Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Faculty of Medical Sciences, Division of Medical Quantum Science, Department of Health Sciences, Kyushu University, Fukuoka, Japan.
PLoS One. 2021 Jan 11;16(1):e0244354. doi: 10.1371/journal.pone.0244354. eCollection 2021.
To propose a novel robust radiogenomics approach to the identification of epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC) using Betti numbers (BNs).
Contrast enhanced computed tomography (CT) images of 194 multi-racial NSCLC patients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers' scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test.
The BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29).
The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.
提出一种新的稳健放射基因组学方法，使用贝蒂数（Betti numbers，BNs）来识别非小细胞肺癌（non-small cell lung cancer，NSCLC）患者中的表皮生长因子受体（epidermal growth factor receptor，EGFR）突变。
收集了来自三个不同国家的 194 名多族裔 NSCLC 患者（79 名 EGFR 突变体和 115 名野生型）的增强对比 CT 图像，这些患者使用 5 家制造商的扫描仪和各种扫描参数进行扫描。其中 99 例来自马来西亚马来亚大学医学中心（University of Malaya Medical Centre，UMMC），用于训练和验证程序。41 例来自日本九州大学医院（Kyushu University Hospital，KUH），54 例来自美国癌症成像档案（The Cancer Imaging Archive，TCIA），用于测试程序。从 BN 图谱中提取放射组学特征，BN 图谱代表 CT 图像上肺癌的拓扑不变异质特征，通过应用直方图和纹理特征计算来实现。使用支持向量机（support vector machine，SVM）模型确定基于 BN 的签名，该模型使用特征的最佳组合，最大化了稳健性指数（robustness index，RI），该指数定义了更高的接收器操作特征曲线（receiver operating characteristics curves，ROCs）下的总面积和训练与验证之间的 AUC 差异。SVM 模型使用签名并在五折交叉验证中进行优化。然后将基于 BN 的模型与基于原始图像（original image，OI）和小波分解（wavelet decomposition，WD）的模型进行比较，比较 RI 在验证和测试之间的差异。
基于 BN 的模型的 RI 为 1.51，高于基于 OI（RI：1.33）和 WD（RI：1.29）的模型。
与传统的基于 OI 和 WD 的模型相比，所提出的模型在识别 NSCLC 患者中的 EGFR 突变方面表现出更高的稳健性。结果表明，基于 BN 的方法在图像扫描仪/扫描参数变化方面具有稳健性。
