Python(编程语言)
蒙特卡罗方法
管道(软件)
线性能量转移
算法
直方图
碳离子放射治疗
计算机科学
估计
数学
放射治疗
统计
人工智能
辐射
医学
物理
工程类
系统工程
量子力学
内科学
图像(数学)
程序设计语言
操作系统
作者
Mai Anakura,YOSHIKI KUBOTA,Takahiro Oike,Akihiko Matsumura,Makoto Sakai,Nobuyuki Kanematsu,Mitsuru Tashiro,Tatsuya Ohno
标识
DOI:10.21873/anticanres.16468
摘要
Background/Aim: This study aimed to develop an improved algorithm for linear energy transfer (LET) estimation in carbon ion radiotherapy (CIRT) using relative biological effectiveness (RBE) and to establish a clinical pipeline for LET assessment. Materials and Methods: New approximation functions for LET versus RBE were developed for the overkill region. LET estimation performance was examined at two facilities (A and B) using archival- and Monte Carlo simulation-derived LET data, respectively, as a reference. A clinical pipeline for LET assessment was developed using Python and treatment planning systems (TPS). Results: In dataset A, LET estimation accuracy in the overkill region was improved by 80.0%. In dataset B, estimation accuracy was 2.3%±0.67% across 5 data points examined. LET distribution and LET-volume histograms were visualized for multiple CIRT plans. Conclusion: The new algorithm showed a greater LET estimation performance at multiple facilities using the same TPS. A clinical pipeline for LET assessment was established.
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