subtopics:

1) Precision diagnosis. Original Research articles that use radiomics to preoperatively diagnose the stage, pathologic type, and gene mutation in cancers.
2) Therapeutic effect evaluation. Original Research articles that use radiomics to predict or evaluate the therapeutic effect of specific treatment in cancers.
3) Prognostic prediction. Original Research articles that use radiomics to predict the survival of cancer patients.
4) Novel radiomic methods. Original Research articles that develop novel radiomic methods such as deep learning, multi-habitat, and multi-modality fusion.
5) Review or meta-analysis articles about methods and applications of radiomics.

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Editors

Bo Gao

Affiliated Hospital of Guizhou Medical University

Di Dong

Institute of Automation, Chinese Academy of Sciences (CAS)

Zaiyi Liu

Guangdong Provincial People's Hospital

Seyedmehdi Payabvash

School of Medicine, Yale University

Bihong T. Chen

City of Hope National Medical Center

Huimao Zhang

First Affiliated Hospital of Jilin University

Impact

193,333 Total views

130,888 Article views

60,295 Article downloads

2,150 Topic views

Published In

Frontiers in Oncology

Cancer Imaging and Image-directed Interventions

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