Luna José Marcio, Barsky Andrew R, Shinohara Russell T, Roshkovan Leonid, Hershman Michelle, Dreyfuss Alexandra D, Horng Hannah, Lou Carolyn, Noël Peter B, Cengel Keith A, Katz Sharyn, Diffenderfer Eric S, Kontos Despina
Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Department of Radiology, University of Pennsylvania, Philadelphia, PA 19103, USA.
Cancers (Basel). 2022 Jan 29;14(3):700. doi: 10.3390/cancers14030700.
We evaluate radiomic phenotypes derived from CT scans as early predictors of overall survival (OS) after chemoradiation in stage III primary lung adenocarcinoma. We retrospectively analyzed 110 thoracic CT scans acquired between April 2012-October 2018. Patients received a median radiation dose of 66.6 Gy at 1.8 Gy/fraction delivered with proton (55.5%) and photon (44.5%) beam treatment, as well as concurrent chemotherapy (89%) with carboplatin-based (55.5%) and cisplatin-based (36.4%) doublets. A total of 56 death events were recorded. Using manual tumor segmentations, 107 radiomic features were extracted. Feature harmonization using ComBat was performed to mitigate image heterogeneity due to the presence or lack of intravenous contrast material and variability in CT scanner vendors. A binary radiomic phenotype to predict OS was derived through the unsupervised hierarchical clustering of the first principal components explaining 85% of the variance of the radiomic features. C-scores and likelihood ratio tests (LRT) were used to compare the performance of a baseline Cox model based on ECOG status and age, with a model integrating the radiomic phenotype with such clinical predictors. The model integrating the radiomic phenotype (C-score = 0.69, 95% CI = (0.62, 0.77)) significantly improved (p<0.005) upon the baseline model (C-score = 0.65, CI = (0.57, 0.73)). Our results suggest that harmonized radiomic phenotypes can significantly improve OS prediction in stage III NSCLC after chemoradiation.
我们评估了源自CT扫描的放射组学表型，作为III期原发性肺腺癌放化疗后总生存期（OS）的早期预测指标。我们回顾性分析了2012年4月至2018年10月期间获取的110份胸部CT扫描。患者接受的中位放射剂量为66.6 Gy，以1.8 Gy/分次给予质子束（55.5%）和光子束（44.5%）治疗，同时接受以卡铂为基础（55.5%）和顺铂为基础（36.4%）的双联化疗（89%）。共记录了56例死亡事件。使用手动肿瘤分割，提取了107个放射组学特征。采用ComBat进行特征归一化，以减轻由于静脉造影剂的存在或缺乏以及CT扫描仪供应商的差异导致的图像异质性。通过对解释放射组学特征85%方差的第一主成分进行无监督层次聚类，得出预测OS的二元放射组学表型。使用C分数和似然比检验（LRT）比较基于ECOG状态和年龄的基线Cox模型与整合放射组学表型和此类临床预测指标的模型的性能。整合放射组学表型的模型（C分数 = 0.69，95% CI =（0.62，0.77））相较于基线模型（C分数 = 0.65，CI =（0.57，0.73））有显著改善（p<0.005）。我们的结果表明，归一化的放射组学表型可显著改善III期非小细胞肺癌放化疗后的OS预测。
