肺腺癌预后的全面 CT 影像学组学分析。
Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication.
机构信息
Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Korea.
出版信息
Oncologist. 2018 Jul;23(7):806-813. doi: 10.1634/theoncologist.2017-0538. Epub 2018 Apr 5.
BACKGROUND
In this era of personalized medicine, there is an expanded demand for advanced imaging biomarkers that reflect the biology of the whole tumor. Therefore, we investigated a large number of computed tomography-derived radiomics features along with demographics and pathology-related variables in patients with lung adenocarcinoma, correlating them with overall survival.
MATERIALS AND METHODS
Three hundred thirty-nine patients who underwent operation for lung adenocarcinoma were included. Analysis was performed using 161 radiomics features, demographic, and pathologic variables and correlated each with patient survival. Prognostic performance for survival was compared among three models: (a) using only clinicopathological data; (b) using only selected radiomics features; and (c) using both clinicopathological data and selected radiomics features.
RESULTS
At multivariate analysis, age, pN, tumor size, type of operation, histologic grade, maximum value of the outer 1/3 of the tumor, and size zone variance were statistically significant variables. In particular, maximum value of outer 1/3 of the tumor reflected tumor microenvironment, and size zone variance represented intratumor heterogeneity. Integration of 31 selected radiomics features with clinicopathological variables led to better discrimination performance.
CONCLUSION
Radiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and has potential to improve prognosis assessment in clinical oncology.
IMPLICATIONS FOR PRACTICE
Two radiomics features were prognostic for lung cancer survival at multivariate analysis: (a) maximum value of the outer one third of the tumor reflects the tumor microenvironment and (b) size zone variance represents the intratumor heterogeneity. Therefore, a radiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and could play a larger role in clinical oncology.
背景
在个性化医学时代，人们对反映整个肿瘤生物学的高级成像生物标志物的需求不断增加。因此，我们研究了大量源自计算机断层扫描的放射组学特征，以及肺腺癌患者的人口统计学和病理学相关变量，并将其与总生存期相关联。
材料和方法
共纳入 339 例接受肺腺癌手术的患者。使用 161 个放射组学特征、人口统计学和病理学变量进行分析，并将每个变量与患者的生存情况相关联。使用三种模型比较生存预后的预测性能：（a）仅使用临床病理数据；（b）仅使用选定的放射组学特征；（c）同时使用临床病理数据和选定的放射组学特征。
结果
在多变量分析中，年龄、pN、肿瘤大小、手术类型、组织学分级、肿瘤外 1/3 的最大值和大小区方差是统计学显著的变量。特别是，肿瘤外 1/3 的最大值反映了肿瘤微环境，而大小区方差代表了肿瘤内异质性。将 31 个选定的放射组学特征与临床病理变量相结合，可以提高鉴别性能。
结论
肺腺癌的放射组学方法能够充分利用医学影像学的潜力，有可能改善临床肿瘤学的预后评估。
实践意义
在多变量分析中，有两个放射组学特征与肺癌的生存相关：（a）肿瘤外 1/3 的最大值反映了肿瘤微环境，（b）大小区方差代表了肿瘤内异质性。因此，肺腺癌的放射组学方法能够充分利用医学影像学的潜力，并在临床肿瘤学中发挥更大的作用。
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