纹理具有基于肿瘤和肿瘤界面的胶质母细胞瘤和孤立脑转移的分化
Texture Feature Differentiation of Glioblastoma and Solitary Brain Metastases Based on Tumor and Tumor-brain Interface
影响因子:3.90000
分区:医学2区 / 核医学2区
发表日期:2025 Jan
作者:
Yini Chen, Hongsen Lin, Jiayi Sun, Renwang Pu, Yujing Zhou, Bo Sun
摘要
质地特征源自整个肿瘤区域和肿瘤到脑界面的区域，是区分肿瘤类型及其恶性肿瘤程度的关键指标。但是，尚未彻底探索来自两个区域的纹理特征的区分价值，用于鉴定胶质母细胞瘤和转移性肿瘤。这项研究的目的是开发和验证一个诊断模型，该模型结合了整个肿瘤区域的纹理特征和10 mM肿瘤到脑接口区域，以尝试识别更稳定和有效的纹理特征。我们追溯收集的t1-weightighted Is-Mathikantoma（GBM）患者（GBM）和9020名患者（GBM）和202例MENTASTS（SB）（s）和202名患者（s）（s）和202名患者（s）（s）和202例METAST toss（s）。基于整个肿瘤的纹理特征和10 mM肿瘤到脑界面区域建立多个诊断模型，以区分GBM和SBM。结果通过5倍交叉验证分析进行了评估，为每个模型计算接收器工作特征曲线（AUC）下的面积。使用DELONG测试比较了每个模型的性能，并通过使用Shapley添加说明（SHAP）进一步增强了优化模型的可解释性。使用功能资源管理器（FAE）软件比较了验证数据集中所有管道的AUCS。在Kruskal-Wallis（KW）和Logistic回归（LR）建立的模型中，使用“一个标准误差”规则的AUC最高。 “ 10MM_GLRM_GRAYLEVELNONIRINILITITITY”被认为是最稳定和预测的功能。训练集，测试集和验证集中的最佳模型不是相同的。在测试集中，KW1LR模型的AUC最高为0.880，精度为0.824。结合整体肿瘤和肿瘤 - 脑界面的纹理特征模型有益于区分胶质母细胞瘤和单独的转移性肿瘤，肿瘤界面的纹理特征表现出更高的异质性。
Abstract
Texture features, derived from both the entire tumor area and the region of the tumor-to-brain interface, are crucial indicators for distinguishing tumor types and their degrees of malignancy. However, the discriminative value of texture features from both regions for identifying glioblastomas and metastatic tumors has not been thoroughly explored. The aim of this study is to develop and validate a diagnostic model that combines texture features from the entire tumor area and a 10 mm tumor-to-brain interface region, in an attempt to identify more stable and effective texture features.We retrospectively collected enhanced T1-weighted imaging data from 97 patients with glioblastoma (GBM) and 90 patients with single brain metastasis (SBM) between 2010 and 2024. Machine learning is used to establish multiple diagnostic models for discriminating GBM and SBM based on texture features of the entire tumor and 10 mm tumor-to-brain interface regions. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP).The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Kruskal-Wallis(KW) and Logistic Regression(LR), the AUC was highest using the "one-standard error" rule. '10mm_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature. The best models in the training set, test set, and validation set were not the same. In the test set, the KW1LR model had the highest AUC of 0.880 and an accuracy of 0.824.The texture feature model that combines the overall tumor and the tumor-brain interface is beneficial for distinguishing glioblastoma from solitary metastatic tumors, and the texture features of the tumor interface exhibit higher heterogeneity.