Radiomics-based T-staging of hollow organ cancers

Dong Huang; Xiaopan Xu; Peng Du; Yuefei Feng; Xi Zhang; Hongbing Lu; Yang Liu

doi:10.3389/fonc.2023.1191519

Radiomics-based T-staging of hollow organ cancers

Front Oncol. 2023 Aug 30:13:1191519. doi: 10.3389/fonc.2023.1191519. eCollection 2023.

Authors

Dong Huang^{1

2}, Xiaopan Xu^{1

2}, Peng Du^{1

2}, Yuefei Feng^{1

2}, Xi Zhang^{1

2}, Hongbing Lu^{1

2}, Yang Liu^{1

2}

Affiliations

¹ School of Biomedical Engineering, Air Force Medical University, Shaanxi, China.
² Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Shaanxi, China.

Abstract

Cancer growing in hollow organs has become a serious threat to human health. The accurate T-staging of hollow organ cancers is a major concern in the clinic. With the rapid development of medical imaging technologies, radiomics has become a reliable tool of T-staging. Due to similar growth characteristics of hollow organ cancers, radiomics studies of these cancers can be used as a common reference. In radiomics, feature-based and deep learning-based methods are two critical research focuses. Therefore, we review feature-based and deep learning-based T-staging methods in this paper. In conclusion, existing radiomics studies may underestimate the hollow organ wall during segmentation and the depth of invasion in staging. It is expected that this survey could provide promising directions for following research in this realm.

Keywords: T-staging; deep learning-based methods; feature-based methods; hollow organ cancer; radiomics; segmentation.

Publication types

Review

Grants and funding

This work was supported by the Natural Science Basic Research Program of Shaanxi Province (No. 2023-JC-QN-0704), National Natural Science Foundation of China under grant (No. 81871424, No. 81901698), the Natural Science Foundation of Shaanxi Province (No. 2020JZ-28), and Key Research and development plan of Shaanxi Province (No. 2021SF-192).