PDF(Pubmed)
Abstract：
BACKGROUND: Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma.
METHODS: A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume (GPTV5, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model.
RESULTS: The GPTV10 radiomics model exhibited superior predictive performance compared to GTV, GPTV5, and GPTV15, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the GPTV10-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets.
CONCLUSIONS: The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma.
METHODS: A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume (GPTV5, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model.
RESULTS: The GPTV10 radiomics model exhibited superior predictive performance compared to GTV, GPTV5, and GPTV15, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the GPTV10-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets.
CONCLUSIONS: The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma.
摘要：
背景：术前准确预测肺腺癌内脏胸膜侵犯（VPI）可为手术及术后治疗提供指导和帮助。我们研究了肿瘤内和瘤周影像组学列线图在术前预测诊断为IA临床期肺腺癌患者VPI状态的价值。
方法：我们医院的404名患者被随机分配到一个训练集（n=283）和一个内部验证集（n=121），比例为7:3，而来自另外两家医院的81名患者构成了外部验证集。我们从大体肿瘤体积（GTV）以及大体肿瘤周围肿瘤体积（GPTV5，10，15）中提取了1218个基于CT的影像组学特征，分别,并构建了放射学模型。此外,我们根据相关CT特征和从最佳影像组学模型得出的radscore开发了列线图.
结果：与GTV相比，GPTV10影像组学模型表现出优越的预测性能，GPTV5和GPTV15,在三组中分别具有0.855、0.842和0.842的曲线下面积(AUC)值。在临床模型中，固体成分的尺寸，胸膜凹陷,固体附件，在CT特征中，血管会聚征被确定为独立的危险因素。列线图的预测性能，结合了相关的CT特征和GPTV10-radscore，优于单独的影像组学模型和临床模型，三组的AUC值分别为0.894、0.828和0.876。
结论：列线图，整合影像组学特征和CT形态特征，在预测肺腺癌的VPI状态方面表现出良好的性能。
方法：我们医院的404名患者被随机分配到一个训练集（n=283）和一个内部验证集（n=121），比例为7:3，而来自另外两家医院的81名患者构成了外部验证集。我们从大体肿瘤体积（GTV）以及大体肿瘤周围肿瘤体积（GPTV5，10，15）中提取了1218个基于CT的影像组学特征，分别,并构建了放射学模型。此外,我们根据相关CT特征和从最佳影像组学模型得出的radscore开发了列线图.
结果：与GTV相比，GPTV10影像组学模型表现出优越的预测性能，GPTV5和GPTV15,在三组中分别具有0.855、0.842和0.842的曲线下面积(AUC)值。在临床模型中，固体成分的尺寸，胸膜凹陷,固体附件，在CT特征中，血管会聚征被确定为独立的危险因素。列线图的预测性能，结合了相关的CT特征和GPTV10-radscore，优于单独的影像组学模型和临床模型，三组的AUC值分别为0.894、0.828和0.876。
结论：列线图，整合影像组学特征和CT形态特征，在预测肺腺癌的VPI状态方面表现出良好的性能。
