Zhang Pengpeng, Rimner Andreas, Yorke Ellen, Hu Yu-Chi, Kuo Licheng, Apte Aditya, Lockney Natalie, Jackson Andrew, Mageras Gig, Deasy Joseph O
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Phys Med Biol. 2017 Jan 10;62(3):702-714. doi: 10.1088/1361-6560/aa54f9.
To develop a geometric atlas that can predict tumor shrinkage and guide treatment planning for non-small-cell lung cancer. To evaluate the impact of the shrinkage atlas on the ability of tumor dose escalation. The creation of a geometric atlas included twelve patients with lung cancer who underwent both planning CT and weekly CBCT for radiotherapy planning and delivery. The shrinkage pattern from the original pretreatment to the residual posttreatment tumor was modeled using a principal component analysis, and used for predicting the spatial distribution of the residual tumor. A predictive map was generated by unifying predictions from each individual patient in the atlas, followed by correction for the tumor's surrounding tissue distribution. Sensitivity, specificity, and accuracy of the predictive model for classifying voxels inside the original gross tumor volume were evaluated. In addition, a retrospective study of predictive treatment planning (PTP) escalated dose to the predicted residual tumor while maintaining the same level of predicted complication rates for a clinical plan delivering uniform dose to the entire tumor. The effect of uncertainty on the predictive model's ability to escalate dose was also evaluated. The sensitivity, specificity and accuracy of the predictive model were 0.73, 0.76, and 0.74, respectively. The area under the receiver operating characteristic curve for voxel classification was 0.87. The Dice coefficient and mean surface distance between the predicted and actual residual tumor averaged 0.75, and 1.6 mm, respectively. The PTP approach allowed elevation of PTV D95 and mean dose to the actual residual tumor by 6.5 Gy and 10.4 Gy, respectively, relative to the clinical uniform dose approach. A geometric atlas can provide useful information on the distribution of resistant tumors and effectively guide dose escalation to the tumor without compromising the organs at risk complications. The atlas can be further refined by using more patient data sets.
开发一个能够预测肿瘤缩小并指导非小细胞肺癌治疗计划的几何图谱。评估缩小图谱对肿瘤剂量递增能力的影响。几何图谱的创建纳入了12例肺癌患者，这些患者接受了用于放疗计划和实施的计划CT及每周一次的CBCT。使用主成分分析对从初始治疗前到治疗后残留肿瘤的缩小模式进行建模，并用于预测残留肿瘤的空间分布。通过统一图谱中每个患者的预测结果，生成预测图，随后对肿瘤周围组织分布进行校正。评估了预测模型对原始大体肿瘤体积内体素分类的敏感性、特异性和准确性。此外，进行了一项预测性治疗计划（PTP）的回顾性研究，在保持临床计划对整个肿瘤给予均匀剂量时预测并发症发生率相同水平的情况下，对预测的残留肿瘤递增剂量。还评估了不确定性对预测模型剂量递增能力的影响。预测模型的敏感性、特异性和准确性分别为0.73、0.76和0.74。体素分类的受试者操作特征曲线下面积为0.87。预测的和实际的残留肿瘤之间的Dice系数和平均表面距离分别平均为0.75和1.6毫米。相对于临床均匀剂量方法，PTP方法使计划靶体积D95和实际残留肿瘤的平均剂量分别提高了6.5 Gy和10.4 Gy。一个几何图谱可以提供关于耐药肿瘤分布的有用信息，并有效地指导对肿瘤的剂量递增，同时不影响危及器官的并发症。通过使用更多患者数据集，该图谱可进一步完善。
