Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, 660 First Avenue, New York, NY, 10016, USA.
NYU Langone Health, New York, NY, USA.
Sci Rep. 2022 Jul 12;12(1):11813. doi: 10.1038/s41598-022-15351-y.
Quantitative radiomic and iodine imaging features have been explored for diagnosis and characterization of tumors. In this work, we invistigate combined whole-lesion radiomic and iodine analysis for the differentiation of pulmonary tumors on contrast-enhanced dual-energy CT (DECT) chest images. 100 biopsy-proven solid lung lesions on contrast-enhanced DECT chest exams within 3 months of histopathologic sampling were identified. Lesions were volumetrically segmented using open-source software. Lesion segmentations and iodine density volumes were loaded into a radiomics prototype for quantitative analysis. Univariate analysis was performed to determine differences in volumetric iodine concentration (mean, median, maximum, minimum, 10th percentile, 90th percentile) and first and higher order radiomic features (n = 1212) between pulmonary tumors. Analyses were performed using a 2-sample t test, and filtered for false discoveries using Benjamini-Hochberg method. 100 individuals (mean age 65 ± 13 years; 59 women) with 64 primary and 36 metastatic lung lesions were included. Only one iodine concentration parameter, absolute minimum iodine, significantly differed between primary and metastatic pulmonary tumors (FDR-adjusted p = 0.015, AUC 0.69). 310 (FDR-adjusted p = 0.0008 to p = 0.0491) radiomic features differed between primary and metastatic lung tumors. Of these, 21 features achieved AUC ≥ 0.75. In subset analyses of lesions imaged by non-CTPA protocol (n = 72), 191 features significantly differed between primary and metastatic tumors, 19 of which achieved AUC ≥ 0.75. In subset analysis of tumors without history of prior treatment (n = 59), 40 features significantly differed between primary and metastatic tumors, 11 of which achieved AUC ≥ 0.75. Volumetric radiomic analysis provides differentiating capability beyond iodine quantification. While a high number of radiomic features differentiated primary versus metastatic pulmonary tumors, fewer features demonstrated good individual discriminatory utility.
定量放射组学和碘成像特征已被用于肿瘤的诊断和特征描述。在这项工作中，我们研究了联合全病变放射组学和碘分析，以区分对比增强双能 CT（DECT）胸部图像上的肺部肿瘤。在组织病理学取样后 3 个月内，对对比增强 DECT 胸部检查中经活检证实的 100 个实性肺部病变进行了识别。使用开源软件对病变进行容积分割。将病变分割和碘密度体积加载到放射组学原型中进行定量分析。进行单变量分析以确定肺部肿瘤之间容积碘浓度（平均值、中位数、最大值、最小值、第 10 百分位数、第 90 百分位数）和一阶和高阶放射组学特征（n=1212）的差异。使用双样本 t 检验进行分析，并使用 Benjamini-Hochberg 方法进行虚假发现过滤。共纳入 100 名个体（平均年龄 65±13 岁；59 名女性），其中 64 例为原发性肺癌，36 例为转移性肺癌。只有一个碘浓度参数，绝对最小碘浓度，在原发性和转移性肺肿瘤之间有显著差异（FDR 调整后 p=0.015，AUC 0.69）。原发性和转移性肺肿瘤之间有 310 个（FDR 调整后 p=0.0008 至 p=0.0491）放射组学特征存在差异。其中，21 个特征的 AUC≥0.75。在非 CTPA 协议成像的病变亚组分析中（n=72），原发性和转移性肿瘤之间有 191 个特征有显著差异，其中 19 个特征的 AUC≥0.75。在无既往治疗史的肿瘤亚组分析中（n=59），原发性和转移性肿瘤之间有 40 个特征有显著差异，其中 11 个特征的 AUC≥0.75。容积放射组学分析提供了比碘定量更好的区分能力。虽然大量的放射组学特征可以区分原发性和转移性肺肿瘤，但只有少数特征具有良好的个体鉴别能力。
