Lin Miaomiao, Li Kai, Zou Yanni, Huang Haipeng, Zhao Xiang, Yang Siyu, Zhao Chunli
Department of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Nanning, 530021, Guangxi, China.
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 06 Shuangyong Road, Nanning, 530021, China.
Clin Transl Oncol. 2025 May;27(5):1994-2004. doi: 10.1007/s12094-024-03705-z. Epub 2024 Oct 5.
This study aimed to investigate the predictive value of intratumoral and peritumoral radiomics model for the cribriform component (CC) of invasive lung adenocarcinoma (LUAD).
The 144 patients with invasive LUAD from our center were randomly divided into training set (n = 100) and internal validation set (n = 44) in a ratio of 7:3, and 75 patients from center 2 were regarded as the external validation set. Clinical risk factors were examined using univariate and multivariate logistic regression to construct the clinical model. We extracted radiomics features from gross tumor volume (GTV), gross and peritumoral volume (GPTV), and peritumoral volume (PTV), respectively. Radiomics models were constructed with selected features. A combined model based on the optimal Radscore and clinically independent predictors was constructed, and its predictive performance was assessed by receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).
The area under curves (AUCs) of the GTV model were 0.882 (95% CI 0.817-0.948), 0.794 (95% CI 0.656-0.932), and 0.766 (95% CI 0.657-0.875) in the training, internal validation, and external validation sets, and the PTV model had AUCs of 0.812 (95% CI 0.725-0.899), 0.749 (95% CI 0.597-0.902), and 0.670 (95% CI 0.543-0.798) in the training, internal validation, and external validation sets, respectively. However, the GPTV radiomics model showed better predictive performance compared with the GTV and PTV radiomics models, with the AUCs of 0.950 (95% CI 0.911-0.989), 0.844 (95% CI 0.728-0.959), and 0.815 (95% CI 0.713-0.917) in the training, internal validation and external validation sets, respectively. In the clinical model, tumor shape, lobulation sign and maximal diameter were the independent predictors of CC in invasive LUAD. The combined model including independent clinical predictors and GPTV-Radscore show the considerable instructive to clinical practice, with the AUCs of 0.954(95% CI 0.918-0.990), 0.861(95% CI 0.752-0.970), and 0.794(95% CI 0.690-0.898) in training, internal validation, and external validation sets, respectively. DCA showed that the combined model had good clinical value and correction effect.
Radiomics model is a very powerful tool for predicting CC growth pattern in invasive LUAD and can help clinicians make the strategies of treatment and surveillance in patients with invasive LUAD.
本研究旨在探讨瘤内及瘤周放射组学模型对浸润性肺腺癌（LUAD）筛状成分（CC）的预测价值。
将来自本中心的144例浸润性LUAD患者按7:3的比例随机分为训练集（n = 100）和内部验证集（n = 44），并将来自中心2的75例患者作为外部验证集。采用单因素和多因素逻辑回归分析临床危险因素以构建临床模型。我们分别从大体肿瘤体积（GTV）、大体及瘤周体积（GPTV）和瘤周体积（PTV）中提取放射组学特征。利用选定的特征构建放射组学模型。构建基于最佳Radscore和临床独立预测因子的联合模型，并通过受试者操作特征曲线（ROC）、校准曲线和决策曲线分析（DCA）评估其预测性能。
GTV模型在训练集、内部验证集和外部验证集中的曲线下面积（AUC）分别为0.882（95%CI 0.817 - 0.948）、0.794（95%CI 0.656 - 0.932）和0.766（95%CI 0.657 - 0.875），PTV模型在训练集、内部验证集和外部验证集中的AUC分别为0.812（95%CI 0.725 - 0.899）、0.749（95%CI 0.597 - 0.902）和0.670（95%CI 0.543 - 0.798）。然而，GPTV放射组学模型与GTV和PTV放射组学模型相比显示出更好的预测性能，其在训练集、内部验证集和外部验证集中的AUC分别为0.950（95%CI 0.911 - 0.989）、0.
