Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study.
Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study.
复制标题
DOI：
10.1016/j.ijrobp.2016.03.018
复制
发表时间：
2016-08-01
期刊：
影响因子：
--
通讯作者：
Li R
中科院分区：
文献类型：
--
作者：
Wu J;Gensheimer MF;Dong X;Rubin DL;Napel S;Diehn M;Loo BW Jr;Li R
开发一种肿瘤内分区框架，用于从18 F-氟脱氧葡萄糖正电子发射断层扫描（FDG-PET）和CT成像中识别高风险亚区，并测试与高风险亚区相关的肿瘤负荷是否是肺癌预后的预后指标。在这项机构审查委员会批准的回顾性研究中，我们分析了44例接受放射治疗的肺癌患者的治疗前FDG-PET和CT扫描。基于两阶段聚类过程开发了一种新的肿瘤内分区方法：首先在患者水平上，通过对集成PET和CT图像的k均值聚类将每个肿瘤过度分割成许多超像素;接下来，通过人口水平的分层聚类合并先前定义的超像素来识别肿瘤子区域。使用Kaplan-Meier分析评价与每个亚区相关的体积，以评估其预测总生存期（OS）和场外进展（OFP）的预后能力。在每个肿瘤内识别出三个空间上不同的子区域，这对PET/CT共配准中的不确定性具有高度鲁棒性。其中，肿瘤中最具代谢活性和代谢异质性的实体成分的体积可预测整个队列的OS和OFP，一致性指数或CI = 0.66-0.67。当将分析限制在III期疾病患者（n = 32）时，同一亚区预测OS的CI = 0.75（HR = 3.93，logrank p = 0.002）甚至更高，预测OFP的CI = 0.76（HR = 4.84，logrank p = 0.002）。相比之下，常规成像标记物包括肿瘤体积、SUVmax和MTV 50不能预测OS或OFP，CI大多低于0.60（对数秩p > 0.05）。我们提出了一个强大的肿瘤内分区方法，以确定临床相关的，高风险的肺癌亚区。我们设想这种方法将适用于在许多癌症类型中识别有用的成像生物标志物。
To develop an intra-tumor partitioning framework for identifying high-risk subregions from 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and CT imaging, and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer. In this institutional review board-approved retrospective study, we analyzed the pre-treatment FDG-PET and CT scans of 44 lung cancer patients treated with radiotherapy. A novel, intra-tumor partitioning method was developed based on a two-stage clustering process: first at patient-level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET and CT images; next, tumor subregions were identified by merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP). Three spatially distinct subregions were identified within each tumor, which were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI = 0.66–0.67. When restricting the analysis to patients with stage III disease (n = 32), the same subregion achieved an even higher CI = 0.75 (HR = 3.93, logrank p = 0.002) for predicting OS, and a CI = 0.76 (HR = 4.84, logrank p = 0.002) for predicting OFP. In comparison, conventional imaging markers including tumor volume, SUVmax and MTV50 were not predictive of OS or OFP, with CI mostly below 0.60 (logrank p > 0.05). We propose a robust intra-tumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types.