Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method
Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method
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DOI：
10.1016/j.ejmp.2019.03.024
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发表时间：
2019-04-01
影响因子：
3.4
通讯作者：
Smedby, Orjan
中科院分区：
文献类型：
--
作者：
Astaraki, Mehdi;Wang, Chunliang;Smedby, Orjan
目的：探讨一种新的定量特征集描述肺癌患者同时和序贯放化疗治疗的肿瘤内异质性的预后和预测价值。方法：分析30例非小细胞肺癌患者的纵向PET-CT图像。为了描述肿瘤细胞的异质性，根据肿瘤的大小将其划分为一到十个同心区域，并且对于每个区域，分别计算PET和CT图像的两次扫描之间的平均强度变化，以形成所提出的特征集。为了验证所提出的方法的预后价值，进行放射组学分析，并评估所提出的新特征集和经典放射组学特征的组合。利用特征选择算法来识别最佳特征，并训练线性支持向量机用于根据受试者工作特征曲线下面积（AUROC）预测总生存期。发现提出的新特征集具有预后作用，甚至优于放射组学方法，且具有显着差异（AUROC（sALop）= 0.90 vs. AUROC（radiomic）= 0.71），而在特征选择的情况下，新特征集和放射组学的组合导致最高的预后值。根据其预后能力判断，所提出的特征对于早期生存预测具有很大的潜力。
Purpose: To explore prognostic and predictive values of a novel quantitative feature set describing intra-tumor heterogeneity in patients with lung cancer treated with concurrent and sequential chemoradiotherapy.Methods: Longitudinal PET-CT images of 30 patients with non-small cell lung cancer were analysed. To describe tumor cell heterogeneity, the tumors were partitioned into one to ten concentric regions depending on their sizes, and, for each region, the change in average intensity between the two scans was calculated for PET and CT images separately to form the proposed feature set. To validate the prognostic value of the proposed method, radiomics analysis was performed and a combination of the proposed novel feature set and the classic radiomic features was evaluated. A feature selection algorithm was utilized to identify the optimal features, and a linear support vector machine was trained for the task of overall survival prediction in terms of area under the receiver operating characteristic curve (AUROC).Results: The proposed novel feature set was found to be prognostic and even outperformed the radiomics approach with a significant difference (AUROC(sALop) = 0.90 vs. AUROC(radiomic) = 0.71) when feature selection was not employed, whereas with feature selection, a combination of the novel feature set and radiomics led to the highest prognostic values.Conclusion: A novel feature set designed for capturing intra-tumor heterogeneity was introduced. Judging by their prognostic power, the proposed features have a promising potential for early survival prediction.