Mao Liting, Chen Huan, Liang Mingzhu, Li Kunwei, Gao Jiebing, Qin Peixin, Ding Xianglian, Li Xin, Liu Xueguo
Department of Radiology, The 5 Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, China.
Department of Radiology, The Second Affiliated Hosptial of Guangzhou University of Traditional Chinese Medicine, Guangzhou 510120, China.
Quant Imaging Med Surg. 2019 Feb;9(2):263-272. doi: 10.21037/qims.2019.02.02.
It is a permanent challenge to differentiate small solid lung nodules. Massive data, extracted from medical image through radiomics analysis, may help early diagnosis of lung cancer. The aim of this study was to assess the usefulness of a quantitative radiomic model developed from baseline low-dose computed tomography (LDCT) screening for the purpose of predicting malignancy in small solid pulmonary nodules (SSPNs).
This retrospective study included malignant and benign SSPNs (6 to 15 mm) detected in baseline low-dose CT screening. The malignancy was confirmed pathologically, and benignity was confirmed by long term follow-up or pathological diagnosis. The non-contrast CT images were reconstructed with a lung kernel of a slice thickness of 1 mm and were processed to extract 385 quantitative radiomic features using Analysis-Kinetic software. A predictive model was established with the training set of 156 benign and 40 malignant nodules, and was tested with the validation set of 77 benign and 21 malignant nodules through the analysis of R square. The performance of the radiomic model in predicting malignancy was compared with that of the ACR Lung Imaging Reporting and Data System (ACR lung-RADS).
In 294 cases of SSPNs, 61 lung cancers and 24 benign nodules were confirmed pathologically and the remaining 209 nodules were stable with long-term follow-up (4.1±0.9 years). Eleven non-redundant features, including 8 high-order texture features, were extracted from the training data set. The sensitivity and specificity of the prediction model in malignancy differentiation were 81.0% and 92.2% respectively. The accuracy was superior to ACR-lung RADS (89.8% 76.5%).
A radiomic model based on baseline low-dose CT screening for lung cancer can improve the accuracy in predicting malignancy of SSPNs.
鉴别肺部小实性结节一直是一项长期挑战。通过放射组学分析从医学影像中提取的大量数据可能有助于肺癌的早期诊断。本研究的目的是评估基于基线低剂量计算机断层扫描（LDCT）筛查开发的定量放射组学模型对预测小实性肺结节（SSPNs）恶性肿瘤的有用性。
这项回顾性研究包括在基线低剂量CT筛查中检测到的恶性和良性SSPNs（6至15毫米）。恶性肿瘤经病理证实，良性肿瘤经长期随访或病理诊断证实。使用1毫米层厚的肺内核重建非增强CT图像，并使用Analysis-Kinetic软件处理以提取385个定量放射组学特征。用156个良性结节和40个恶性结节的训练集建立预测模型，并通过R平方分析用77个良性结节和21个恶性结节的验证集进行测试。将放射组学模型预测恶性肿瘤的性能与美国放射学会（ACR）肺部影像报告和数据系统（ACR lung-RADS）的性能进行比较。
在294例SSPNs病例中，61例肺癌和24例良性结节经病理证实，其余209个结节经长期随访（4.1±0.9年）稳定。从训练数据集中提取了11个非冗余特征，包括8个高阶纹理特征。预测模型在恶性肿瘤鉴别中的敏感性和特异性分别为81.0%和92.2%。其准确性优于ACR-lung RADS（89.8%对76.5%）。
基于基线低剂量CT筛查肺癌的放射组学模型可提高预测SSPNs恶性肿瘤的准确性。
