| 王立英,李辛子,李颖,郑美敏,陈森,叶兆祥,王春祥.腰椎骨髓MR T1 mapping影像组学预测儿童急性淋巴细胞白血病临床危险度[J].中国医学影像技术,2024,40(9):1284~1288 |
| 腰椎骨髓MR T1 mapping影像组学预测儿童急性淋巴细胞白血病临床危险度 |
| Lumbar spine marrow MR T1 mapping radiomics for predicting clinical risk of acute lymphoblastic leukemia in children |
| 投稿时间：2024-05-24 修订日期：2024-07-01 |
| DOI：10.13929/j.issn.1003-3289.2024.09.002 |
| 中文关键词: 儿童 白血病 磁共振成像 影像组学 前瞻性研究 |
| 英文关键词:child leukemia magnetic resonance imaging radiomics prospective study |
| 基金项目:天津市医学重点学科建设项目(TJYXZDXK-040A)。 |
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| 中文摘要: |
| 目的 观察腰椎骨髓MR T1 mapping影像组学预测儿童急性淋巴细胞白血病(ALL)临床危险度的价值。方法 前瞻性对77例初诊ALL患儿采集腰椎骨髓T1 mapping,以3D Slicer软件分割L3椎体感兴趣体积(VOI)、提取2 060个影像组学特征并筛选最佳特征。以8 ∶ 2比例将患儿分为训练集与测试集,分别以逻辑回归(LR)、支持向量机(SVM)及随机森林(RF)分类器基于最佳特征建立影像组学模型,于训练集进行训练、于测试集进行验证;结合初诊危险度及MR检查后患儿对于化学治疗的反应评估其临床危险度,以采用受试者工作特征(ROC)曲线及曲线下面积(AUC)评估模型预测效能。结果 低-中危组52例、高危组25例。训练集含44例低-中危及17例高危,测试集含8例低-中危及8例高危患儿。共获得12个最佳影像组学特征,以之建立的RF模型在训练集中的敏感度及准确率均为100%,在测试集的敏感度(50.00%)及准确率(75.00%)均较低,提示存在过拟合;LR模型在测试集AUC(0.95)稍高于SVM模型(0.92)但差异无统计学意义(P＞0.05),而其准确率一致。结论 腰椎骨髓T1 mapping LR及SVM影像组学模型均可用于预测儿童ALL临床危险度;LR模型预测效能较佳。 |
| 英文摘要: |
| Objective To observe the value of lumbar spine bone marrow MR T1 mapping radiomics for predicting clinical risk of acute lymphoblastic leukemia (ALL) in children. Methods Lumbar bone marrow T1 mappings were prospectively acquired from 77 newly diagnosed ALL children. The volume of interest (VOI) of L3 vertebral body was segmented using 3D Slicer software and 2 060 radiomics features were extracted, and the best features were screened. The children were divided into training and testing sets at the ratio of 8 ∶ 2. Logistic regression (LR), support vector machine (SVM) and random forest (RF) were used to established radiomics models based on the best features, respectively, which were trained in training set and verified in testing set. The clinical risk was evaluated according to newly diagnosed risk and the response to chemotherapy after MR examination. Receiver operating characteristic (ROC) curve was drawn, and the area under the curve (AUC) was calculated to evaluate the efficacy of each model for predicting clinical risk of ALL in children. Results There were 52 cases in low-medium risk group and 25 in high risk group. The training set consisted of 44 cases of low-medium risk and 17 of high risk, while the testing set consisted of 8 cases of low-medium risk and 8 of high risk. Twelve best features were selected to establish radiomics models. The sensitivity and accuracy of RF model in training set were both 100%, but its sensitivity (50.00%) and accuracy (75.00%) in testing set were both low, which indicating overfitting. The AUC (0.95) of LR model was slightly higher than that of SVM model (0.92) in testing set, but no significantly difference was found (P＞0.05), and the accuracy of these two models was consistent. Conclusion Both lumbar bone marrow T1 mapping LR and SVM radiomics models could be used to predict clinical risk of ALL in children, and LR model had better predictive efficacy. |
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