a Department of Immunology, Genetics and Pathology , Medical Radiation Sciences, Uppsala University , Uppsala , Sweden.
b Department of Immunology, Genetics and Pathology , Experimental and Clinical Oncology, Uppsala University , Uppsala , Sweden.
Acta Oncol. 2018 May;57(5):574-581. doi: 10.1080/0284186X.2017.1415457. Epub 2017 Dec 20.
Gleason scores for prostate cancer correlates with an increased recurrence risk after radiotherapy (RT). Furthermore, higher Gleason scores correlates with decreasing apparent diffusion coefficient (ADC) data from diffusion weighted MRI (DWI-MRI). Based on these observations, we present a formalism for dose painting prescriptions of prostate volumes based on ADC images mapped to Gleason score driven dose-responses.
The Gleason score driven dose-responses were derived from a learning data set consisting of pre-RT biopsy data and post-RT outcomes for 122 patients treated with a homogeneous dose to the prostate. For a test data set of 18 prostate cancer patients with pre-RT ADC images, we mapped the ADC data to the Gleason driven dose-responses by using probability distributions constructed from published Gleason score correlations with ADC data. We used the Gleason driven dose-responses to optimize dose painting prescriptions that maximize the tumor control probability (TCP) with equal average dose as for the learning sets homogeneous treatment dose.
The dose painting prescriptions increased the estimated TCP compared to the homogeneous dose by 0-51% for the learning set and by 4-30% for the test set. The potential for individual TCP gains with dose painting correlated with increasing Gleason score spread and larger prostate volumes. The TCP gains were also found to be larger for patients with a low expected TCP for the homogeneous dose prescription.
We have from retrospective treatment data demonstrated a formalism that yield ADC driven dose painting prescriptions for prostate volumes that potentially can yield significant TCP increases without increasing dose burdens as compared to a homogeneous treatment dose. This motivates further development of the approach to consider more accurate ADC to Gleason mappings, issues with delivery robustness of heterogeneous dose distributions, and patient selection criteria for design of clinical trials.
前列腺癌的 Gleason 评分与放疗（RT）后复发风险增加相关。此外，较高的 Gleason 评分与来自扩散加权磁共振成像（DWI-MRI）的表观扩散系数（ADC）数据降低相关。基于这些观察结果，我们提出了一种基于 ADC 图像映射到 Gleason 评分驱动的剂量反应的前列腺体积剂量描绘处方的形式。
Gleason 评分驱动的剂量反应是从一个学习数据集推导出来的，该数据集由 122 名接受前列腺均匀剂量治疗的患者的治疗前活检数据和治疗后结果组成。对于 18 名接受治疗前 ADC 图像的前列腺癌患者的测试数据集，我们通过使用从发表的 Gleason 评分与 ADC 数据的相关性中构建的概率分布，将 ADC 数据映射到 Gleason 驱动的剂量反应。我们使用 Gleason 驱动的剂量反应来优化剂量描绘处方，使肿瘤控制概率（TCP）最大化，同时保持与学习集均匀治疗剂量相等的平均剂量。
与学习集的均匀剂量相比，剂量描绘处方增加了估计的 TCP，增加幅度为 0-51%，对于测试集，增加幅度为 4-30%。剂量描绘处方获得的个体 TCP 增益与 Gleason 评分的扩展和前列腺体积的增大呈正相关。对于接受均匀剂量处方的 TCP 预期较低的患者，TCP 增益也更大。
我们从回顾性治疗数据中证明了一种形式，该形式可以为前列腺体积生成 ADC 驱动的剂量描绘处方，与均匀治疗剂量相比，有可能在不增加剂量负担的情况下显著提高 TCP。这促使我们进一步开发该方法，考虑更准确的 ADC 到 Gleason 映射、异质剂量分布的传递稳健性问题，以及临床试验设计的患者选择标准。
