Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411000, China.
School of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China.
J Cardiothorac Surg. 2024 Aug 30;19(1):505. doi: 10.1186/s13019-024-03008-y.
We aimed to evaluate the efficiency of computed tomography (CT) radiomic features extracted from gross tumor volume (GTV) and peritumoral volumes (PTV) of 5, 10, and 15 mm to identify the tumor grades corresponding to the new histological grading system proposed in 2020 by the Pathology Committee of the International Association for the Study of Lung Cancer (IASLC).
A total of 151 lung adenocarcinomas manifesting as pure ground-glass lung nodules (pGGNs) were included in this randomized multicenter retrospective study. Four radiomic models were constructed from GTV and GTV + 5/10/15-mm PTV, respectively, and compared. The diagnostic performance of the different models was evaluated using receiver operating characteristic curve analysis RESULTS: The pGGNs were classified into grade 1 (117), 2 (34), and 3 (0), according to the IASLC grading system. In all four radiomic models, pGGNs of grade 2 had significantly higher radiomic scores than those of grade 1 (P < 0.05). The AUC of the GTV and GTV + 5/10/15-mm PTV were 0.869, 0.910, 0.951, and 0.872 in the training cohort and 0.700, 0.715, 0.745, and 0.724 in the validation cohort, respectively.
The radiomic features we extracted from the GTV and PTV of pGGNs could effectively be used to differentiate grade-1 and grade-2 tumors. In particular, the radiomic features from the PTV increased the efficiency of the diagnostic model, with GTV + 10 mm PTV exhibiting the highest efficacy.
我们旨在评估从大体肿瘤体积（GTV）和周围肿瘤体积（PTV）中提取的计算机断层扫描（CT）放射组学特征的效率，以识别与国际肺癌研究协会（IASLC）病理学委员会在 2020 年提出的新组织学分级系统相对应的肿瘤分级。
这项随机多中心回顾性研究共纳入了 151 例表现为单纯磨玻璃肺结节（pGGN）的肺腺癌患者。分别从 GTV 和 GTV+5/10/15-mm PTV 构建了 4 个放射组学模型，并进行了比较。使用受试者工作特征曲线分析评估不同模型的诊断性能。
根据 IASLC 分级系统，pGGNs 被分为 1 级（117 例）、2 级（34 例）和 3 级（0 例）。在所有 4 个放射组学模型中，2 级 pGGNs 的放射组学评分明显高于 1 级（P<0.05）。在训练队列中，GTV 和 GTV+5/10/15-mm PTV 的 AUC 分别为 0.869、0.910、0.951 和 0.872，在验证队列中分别为 0.700、0.715、0.745 和 0.724。
我们从 pGGNs 的 GTV 和 PTV 中提取的放射组学特征可有效用于区分 1 级和 2 级肿瘤。特别是，来自 PTV 的放射组学特征提高了诊断模型的效率，其中 GTV+10-mm PTV 的效果最高。
