Research on Segmentation Technology in Lung Cancer Radiotherapy Based on Deep Learning

Overview

Journal Curr Med Imaging

Specialty Radiology

Date 2023 Jan 25

PMID 36694318

Authors

Jun Huang

Tao Liu

Beibei Qian

Zhibo Chen

Ya Wang

Affiliations

Soon will be listed here.

Abstract

Background: Lung cancer has the highest mortality rate among cancers. Radiation therapy (RT) is one of the most effective therapies for lung cancer. The correct segmentation of lung tumors (LTs) and organs at risk (OARs) is the cornerstone of successful RT.

Methods: We searched four databases for relevant material published in the last 10 years: Web of Science, PubMed, Science Direct, and Google Scholar. The advancement of deep learning-based segmentation technology for lung cancer radiotherapy (DSLC) research was examined from the perspectives of LTs and OARs.

Results: In this paper, Most of the dice similarity coefficient (DSC) values of LT segmentation in the surveyed literature were above 0.7, whereas the DSC indicators of OAR segmentation were all over 0.8.

Conclusion: The contribution of this review is to summarize DSLC research methods and the issues that DSLC faces are discussed, as well as possible viable solutions. The purpose of this review is to encourage collaboration among experts in lung cancer radiotherapy and DL and to promote more research into the use of DL in lung cancer radiotherapy.

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