Department of Ophthalmology, Shanghai Changzheng Hospital, Shanghai, China.
BMC Ophthalmol. 2023 Jun 23;23(1):288. doi: 10.1186/s12886-023-03036-7.
Preoperative differentiation between IgG4-related orbital disease (IgG4-ROD) and orbital mucosa-associated lymphoid tissue (MALT) lymphoma has a significant impact on clinical decision-making. Our research aims to construct and evaluate a magnetic resonance imaging (MRI)-based radiomics model to assist clinicians to better identify IgG4-ROD and orbital MALT lymphoma and make better preoperative medical decisions.
MR images and clinical data from 20 IgG4-ROD patients and 30 orbital MALT lymphoma patients were classified into a training (21 MALT; 14 IgG4-ROD) or validation set (nine MALT; six IgG4-ROD). Radiomics features were collected from T1-weighted (T1WI) and T2-weighted images (T2WI). Student's t-test, the least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA) were conducted to screen and select the radiomics features. Support vector machine (SVM) classifiers developed from the selected radiomic features for T1WI, T2WI and combined T1WI and T2WI were trained and tested on the training and validation set via five-fold cross-validation, respectively. Diagnostic performance of the classifiers were evaluated with area under the curve (AUC) readings of the receiver operating characteristic (ROC) curve, and readouts for precision, accuracy, recall and F1 score.
Among 12 statistically significant features from T1WI, four were selected for SVM modelling after LASSO analysis. For T2WI, eight of 51 statistically significant features were analyzed by LASSO followed by PCA, with five features finally used for SVM. Combined analysis of T1WI and T2WI features selected two and four, respectively, for SVM. The AUC values for T1WI and T2WI classifiers separately were 0.722 ± 0.037 and 0.744 ± 0.027, respectively, while combined analysis of T1WI and T2WI classifiers further enhanced the classification performances with AUC values ranging from 0.727 to 0.821.
The radiomics model based on features from both T1WI and T2WI images is effective and promising for the differential diagnosis of IgG4-ROD and MALT lymphoma. More detailed radiomics features and advanced techniques should be considered to further explore the differences between these diseases.
术前鉴别 IgG4 相关眼眶疾病（IgG4-ROD）和眼眶黏膜相关淋巴组织（MALT）淋巴瘤对临床决策具有重要影响。本研究旨在构建和评估一种基于磁共振成像（MRI）的放射组学模型，以帮助临床医生更好地识别 IgG4-ROD 和眼眶 MALT 淋巴瘤，并做出更好的术前医疗决策。
将 20 例 IgG4-ROD 患者和 30 例眼眶 MALT 淋巴瘤患者的 MRI 图像和临床资料分为训练集（21 例 MALT；14 例 IgG4-ROD）和验证集（9 例 MALT；6 例 IgG4-ROD）。从 T1 加权（T1WI）和 T2 加权图像（T2WI）中提取放射组学特征。采用学生 t 检验、最小绝对值收缩和选择算子（LASSO）和主成分分析（PCA）筛选和选择放射组学特征。通过五重交叉验证，分别在训练集和验证集上训练和测试基于所选放射组学特征的支持向量机（SVM）分类器。使用受试者工作特征（ROC）曲线的曲线下面积（AUC）评估分类器的诊断性能，并评估精度、准确性、召回率和 F1 评分。
在 T1WI 中，12 个具有统计学意义的特征中，有 4 个经过 LASSO 分析后用于 SVM 建模。对于 T2WI，经过 LASSO 分析和 PCA 分析后，有 51 个具有统计学意义的特征中有 8 个被分析，最终有 5 个特征用于 SVM。T1WI 和 T2WI 联合分析分别选择了 2 个和 4 个用于 SVM。T1WI 和 T2WI 分类器的 AUC 值分别为 0.722±0.037 和 0.744±0.027，而 T1WI 和 T2WI 联合分析分类器的 AUC 值范围为 0.727 至 0.821，进一步提高了分类性能。
基于 T1WI 和 T2WI 图像特征的放射组学模型对 IgG4-ROD 和 MALT 淋巴瘤的鉴别诊断有效且有前景。应考虑更详细的放射组学特征和先进技术，以进一步探讨这些疾病之间的差异。
