The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules

医学 无线电技术 腺癌 接收机工作特性 放射科 肺癌 逻辑回归 队列 神经组阅片室 回顾性队列研究 机构审查委员会 全国肺筛查试验 内科学 癌症 肺癌筛查 外科 精神科 神经学
作者
Yunlang She,Lei Zhang,Huanhuan Zhu,Chenyang Dai,Dong Xie,Huikang Xie,Wei Zhang,Lilan Zhao,Liling Zou,Ke Fei,Xuejun Sun,Chang Chen
出处
期刊:European Radiology [Springer Nature]
卷期号:28 (12): 5121-5128 被引量:88
标识
DOI:10.1007/s00330-018-5509-9
摘要

Adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are assumed to be indolent lung adenocarcinoma with excellent prognosis. We aim to identify these lesions from invasive adenocarcinoma (IA) by a radiomics approach. This retrospective study was approved by institutional review board with a waiver of informed consent. Pathologically confirmed lung adenocarcinomas manifested as lung nodules less than 3 cm were retrospectively identified. In-house software was used to quantitatively extract 60 CT-based radiomics features quantifying nodule’s volume, intensity and texture property through manual segmentation. In order to differentiate AIS/MIA from IA, least absolute shrinkage and selection operator (LASSO) logistic regression was used for feature selection and developing radiomics signatures. The predictive performance of the signature was evaluated via receiver operating curve (ROC) and calibration curve, and validated using an independent cohort. 402 eligible patients were included and divided into the primary cohort (n = 207) and the validation cohort (n = 195). Using the primary cohort, we developed a radiomics signature based on five radiomics features. The signature showed good discrimination between MIA/AIS and IA in both the primary and validation cohort, with AUCs of 0.95 (95% CI, 0.91–0.98) and 0.89 (95% CI, 0.84–0.93), respectively. Multivariate logistic analysis revealed that the signature (OR, 13.3; 95% CI, 6.2–28.5; p < 0.001) and gender (OR, 3.5; 95% CI, 1.2–10.9; p = 0.03) were independent predictors of indolent lung adenocarcinoma. The signature based on radiomics features helps to differentiate indolent from invasive lung adenocarcinoma, which might be useful in guiding the intervention choice for patients with pulmonary nodules. • Based on radiomics features, a signature is established to differentiate adenocarcinoma in situ and minimally invasive adenocarcinoma from invasive lung adenocarcinoma.
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