School of Physics, Mathematics and Computing, University of Western Australia, Crawley, Western Australia, Australia.
Institute of Medical Physics, The University of Sydney, Camperdown, New South Wales, Australia.
Med Phys. 2023 Jun;50(6):3746-3761. doi: 10.1002/mp.16264. Epub 2023 Feb 15.
In prostate radiation therapy, recent studies have indicated a benefit in increasing the dose to intraprostatic lesions (IPL) compared with standard whole gland radiation therapy. Such approaches typically aim to deliver a target dose to the IPL(s) with no deliberate effort to modulate the dose within the IPL. Prostate cancers demonstrate intra-tumor heterogeneity and hence it is hypothesized that further gains in the optimal delivery of radiation therapy can be achieved through modulation of the dose distribution within the tumor. To account for tumor heterogeneity, biologically targeted radiation therapy (BiRT) aims to utilize a voxel-wise approach to IPL dose prescription by incorporating knowledge of the spatial distribution of tumor characteristics.
The aim of this study was to develop a workflow for generating voxel-wise optimal dose prescriptions that maximize patient tumor control probability (TCP), and evaluate the feasibility and benefits of applying this workflow on a cohort of 62 prostate cancer patients.
The source data for this proof-of-concept study included high resolution histology images annotated with tumor location and grade. Image processing techniques were used to compute voxel-level cell density distribution maps. An absolute tumor cell distribution was calculated via linearly scaling according to published estimated tumor cell numbers. For the IPLs of each patient, optimal dose prescriptions were obtained via three alternative methods for redistribution of IPL boost doses according to maximization of TCP. The radiosensitivity uncertainties were considered using a truncated log-normally distributed linear radiosensitivity parameter ( ) and compared with Gleason pattern (GP) dependent radiosensitivity parameters that were derived based on previously published methods. An ensemble machine learning method was implemented to identify patient-specific features that predict the TCP improvement resulting from dose redistribution relative to a uniform dose distribution.
The Gleason pattern-dependent radiosensitivity parameters were calculated for 20 published prostate cancer ratios. Optimal voxel-level dose prescriptions were generated for all 62 PCa patients. For all dose redistribution scenarios, the optimal dose distribution always shows a higher (or equivalent) TCP level than the uniform dose distribution. The applied random forest regressor could predict patient-specific TCP improvement with low root mean square error (≤1.5%) by using total tumor number, volume of IPLs and the standard deviation of tumor cell number among all voxels.
Biologically-optimized redistribution of a boost dose can yield TCP improvement relative to a uniform-boost dose distribution. Patient-specific tumor characteristics can be used to predict the likelihood of benefit from a redistribution approach for the individual patient.
在前列腺放射治疗中，最近的研究表明，与标准全腺放射治疗相比，增加前列腺内病变（intraprostatic lesions，IPL）的剂量有益处。此类方法通常旨在将目标剂量输送至 IPL 而无需刻意调节 IPL 内的剂量。前列腺癌表现出肿瘤内异质性，因此，人们假设通过调节肿瘤内的剂量分布，可以进一步提高放射治疗的最佳效果。为了考虑肿瘤异质性，生物靶向放射治疗（biologically targeted radiation therapy，BiRT）旨在通过结合肿瘤特征的空间分布知识，利用 IPL 剂量处方的体素方法。
本研究的目的是开发一种生成体素优化剂量处方的工作流程，使患者的肿瘤控制概率（tumor control probability，TCP）最大化，并评估该工作流程在 62 例前列腺癌患者队列上的可行性和益处。
本概念验证研究的原始数据包括高分辨率组织学图像，这些图像标记有肿瘤位置和分级。图像处理技术用于计算体素级别的细胞密度分布图。通过根据已发表的估计肿瘤细胞数量进行线性缩放，计算出绝对肿瘤细胞分布。对于每个患者的 IPL，通过根据 TCP 最大化的三种替代方法重新分配 IPL 增量剂量，获得最佳剂量处方。使用截断对数正态分布的线性放射敏感性参数（ ）考虑放射敏感性不确定性，并与根据以前发表的方法得出的基于 Gleason 模式（Gleason pattern，GP）的放射敏感性参数进行比较。实施了一个集成机器学习方法，以确定预测相对于均匀剂量分布的剂量重新分配导致 TCP 改善的患者特定特征。
为 20 个已发表的前列腺癌 比计算了基于 GP 的放射敏感性参数。为所有 62 例 PCa 患者生成了最佳体素剂量处方。在所有剂量重新分配情况下，最优剂量分布始终显示出比均匀剂量分布更高（或等效）的 TCP 水平。应用随机森林回归器可以通过使用总肿瘤数量、IPL 体积和所有体素中肿瘤细胞数量的标准差，预测患者特异性 TCP 改善，其均方根误差（≤1.5%）较低。
与均匀增量剂量分布相比，生物优化的增量剂量再分配可以提高 TCP。可以使用患者特定的肿瘤特征来预测个体患者从重新分配方法中获益的可能性。
