Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.
The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.
Eur Radiol. 2021 Dec;31(12):9030-9037. doi: 10.1007/s00330-021-07948-0. Epub 2021 May 26.
To evaluate the ability of CT radiomic features extracted from peritumoral parenchyma of 2 mm and 5 mm distinguishing invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA).
For this retrospective study, 121 lung adenocarcinomas appearing as ground-glass nodules on thin-section CT were evaluated. Quantitative radiomic features were extracted from the peritumoral parenchymal region of 2 mm and 5 mm on CT imaging, and the radiomic models of External2 and External5 were constructed. The ROC curves were used to evaluate the performance of different models. Differences between the AUCs were evaluated using DeLong's method.
The radiomic scores of IAC were statistically higher than those of MIA/AIS in both the External2 and External5 models. The AUCs of the External2 and External5 models were 0.882, 0.778 in the training cohort and 0.888, 0.804 in the validation cohort, respectively. The AUC of the External2 model was not statistically different from the External5 model both in the training cohort (p = 0.116) and validation cohort (p = 0.423).
The radiomic features extracted from the peritumoral region of 2 mm and 5 mm at thin-section CT showed good predictive values to differentiate the IAC from AIS/MIA. The radiomic features from the peritumoral region of 5 mm provide no additional benefit in distinguishing IAC from MIA/AIS than that of the 2 mm region.
• The radiomic models from various peritumoral lung parenchyma were developed and validated to predict invasiveness of adenocarcinoma. • The peritumoral parenchyma of lung adenocarcinoma may contain useful information. • Radiomics from peritumoral lung parenchyma of 5 mm provides no added efficiency of the prediction for invasiveness of lung adenocarcinoma.
评估从 2mm 和 5mm 肿瘤周围实质提取的 CT 放射组学特征区分浸润性腺癌（IAC）与原位腺癌（AIS）/微浸润性腺癌（MIA）的能力。
本回顾性研究纳入了 121 例在薄层 CT 上表现为磨玻璃结节的肺腺癌。从 CT 成像上的肿瘤周围实质区域提取定量放射组学特征，并构建外部 2 模型和外部 5 模型。使用 ROC 曲线评估不同模型的性能。使用 DeLong 方法评估 AUC 之间的差异。
在外部 2 模型和外部 5 模型中，IAC 的放射组学评分均显著高于 MIA/AIS。训练队列中外部 2 模型和外部 5 模型的 AUC 分别为 0.882 和 0.778，验证队列中分别为 0.888 和 0.804。在训练队列（p=0.116）和验证队列（p=0.423）中，外部 2 模型的 AUC 与外部 5 模型均无统计学差异。
从薄层 CT 肿瘤周围区域提取的放射组学特征对预测 IAC 与 AIS/MIA 具有良好的预测价值。与 2mm 区域相比，5mm 肿瘤周围区域的放射组学特征在区分 IAC 与 MIA/AIS 方面没有提供额外的益处。
开发和验证了来自不同肿瘤周围肺实质的放射组学模型，以预测腺癌的侵袭性。
肺腺癌的肿瘤周围实质可能包含有用的信息。
5mm 肿瘤周围肺实质的放射组学对预测肺腺癌的侵袭性没有额外的效率。
