Kodama Takumi, Arimura Hidetaka, Tokuda Tomoki, Tanaka Kentaro, Yabuuchi Hidetake, Gowdh Nadia Fareeda Muhammad, Liam Chong-Kin, Chai Chee-Shee, Ng Kwan Hoong
Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
Comput Biol Med. 2025 Feb;185:109519. doi: 10.1016/j.compbiomed.2024.109519. Epub 2024 Dec 11.
We hypothesized that persistent lifetime (PLT) images could represent tumor imaging traits, locations, and persistent contrasts of topological components (connected and hole components) corresponding to gene mutations such as epidermal growth factor receptor (EGFR) mutant signs. We aimed to develop a topological radiogenomic approach using PLT images to identify EGFR mutation-positive patients with non-small cell lung cancer (NSCLC). The PLT image was newly proposed to visualize the locations and persistent contrasts of the topological components for a sequence of binary images with consecutive thresholding of an original computed tomography (CT) image. This study employed 226 NSCLC patients (94 mutant and 132 wildtype patients) with pretreatment contrast-enhanced CT images obtained from four datasets from different countries for training and testing prediction models. Two-dimensional (2D) and three-dimensional (3D) PLT images were assumed to characterize specific imaging traits (e.g., air bronchogram sign, cavitation, and ground glass nodule) of EGFR-mutant tumors. Seven types of machine learning classification models were constructed to predict EGFR mutations with significant features selected from 2D-PLT, 3D-PLT, and conventional radiogenomic features. Among the means and standard deviations of the test areas under the receiver operating characteristic curves (AUCs) of all radiogenomic approaches in a four-fold cross-validation test, the 2D-PLT features showed the highest AUC with the lowest standard deviation of 0.927 ± 0.08. The best radiogenomic approaches with the highest AUC were the random forest model trained with the Betti number (BN) map features (AUC = 0.984) in the internal test and the adapting boosting model trained with the BN map features (AUC = 0.717) in the external test. PLT features can be used as radiogenomic imaging biomarkers for the identification of EGFR mutation status in patients with NSCLC.
我们假设，持久寿命（PLT）图像可以表征肿瘤成像特征、位置以及与基因突变（如表皮生长因子受体（EGFR）突变体征）相对应的拓扑成分（连通和空洞成分）的持久对比度。我们旨在开发一种使用PLT图像的拓扑放射基因组学方法，以识别非小细胞肺癌（NSCLC）的EGFR突变阳性患者。新提出的PLT图像用于可视化通过对原始计算机断层扫描（CT）图像进行连续阈值处理得到的一系列二值图像的拓扑成分的位置和持久对比度。本研究采用了226例NSCLC患者（94例突变患者和132例野生型患者），其治疗前的对比增强CT图像来自不同国家的四个数据集，用于训练和测试预测模型。二维（2D）和三维（3D）PLT图像被认为可以表征EGFR突变肿瘤的特定成像特征（如空气支气管造影征、空洞形成和磨玻璃结节）。构建了七种机器学习分类模型，以利用从2D-PLT、3D-PLT和传统放射基因组学特征中选择的显著特征来预测EGFR突变。在四重交叉验证测试中，所有放射基因组学方法的受试者操作特征曲线（AUC）下测试区域的均值和标准差中，2D-PLT特征显示出最高的AUC，标准差最低，为0.927±0.08。内部测试中，AUC最高的最佳放射基因组学方法是使用贝蒂数（BN）图特征训练的随机森林模型（AUC = 0.984），外部测试中是使用BN图特征训练的自适应增强模型（AUC = 0.717）。PLT特征可作为放射基因组学成像生物标志物，用于识别NSCLC患者的EGFR突变状态。
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