Alpert Jeffrey B, Rusinek Henry, Ko Jane P, Dane Bari, Pass Harvey I, Crawford Bernard K, Rapkiewicz Amy, Naidich David P
Department of Radiology, New York University Langone Medical Center, 660 First Avenue, 3rd Floor, New York, NY 10016.
Department of Radiology, New York University Langone Medical Center, 660 First Avenue, 3rd Floor, New York, NY 10016.
Acad Radiol. 2017 Dec;24(12):1604-1611. doi: 10.1016/j.acra.2017.07.008. Epub 2017 Aug 24.
This study aimed to differentiate pathologically defined lepidic predominant lesions (LPL) from more invasive adenocarcinomas (INV) using three-dimensional (3D) volumetric density and first-order texture histogram analysis of surgically excised stage 1 lung adenocarcinomas.
This retrospective study was institutional review board approved and Health Insurance Portability and Accountability Act compliant. Sixty-four cases of pathologically proven stage 1 lung adenocarcinoma surgically resected between September 2006 and October 2015, including LPL (n = 43) and INV (n = 21), were evaluated using high-resolution computed tomography. Quantitative measurements included nodule volume, percent solid volume (% solid), and first-order texture histogram analysis including skewness, kurtosis, entropy, and mean nodule attenuation within each histogram quartile. Binomial logistic regression models were used to identify the best set of parameters distinguishing LPL from INV.
Univariate analysis of 3D volumetric density and histogram features was statistically significant between LPL and INV groups (P < .05). Accuracy of a binomial logistic model to discriminate LPL from INV based on size and % solid was 85.9%. With optimized probability cutoff, the model achieves 81% sensitivity, 76.7% specificity, and area under the receiver operating characteristic curve of 0.897 (95% confidence interval, 0.821-0.973). An additional model based on size and mean nodule attenuation of the third quartile (Hu_Q3) of the histogram achieved similar accuracy of 81.3% and area under the receiver operating characteristic curve of 0.877 (95% confidence interval, 0.790-0.964).
Both 3D volumetric density and first-order texture analysis of stage 1 lung adenocarcinoma allow differentiation of LPL from more invasive adenocarcinoma with overall accuracy of 85.9%-81.3%, based on multivariate analyses of either size and % solid or size and Hu_Q3, respectively.
本研究旨在通过对手术切除的Ⅰ期肺腺癌进行三维（3D）体积密度和一阶纹理直方图分析，从更具侵袭性的腺癌（INV）中鉴别出病理定义的鳞屑样为主型病变（LPL）。
本回顾性研究经机构审查委员会批准，并符合《健康保险流通与责任法案》。对2006年9月至2015年10月期间手术切除的64例经病理证实的Ⅰ期肺腺癌病例进行评估，其中包括LPL（n = 43）和INV（n = 21），采用高分辨率计算机断层扫描。定量测量包括结节体积、实性体积百分比（%实性），以及一阶纹理直方图分析，包括偏度、峰度、熵和每个直方图四分位数内的平均结节衰减。使用二项逻辑回归模型确定区分LPL和INV的最佳参数集。
LPL组和INV组之间3D体积密度和直方图特征的单变量分析具有统计学意义（P < 0.05）。基于大小和%实性的二项逻辑模型区分LPL和INV的准确率为85.9%。通过优化概率截断值，该模型的灵敏度为81%，特异度为76.7%，受试者操作特征曲线下面积为0.897（95%置信区间，0.821 - 0.973）。另一个基于大小和直方图第三四分位数（Hu_Q3）的平均结节衰减的模型达到了相似的准确率81.3%，受试者操作特征曲线下面积为0.877（95%置信区间，0.790 - 0.964）。
基于大小和%实性或大小和Hu_Q3的多变量分析，Ⅰ期肺腺癌的3D体积密度和一阶纹理分析均可将LPL与更具侵袭性的腺癌区分开来，总体准确率分别为85.9% - 81.3%。
