Zuo Zhichao, Deng Jinqiu, Ge Wu, Zhou Yinjun, Liu Haibo, Zhang Wei, Zeng Ying
Department of Radiology, Xiangtan Central Hospital, Xiangtan, 411000, P. R. China.
The School of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, P. R. China.
BMC Cancer. 2025 Jan 9;25(1):51. doi: 10.1186/s12885-025-13445-0.
This study aims to quantify intratumoral heterogeneity (ITH) using preoperative CT image and evaluate its ability to predict pathological high-grade patterns, specifically micropapillary and/or solid components (MP/S), in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC).
In this retrospective study, we enrolled 457 patients who were postoperatively diagnosed with clinical stage I solid LADC from two medical centers, assigning them to either a training set (n = 304) or a test set (n = 153). Sub-regions within the tumor were identified using the K-means method. Both intratumoral ecological diversity features (hereafter referred to as ITH) and conventional radiomics (hereafter referred to as C-radiomics) were extracted to generate ITH scores and C-radiomics scores. Next, univariate and multivariate logistic regression analyses were employed to identify clinical-radiological (Clin-Rad) features associated with the MP/S (+) group for constructing the Clin-Rad classification. Subsequently, a hybrid model which presented as a nomogram was developed, integrating the Clin-Rad classification and ITH score. The performance of models was assessed using the receiver operating characteristic (ROC) curves, and the area under the curve (AUC), accuracy, sensitivity, and specificity were determined.
The ITH score outperformed both C-radiomics scores and Clin-Rad classification, as evidenced by higher AUC values in the training set (0.820 versus 0.810 and 0.700, p = 0.049 and p = 0.031, respectively) and in the test set (0.805 versus 0.771 and 0.732, p = 0.041 and p = 0.025, respectively). Finally, the hybrid model consistently demonstrated robust predictive capabilities in identifying presence of MP/S components, achieving AUC of 0.830 in the training set and 0.849 in the test set (all p < 0.05).
The ITH derived from sub-region within the tumor has been shown to be a reliable predictor for MP/S (+) in clinical stage I solid LADC.
本研究旨在利用术前CT图像量化肿瘤内异质性（ITH），并评估其预测临床I期实性肺腺癌（LADC）患者病理高级别模式，特别是微乳头和/或实性成分（MP/S）的能力。
在这项回顾性研究中，我们纳入了来自两个医疗中心的457例术后诊断为临床I期实性LADC的患者，将他们分为训练集（n = 304）或测试集（n = 153）。使用K均值方法识别肿瘤内的子区域。提取肿瘤内生态多样性特征（以下简称ITH）和传统放射组学（以下简称C-放射组学）以生成ITH分数和C-放射组学分数。接下来，采用单因素和多因素逻辑回归分析来识别与MP/S（+）组相关的临床放射学（Clin-Rad）特征，以构建Clin-Rad分类。随后，开发了一种以列线图形式呈现的混合模型，整合了Clin-Rad分类和ITH分数。使用受试者操作特征（ROC）曲线评估模型的性能，并确定曲线下面积（AUC）、准确性、敏感性和特异性。
ITH分数优于C-放射组学分数和Clin-Rad分类，训练集（分别为0.820对0.810和0.700，p = 0.049和p = 0.031）和测试集（分别为0.805对0.771和0.732，p = 0.041和p = 0.025）中较高的AUC值证明了这一点。最后，混合模型在识别MP/S成分的存在方面始终表现出强大的预测能力，训练集中的AUC为0.830，测试集中为0.849（所有p < 0.05）。
肿瘤内子区域衍生的ITH已被证明是临床I期实性LADC中MP/S（+）的可靠预测指标。
