Sun Haitao, Peng Zhaohui, Chen Guoyue, Dai Zhengjun, Yao Jian, Zhou Peng
Medical Imaging Center of Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Scientific Research Department of Huiying Medical Technology Co. Ltd, Beijing, China.
Cancer Med. 2024 Dec;13(23):e70473. doi: 10.1002/cam4.70473.
The objective is to establish a radiomics nomogram (Rad-nomogram) using dual-phase enhanced computed tomography (DPE-CT) for the prediction of progression-free survival (PFS) in patients diagnosed with stage IV lung adenocarcinoma (ADC).
From DPE-CT scans, radiomic characteristics were retrieved from 133 patients diagnosed with stage IV lung ADC. Clinical data were analyzed using univariate and multivariate Cox regression analyses. The radiomics signature was combined with clinical features employing multivariate Cox analysis in order to develop a Rad-nomogram. The predictive efficiency of the nomogram was evaluated using survival studies, such as Kaplan-Meier curves and Harrell's C-index. The benefits and clinical utility of various models were compared using the net reclassification index (NRI), decision curve analysis (DCA), and integrated discrimination improvement (IDI).
In the test cohort, the C-indexes for the clinical, artery, and vein phase CT models were 0.675, 0.691, and 0.678, respectively. The dual-phase achieved a C-index of 0.731, exceeding the CT model, while the developed nomogram reached a C-index of 0.783. The Kaplan-Meier survival study classified patients into low-risk and high-risk groups related to PFS using the Rad-nomogram (p < 0.05). The Rad-nomogram demonstrated a greater net advantage when compared with clinical and Rad models, as indicated by positive values of the NRI and IDI (ranging from 11.6% to 52.6%, p < 0.05).
The Rad-nomogram, employing DPE-CT scans, offers a promising approach to predict PFS in individuals diagnosed with stage IV lung ADC.
本研究旨在利用双期增强计算机断层扫描（DPE-CT）建立一种影像组学列线图（Rad-列线图），用于预测IV期肺腺癌（ADC）患者的无进展生存期（PFS）。
从133例IV期肺ADC患者的DPE-CT扫描中提取影像组学特征。采用单因素和多因素Cox回归分析临床数据。通过多因素Cox分析将影像组学特征与临床特征相结合，以建立Rad-列线图。采用生存研究（如Kaplan-Meier曲线和Harrell C指数）评估列线图的预测效率。使用净重新分类指数（NRI）、决策曲线分析（DCA）和综合判别改善（IDI）比较各种模型的优势和临床实用性。
在测试队列中，临床、动脉期和静脉期CT模型的C指数分别为0.675、0.691和0.678。双期模型的C指数为0.731，超过了CT模型，而所建立的列线图的C指数达到了0.783。Kaplan-Meier生存研究使用Rad-列线图将患者分为与PFS相关的低风险和高风险组（p<0.05）。NRI和IDI的正值表明，与临床和Rad模型相比，Rad-列线图具有更大的净优势（范围为11.6%至52.6%，p<0.05）。
采用DPE-CT扫描的Rad-列线图为预测IV期肺ADC患者的PFS提供了一种有前景的方法。
