A Patients-Based Statistical Model of Radiotherapy Dose Distribution in Nasopharyngeal Cancer

• Type of Material: Article
• Authors: Gang Liu, Wuhan University; Jing Yang, Huazhong University of Science & Technology; Xin Nie, Huazhong University of Science & Technology; Xiaohui Zhu, Huazhong University of Science & Technology; Xiaoqiang Li, Beaumont Health System; Jun Zhou, Emory University; Peyman Kabolizadeh, Beaumont Health System; Qin Li, Huazhong University of Science & Technology; Hong Quan, Wuhan University; Xuanfeng Ding, Beaumont Health System
• Language: English
• Date: 2019-10-01
• Publisher: SAGE PUBLICATIONS INC
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2019.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1177/1559325819892359
• Title of Journal or Parent Work: DOSE-RESPONSE
• Volume: 17
• Issue: 4
• Start Page: 1559325819892359
• End Page: 1559325819892359
• Grant/Funding Information: This study was supported by the Natural Science Foundation of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (No. 02.03.2017-289).
• Keywords: Radiology, Nuclear Medicine & Medical Imaging; PLAN QUALITY; Toxicology; Pharmacology & Pharmacy; principal component analysis; cancer; Science & Technology; modeling; radiation; Life Sciences & Biomedicine; QUANTITATIVE-EVALUATION; statistical model; IMRT
• Research Categories: Health Sciences, Radiology; Health Sciences, Oncology; Health Sciences, Pharmacology

Abstract

Purpose: To develop a patients-based statistical model of dose distribution among patients with nasopharyngeal cancer (NPC). Methods and Materials: The dose distributions of 75 patients with NPC were acquired and preprocessed to generate a dose-template library. Subsequently, the dominant modes of dose distribution were extracted using principal component analysis (PCA). Leave-one-out cross-validation (LOOCV) was performed for evaluation. Residual reconstruction errors between the doses reconstructed using different dominating eigenvectors and the planned dose distribution were calculated to investigate the convergence characteristics. Three-dimensional Gamma analysis was performed to investigate the accuracy of dose reconstruction. Results: The first 29 components contained 90% of the variance in dose distribution, and 45 components accounted for more than 95% of the variance on average. The residual error of the LOOCV model for the cumulative sum of components over all patients decreased from 8.16 to 4.79 Gy when 1 to 74 components were included in the LOOCV model. The 3-dimensional Gamma analysis results implied that the PCA model was capable of dose distribution reconstruction, and the accuracy was especially satisfactory in the high-dose area. Conclusions: A PCA-based model of dose distribution variations in patients with NPC was developed, and its accuracy was determined. This model could serve as a predictor of 3-dimensional dose distribution.