通过具有球形卷积的新型深度神经网络进行单等中心多靶点（SIMT）放射外科手术中的全脑剂量估计。

Total brain dose estimation in single-isocenter-multiple-targets (SIMT) radiosurgery via a novel deep neural network with spherical convolutions.

作者信息

Yang Zhenyu, Khazaieli Mercedeh, Vaios Eugene, Zhang Rihui, Zhao Jingtong, Mullikin Trey, Yang Albert, Yin Fang-Fang, Wang Chunhao

机构信息

Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China.

Department of Radiation Oncology, Duke University, Durham, North Carolina, USA.

出版信息

Med Phys. 2025 Jun;52(6):4266-4277. doi: 10.1002/mp.17748. Epub 2025 Mar 18.

DOI:10.1002/mp.17748

PMID:40100547

Abstract

BACKGROUND AND PURPOSE

Accurate prediction of normal brain dosimetric parameters is crucial for the quality control of single-isocenter multi-target (SIMT) stereotactic radiosurgery (SRS) treatment planning. Reliable dose estimation of normal brain tissue is one of the great indicators to evaluate plan quality and is used as a reference in clinics to improve potentially SIMT SRS treatment planning quality consistency. This study aimed to develop a spherical coordinate-defined deep learning model to predict the dose to a normal brain for SIMT SRS treatment planning.

METHODS

By encapsulating the human brain within a sphere, 3D volumetric data of planning target volume (PTVs) can be projected onto this geometry as a 2D spherical representation (in azimuthal and polar angles). A novel deep learning model spherical convolutional neural network (SCNN) was developed based on spherical convolution to predict brain dosimetric evaluators from spherical representation. Utilizing 106 SIMT cases, the model was trained to predict brain V50%, V60%, and V66.7%, corresponding to V10Gy and V12Gy, as key dosimetric indicators. The model prediction performance was evaluated using the coefficient of determination (R), mean absolute error (MAE), and mean absolute percentage error (MAPE).

RESULTS

The SCNN accurately predicted normal brain dosimetric values from the modeled spherical PTV representation, with R scores of 0.92 ± 0.05/0.94 ± 0.10/0.93 ± 0.09 for V50%/V60%/V66.7%, respectively. MAEs values were 1.94 ± 1.61 cc/1.23 ± 0.98 cc/1.13 ± 0.99 cc, and MAPEs were 19.79 ± 20.36%/20.79 ± 21.07%/21.15 ± 22.24%, respectively.

CONCLUSIONS

The deep learning model provides treatment planners with accurate prediction of dose to normal brain, enabling improved consistency in treatment planning quality. This method can be extended to other brain-related analyses as an efficient data dimension reduction method.

摘要

背景与目的

准确预测正常脑剂量学参数对于单等中心多靶点（SIMT）立体定向放射外科（SRS）治疗计划的质量控制至关重要。正常脑组织的可靠剂量估计是评估计划质量的重要指标之一，并在临床上用作参考，以提高潜在的SIMT SRS治疗计划质量的一致性。本研究旨在开发一种基于球坐标定义的深度学习模型，用于预测SIMT SRS治疗计划中正常脑的剂量。

方法

通过将人脑封装在一个球体内，计划靶区（PTV）的三维体积数据可以投影到这个几何体上，形成二维球面表示（方位角和极角）。基于球面卷积开发了一种新型深度学习模型——球面卷积神经网络（SCNN），用于从球面表示中预测脑剂量学评估指标。利用106例SIMT病例，对该模型进行训练，以预测脑V50%、V60%和V66.7%，分别对应V10Gy和V12Gy，作为关键剂量学指标。使用决定系数（R）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）评估模型的预测性能。

结果

SCNN能够从建模的球面PTV表示中准确预测正常脑剂量学值，V50%/V60%/V66.7%的R分数分别为0.92±0.05/0.94±0.10/0.93±0.09。MAE值分别为1.94±1.61cc/1.23±0.98cc/1.13±0.99cc，MAPE分别为19.79±20.36%/20.79±21.07%/21.15±22.24%。