The Department of Radiology, The Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen City, Guangdong Province, China; School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin City, Guangxi Province, China.
The Department of Radiology, The Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen City, Guangdong Province, China.
Clin Radiol. 2019 Jul;74(7):570.e1-570.e11. doi: 10.1016/j.crad.2019.03.018. Epub 2019 May 2.
To evaluate the preoperative differentiation between the minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) in patients with sub-solid pulmonary nodules using a radiomics nomogram.
A total of 100 patients with sub-solid pulmonary nodules who had pathologically confirmed MIA (43 patients, 13 male and 30 female) or IAC (57 patients, 26 male and 31 female) were recruited retrospectively. Radiomics features were extracted from computed tomography (CT) images. A radiomics signature was constructed by the least absolute shrinkage and selection operator (LASSO) algorithm. Solid presence, lesion size, shape regularity, and margins of pulmonary nodules were assessed to construct a subjective finding model. An integrated model of radiomics signatures and CT-based subjective findings, which was presented as a radiomics nomogram, was developed based on a multivariate logistic regression. The nomogram performance was assessed by its calibration, discrimination, and clinical usefulness.
The radiomics signature, which consisted of 11 radiomics features, showed good discrimination accuracy. The radiomics nomogram showed good calibration and discrimination in the training set (AUC [area under the curve] 0.943; 95% confidence interval [CI]: 0.895-0.991) and validation set (AUC 0.912; 95% CI: 0.780-1.000). The radiomics nomogram was determined to be clinically useful in the decision curve analysis (DCA).
The proposed radiomics nomogram has the potential to preoperatively differentiate MIA and IAC in patients with sub-solid pulmonary nodules.
利用放射组学列线图评估亚实性肺结节患者的微创腺癌（MIA）和浸润性腺癌（IAC）的术前差异。
回顾性分析了 100 例经病理证实为 MIA（43 例，男 13 例，女 30 例）或 IAC（57 例，男 26 例，女 31 例）的亚实性肺结节患者。从 CT 图像中提取放射组学特征。采用最小绝对值收缩和选择算子（LASSO）算法构建放射组学特征。评估实性存在、病变大小、形状规则性和肺结节边缘，以构建主观发现模型。基于多变量逻辑回归，建立了放射组学特征和基于 CT 的主观发现的综合模型，即放射组学列线图。通过校准、鉴别和临床实用性评估该列线图的性能。
由 11 个放射组学特征组成的放射组学特征具有良好的鉴别准确性。在训练集（AUC [曲线下面积] 0.943；95%置信区间[CI]：0.895-0.991）和验证集（AUC 0.912；95%CI：0.780-1.000）中，放射组学列线图均表现出良好的校准和鉴别能力。在决策曲线分析（DCA）中，确定放射组学列线图具有临床实用性。
所提出的放射组学列线图有可能在术前区分亚实性肺结节患者的 MIA 和 IAC。
