Department of Hematology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China; Department of Oncology Medicine, Fuzhou Pulmonary Hospital of Fujian Province, The Teaching Hospital of Fujian Medical University, 2 Hubian Rd, 350001 Fuzhou, Fujian, China.
Department of Hematology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China.
Methods. 2024 Apr;224:54-62. doi: 10.1016/j.ymeth.2024.02.003. Epub 2024 Feb 17.
The aim of this study is to create and validate a radiomics model based on CT scans, enabling the distinction between pulmonary mucosa-associated lymphoid tissue (MALT) lymphoma and other pulmonary lesion causes.
Patients diagnosed with primary pulmonary MALT lymphoma and lung infections at Fuzhou Pulmonary Hospital were randomly assigned to either a training group or a validation group. Meanwhile, individuals diagnosed with primary pulmonary MALT lymphoma and lung infections at Fujian Provincial Cancer Hospital were chosen as the external test group. We employed ITK-SNAP software for delineating the Region of Interest (ROI) within the images. Subsequently, we extracted radiomics features and convolutional neural networks using PyRadiomics, a component of the Onekey AI software suite. Relevant radiomic features were selected to build an intelligent diagnostic prediction model utilizing CT images, and the model's efficacy was assessed in both the validation group and the external test group.
Leveraging radiomics, ten distinct features were carefully chosen for analysis. Subsequently, this study employed the machine learning techniques of Logistic Regression (LR), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) to construct models using these ten selected radiomics features within the training groups. Among these, SVM exhibited the highest performance, achieving an accuracy of 0.868, 0.870, and 0.90 on the training, validation, and external testing groups, respectively. For LR, the accuracy was 0.837, 0.863, and 0.90 on the training, validation, and external testing groups, respectively. For KNN, the accuracy was 0.884, 0.859, and 0.790 on the training, validation, and external testing groups, respectively.
We established a noninvasive radiomics model utilizing CT imaging to diagnose pulmonary MALT lymphoma associated with pulmonary lesions. This model presents a promising adjunct tool to enhance diagnostic specificity for pulmonary MALT lymphoma, particularly in populations where pulmonary lesion changes may be attributed to other causes.
本研究旨在创建和验证一种基于 CT 扫描的放射组学模型，以区分肺黏膜相关淋巴组织（MALT）淋巴瘤和其他肺部病变原因。
将在福州肺科医院诊断为原发性肺 MALT 淋巴瘤和肺部感染的患者随机分配到训练组或验证组，同时选择在福建省肿瘤医院诊断为原发性肺 MALT 淋巴瘤和肺部感染的患者作为外部测试组。我们使用 ITK-SNAP 软件在图像中勾画感兴趣区（ROI）。然后，我们使用 PyRadiomics 从图像中提取放射组学特征和卷积神经网络，这是 Onekey AI 软件套件的一部分。选择相关的放射组学特征来构建基于 CT 图像的智能诊断预测模型，并在验证组和外部测试组中评估该模型的效能。
利用放射组学，我们精心选择了十个不同的特征进行分析。然后，本研究使用 Logistic Regression（LR）、支持向量机（SVM）和 k-Nearest Neighbors（KNN）等机器学习技术，在训练组中使用这十个选定的放射组学特征构建模型。其中，SVM 的性能最高，在训练组、验证组和外部测试组中的准确率分别为 0.868、0.870 和 0.90。对于 LR，在训练组、验证组和外部测试组中的准确率分别为 0.837、0.863 和 0.90。对于 KNN，在训练组、验证组和外部测试组中的准确率分别为 0.884、0.859 和 0.790。
我们利用 CT 成像建立了一种非侵入性放射组学模型，用于诊断与肺部病变相关的肺 MALT 淋巴瘤。该模型有望成为增强肺 MALT 淋巴瘤诊断特异性的辅助工具，特别是在肺部病变变化可能归因于其他原因的人群中。
