OBJECTIVES 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. METHODS 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. RESULTS 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. CONCLUSIONS 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. KEY POINTS • A noninvasive imaging biomarker, Intensity-Skewness, which described the distortion of pixel-intensity distribution within lesions on CT images, was identified as a biomarker to predict disease-free-survival-associated histologic subgroups in lung adenocarcinoma. • An Intensity-Skewness of ≤ 1.5 has high specificity in predicting the mid/poor disease-free survival histologic patient group in both the training cohort and the external validation cohort. • The Intensity-Skewness is a feature that can be automatically computed with high reproducibility and robustness.

中文翻译：

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用于预测肺腺癌中无病生存相关组织学亚组的定量成像生物标志物。

目的 组织学亚组分类对接受手术切除的肺腺癌患者具有重要的预后价值。然而，临床组织病理学评估通常仅对来自活检或手术的整个肿瘤的一小部分进行。我们的目标是确定用于肺腺癌患者组织学亚组分类的无创定量成像生物标志物 (QIB)。方法 我们回顾性收集和回顾了 2014 年 1 月至 2017 年 10 月期间来自两个相距遥远的机构的 1313 例切除肺腺癌患者的 CT 扫描。创建了三个研究队列，培训、内部验证和外部验证队列，其中肺腺癌分为两个无病生存（DFS）相关的组织学亚组，中/差和良好DFS组。采用基于机器学习和深度学习的综合分析系统来识别可重复的 QIB 并帮助了解 QIB 的重要性。结果 强度偏度是一种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。Intensity-Skewness 在训练、内部验证和外部验证队列中分别实现了 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，表明肿瘤密度高，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%（敏感性 46%）和 99%（敏感性 53%）。结论 源自常规 C​​T 的 QIB 能够预测肺腺癌组织学亚组，提供了一种可能有利于肺癌患者个性化治疗决策的非侵入性方法。要点 • 一种无创成像生物标志物Intensity-Skewness 描述了CT 图像上病灶内像素强度分布的失真，被确定为预测肺腺癌中无病生存相关组织学亚组的生物标志物。• ≤ 1 的强度偏度。5 在预测训练队列和外部验证队列中的中/差无病生存组织学患者组方面具有高度特异性。• Intensity-Skewness 是一种可以自动计算且具有高重现性和鲁棒性的特征。