Department of Radiology, Changzheng Hospital, Second Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China.
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 Zhongguancun East Road, Beijing, 100190, China.
Eur Radiol. 2019 Feb;29(2):889-897. doi: 10.1007/s00330-018-5530-z. Epub 2018 Jul 2.
To identify the radiomics signature allowing preoperative discrimination of lung invasive adenocarcinomas from non-invasive lesions manifesting as ground-glass nodules.
This retrospective primary cohort study included 160 pathologically confirmed lung adenocarcinomas. Radiomics features were extracted from preoperative non-contrast CT images to build a radiomics signature. The predictive performance and calibration of the radiomics signature were evaluated using intra-cross (n=76), external non-contrast-enhanced CT (n=75) and contrast-enhanced CT (n=84) validation cohorts. The performance of radiomics signature and CT morphological and quantitative indices were compared.
355 three-dimensional radiomics features were extracted, and two features were identified as the best discriminators to build a radiomics signature. The radiomics signature showed a good ability to discriminate between invasive adenocarcinomas and non-invasive lesions with an accuracy of 86.3%, 90.8%, 84.0% and 88.1%, respectively, in the primary and validation cohorts. It remained an independent predictor after adjusting for traditional preoperative factors (odds ratio 1.87, p < 0.001) and demonstrated good calibration in all cohorts. It was a better independent predictor than CT morphology or mean CT value.
The radiomics signature showed good predictive performance in discriminating between invasive adenocarcinomas and non-invasive lesions. Being a non-invasive biomarker, it could assist in determining therapeutic strategies for lung adenocarcinoma.
• The radiomics signature was a non-invasive biomarker of lung invasive adenocarcinoma. • The radiomics signature outweighed CT morphological and quantitative indices. • A three-centre study showed that radiomics signature had good predictive performance.
确定能够在术前区分表现为磨玻璃结节的浸润性腺癌与非浸润性病变的放射组学特征。
本回顾性的主要队列研究纳入了 160 例经病理证实的肺腺癌患者。从术前非增强 CT 图像中提取放射组学特征，构建放射组学特征。使用内部交叉验证（n=76）、外部非增强 CT（n=75）和增强 CT（n=84）验证队列评估放射组学特征的预测性能和校准能力。比较放射组学特征和 CT 形态及定量指标的性能。
提取了 355 个三维放射组学特征，确定了两个最佳鉴别特征来构建放射组学特征。该放射组学特征在主要和验证队列中，分别具有 86.3%、90.8%、84.0%和 88.1%的区分浸润性腺癌与非浸润性病变的良好能力。在调整了传统术前因素后，它仍然是一个独立的预测因素（优势比 1.87，p<0.001），并在所有队列中均具有良好的校准能力。它是比 CT 形态或平均 CT 值更好的独立预测因素。
放射组学特征在区分浸润性腺癌与非浸润性病变方面具有良好的预测性能。作为一种非侵入性生物标志物，它可以帮助确定肺腺癌的治疗策略。
放射组学特征是肺浸润性腺癌的一种非侵入性生物标志物。
放射组学特征优于 CT 形态和定量指标。
一项三中心研究表明，放射组学特征具有良好的预测性能。
