A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarcinoma.
A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarcinoma.
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DOI：
10.1007/s00330-020-06663-6
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发表时间：
2020-07
影响因子：
5.9
通讯作者：
中科院分区：
文献类型：
--
作者：
组织学亚组分类对肺腺癌手术切除患者的预后有重要价值。然而，临床组织病理学评估通常仅对来自活检或手术的整体肿瘤的一小部分进行。我们的目的是确定一个非侵入性的定量成像生物标志物（QIB）的肺腺癌患者的组织学亚组的分类。我们回顾性地收集并分析了2014年1月至2017年10月期间来自两个地理位置遥远的机构的1313例肺腺癌切除患者的CT扫描。建立了三个研究队列，即训练、内部验证和外部验证队列，其中肺腺癌被分为两个无病生存期（DFS）相关的组织学亚组，即中/差DFS组和良好DFS组。采用了一个全面的基于机器学习和深度学习的分析系统来识别可重复的QIB，并帮助理解QIB的重要性。强度-偏度，量化肿瘤密度分布的QIB，被确定为预测组织学亚组的最佳生物标志物。在训练、内部验证和外部验证队列中，强度-偏度分别达到了0.849（0.813，0.881）、0.820（0.781，0.856）和0.863（0.827，0.895）的高AUC（95%CI）。强度-偏度≤ 1.5的标准表明高肿瘤密度，在训练和外部验证队列中，预测中/差DFS组的特异性分别为96%（灵敏度46%）和99%（灵敏度53%）。来自常规CT的QIB能够预测肺腺癌组织学亚组，提供了一种非侵入性方法，可能有利于肺癌患者的个性化治疗决策。
Classification of histologic subgroups has significant prognostic value for lung adenocarcinoma patients who undergo surgical resection. However, clinical histopathology assessment is generally performed on only a small portion of the overall tumor from biopsy or surgery. Our objective is to identify a noninvasive quantitative imaging biomarker (QIB) for the classification of histologic subgroups in lung adenocarcinoma patients. We retrospectively collected and reviewed 1313 CT scans of patients with resected lung adenocarcinomas from two geographically distant institutions who were seen between January 2014 and October 2017. Three study cohorts, the training, internal validation, and external validation cohorts, were created, within which lung adenocarcinomas were divided into two disease-free-survival (DFS)-associated histologic subgroups, the mid/poor and good DFS groups. A comprehensive machine learning– and deep learning–based analytical system was adopted to identify reproducible QIBs and help to understand QIBs’ significance. Intensity-Skewness, a QIB quantifying tumor density distribution, was identified as the optimal biomarker for predicting histologic subgroups. Intensity-Skewness achieved high AUCs (95% CI) of 0.849(0.813,0.881), 0.820(0.781,0.856) and 0.863(0.827,0.895) on the training, internal validation, and external validation cohorts, respectively. A criterion of Intensity-Skewness ≤ 1.5, which indicated high tumor density, showed high specificity of 96% (sensitivity 46%) and 99% (sensitivity 53%) on predicting the mid/poor DFS group in the training and external validation cohorts, respectively. A QIB derived from routinely acquired CT was able to predict lung adenocarcinoma histologic subgroups, providing a noninvasive method that could potentially benefit personalized treatment decision-making for lung cancer patients.
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DOI：
10.1016/j.radonc.2016.04.004
发表时间：
2016-06
期刊：
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
影响因子：
--
作者：
Coroller TP;Agrawal V;Narayan V;Hou Y;Grossmann P;Lee SW;Mak RH;Aerts HJ
通讯作者：
Aerts HJ
影响因子：
4.6
作者：
Nomori, H;Ohtsuka, T;Suemasu, K
通讯作者：
Suemasu, K
DOI：
10.1016/j.bbrc.2007.04.097
发表时间：
2007-06-22
影响因子：
3.1
作者：
Liu, Boshu;Li, Sujun;Cai, Yudong
通讯作者：
Cai, Yudong
影响因子：
0.4
作者：
Travis, William D.
通讯作者：
Travis, William D.
影响因子：
19.5
作者：
Russakovsky, Olga;Deng, Jia;Fei-Fei, Li
通讯作者：
Fei-Fei, Li