W Zheng^1,2, S Huang¹, L Ye^1,3, X Zhang^1,4, Y Duan², X Yang¹*, (1) Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong province, 510060, China, (2) Department of Radiation Oncology, Southern Theater Air Force Hospital of the People's Liberation Army, Guangzhou, Guangdong province, 510050, China, (3) Department of Radiation Oncology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, 510095, China,(4) Department of Radiation Oncology, The Fifth Affiliated Cancer Hospital of Guangzhou Medical University, 510799, China

PO-GePV-M-26 (Sunday, 7/10/2022)   [Eastern Time (GMT-4)]

ePoster Forums

Purpose: To achieve the nomenclature standardization based on the AAPM-TG263 and verify the method in multi-center for H&N tumor.

Methods: The H&N radiotherapy plans are randomly selected from three centers: SYSUCC (98 cases), PLA (85 cases), and GYFW (45 cases). Firstly, the CT images and the RT structure files are analyzed via an in-house developed software by Matlab. Secondly, through statistical structure naming and its color definition (RGB), the differences of each center are analyzed, and a multi-center standard naming library and color library are developed to complete a Semanteme-Based Standardizing Nomenclatures (SBSN). Thirdly, the geometric features of OAR, the texture features of the first-order gray histogram and the gray level co-occurrence matrix (GLCM) are used to construct a Content-Based Standardized Nomenclatures (CBSN) library and the discriminant library. Finally, 85% of the cases are used to build a multi-center CBSN-knowledge base, and the remaining 15% of the cases are used as a test set for the model validity evaluation respectively.

Results: We developed a standard naming library and color library for polycentric structures, unifying the normalized naming and coloring of a total of 93 structures across the three centers. The CBSN knowledge library of various centers was established, the test cases of each center were tested, and 20 errors/mismatches were found. For example, SYSUCC has the abnormal position and missing structure; PLA has empty structure and missing structure, and GZFW mainly has missing structure.

Conclusion: Based on AAPM-TG263, this study standardized the semantics, color, and the content of H&N radiotherapy structures and applied them in multi-center. Standardizing different structure names and colors is conducive to data sharing and communication between different institutions; at the same time, the knowledge bases established by different centers are compared and independently tested, and the model can detect the mismatches between semantic names and labels.

Keywords

Structure Analysis, Radiation Therapy

Taxonomy

IM/TH- Informatics: Informatics in Therapy (general)

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