Permuth Jennifer B, Vyas Shraddha, Li Jiannong, Chen Dung-Tsa, Jeong Daniel, Choi Jung W
Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States.
Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States.
Front Oncol. 2021 Jul 22;11:712950. doi: 10.3389/fonc.2021.712950. eCollection 2021.
Significant racial disparities in pancreatic cancer incidence and mortality rates exist, with the highest rates in African Americans compared to Non-Hispanic Whites and Hispanic/Latinx populations. Computer-derived quantitative imaging or "radiomic" features may serve as non-invasive surrogates for underlying biological factors and heterogeneity that characterize pancreatic tumors from African Americans, yet studies are lacking in this area. The objective of this pilot study was to determine if the radiomic tumor profile extracted from pretreatment computed tomography (CT) images differs between African Americans, Non-Hispanic Whites, and Hispanic/Latinx with pancreatic cancer.
We evaluated a retrospective cohort of 71 pancreatic cancer cases (23 African American, 33 Non-Hispanic White, and 15 Hispanic/Latinx) who underwent pretreatment CT imaging at Moffitt Cancer Center and Research Institute. Whole lesion semi-automated segmentation was performed on each slice of the lesion on all pretreatment venous phase CT exams using Healthmyne Software (Healthmyne, Madison, WI, USA) to generate a volume of interest. To reduce feature dimensionality, 135 highly relevant non-texture and texture features were extracted from each segmented lesion and analyzed for each volume of interest.
Thirty features were identified and significantly associated with race/ethnicity based on Kruskal-Wallis test. Ten of the radiomic features were highly associated with race/ethnicity independent of tumor grade, including sphericity, volumetric mean Hounsfield units (HU), minimum HU, coefficient of variation HU, four gray level texture features, and two wavelet texture features. A radiomic signature summarized by the first principal component partially differentiated African American from non-African American tumors (area underneath the curve = 0.80). Poorer survival among African Americans compared to Non-African Americans was observed for tumors with lower volumetric mean CT [HR: 3.90 (95% CI:1.19-12.78), p=0.024], lower GLCM Avg Column Mean [HR:4.75 (95% CI: 1.44,15.37), p=0.010], and higher GLCM Cluster Tendency [HR:3.36 (95% CI: 1.06-10.68), p=0.040], and associations persisted in volumetric mean CT and GLCM Avg Column after adjustment for key clinicopathologic factors.
This pilot study identified several textural radiomics features associated with poor overall survival among African Americans with PDAC, independent of other prognostic factors such as grade. Our findings suggest that CT radiomic features may serve as surrogates for underlying biological factors and add value in predicting clinical outcomes when integrated with other parameters in ongoing and future studies of cancer health disparities.
胰腺癌的发病率和死亡率存在显著的种族差异，非西班牙裔白人和西班牙裔/拉丁裔人群相比，非裔美国人的发病率和死亡率最高。计算机衍生的定量成像或“放射组学”特征可能作为潜在生物学因素和异质性的非侵入性替代指标，这些因素和异质性是非洲裔美国人胰腺肿瘤的特征，但该领域的研究尚缺。本初步研究的目的是确定从治疗前计算机断层扫描（CT）图像中提取的放射组学肿瘤特征在患有胰腺癌的非裔美国人、非西班牙裔白人和西班牙裔/拉丁裔之间是否存在差异。
我们评估了在莫菲特癌症中心和研究所接受治疗前CT成像的71例胰腺癌病例的回顾性队列（23例非裔美国人、33例非西班牙裔白人、15例西班牙裔/拉丁裔）。使用Healthmyne软件（美国威斯康星州麦迪逊市的Healthmyne公司）对所有治疗前静脉期CT检查的病变每一层进行全病变半自动分割，以生成感兴趣体积。为了降低特征维度，从每个分割病变中提取135个高度相关的非纹理和纹理特征，并对每个感兴趣体积进行分析。
基于Kruskal-Wallis检验，确定了30个特征并与种族/民族显著相关。其中10个放射组学特征与种族/民族高度相关，与肿瘤分级无关，包括球形度、体积平均亨氏单位（HU）、最小HU、HU变异系数、4个灰度纹理特征和2个小波纹理特征。由第一主成分总结的放射组学特征部分区分了非裔美国人和非非裔美国人的肿瘤（曲线下面积=0.80）。对于体积平均CT较低的肿瘤[风险比：3.90（95%置信区间：1.19-12.78），p=0.024]、灰度共生矩阵平均列均值较低的肿瘤[风险比：4.75（95%置信区间：1.44,15.37），p=0.010]以及灰度共生矩阵聚类倾向较高的肿瘤[风险比：3.36（95%置信区间：1.06-10.68），p=0.040]，观察到非裔美国人的生存率低于非非裔美国人，在调整关键临床病理因素后，体积平均CT和灰度共生矩阵平均列均值的关联仍然存在。
本初步研究确定了几个与胰腺导管腺癌（PDAC）非裔美国人总体生存率差相关的纹理放射组学特征，独立于其他预后因素，如分级。我们的研究结果表明，CT放射组学特征可作为潜在生物学因素的替代指标，并在癌症健康差异的正在进行和未来研究中与其他参数整合时，在预测临床结果方面具有附加价值。
