Study of standardizing nomenclatures for organs at risk of nasopharyngeal carcinoma via the contouring content-based image retrieval method
10.3760/cma.j.cn113030-20200406-00159
- VernacularTitle:基于勾画内容检索方法的鼻咽癌危及器官结构标准化命名的研究
- Author:
Xiuying MAI
1
;
Shen HUANG
;
Zhenfang ZHONG
;
Wanjia ZHENG
;
Shuxian CHEN
;
Guangsen HUANG
;
Su ZHOU
;
Sijuan HUANG
;
Yunfei XIA
;
Xiaoyan HUANG
;
Xin YANG
Author Information
1. 中山大学肿瘤防治中心 华南肿瘤学国家重点实验室 肿瘤医学协同创新中心 广东省鼻咽癌诊治研究重点实验室，广州 510060
- Keywords:
Content-based standardizing nomenclatures;
Nasopharyngeal carcinoma;
Radiotherapy structure;
Quality assurance
- From:
Chinese Journal of Radiation Oncology
2021;30(8):803-810
- CountryChina
- Language:Chinese
-
Abstract:
Objective:Based on the AAPM TG-263, a Content-Based Standardizing Nomenclatures (CBSN) was proposed to explore the feasibility of its standardization verification for organs at risk (OAR) of nasopharyngeal carcinoma (NPC).Methods:The radiotherapy structure files of 855 patients with nasopharyngeal carcinoma (NPC) receiving intensity-modulated radiotherapy (IMRT) from 2017 to 2019(15 of whom showed clinical anomalous structures) were retrospectively collected and processed. The Matlab self-developed software was used to obtain the image position, geometric features, first-order gray histogram, and the Gray-level Co-occurrence Matrix′s texture features of the OAR contour outlined by the doctor to establish the CBSN Location Verification model and CBSN Knowledge Library. Fisher discriminant analysis was employed to establish a CBSN OAR classification model, which was evaluated using self-validation, cross-validation, and external validation, respectively.Results:99%(69/70) of the simulated anomalous structures were outside the 90% reference range of the CBSN Knowledge Library and the characteristic parameters significantly differed among different OARs (all P<0.001). The accuracy rates of self-validation, cross-validation and external verification of the CBSN OAR classification model were 92.1%, 92.0% and 91.8%, respectively. Fourteen cases of clinical abnormal structures were successfully detected by CBSN with an accuracy rate of 93%(14/15). In the simulation test, the accuracy of the left and right location verification reached 100%, such as detecting the right eye lens named Len_L. Conclusion:CBSN can be used for OAR verification of NPC, providing reference for multi-center cooperation and standardized radiotherapy of NPC patients.
