| 常燃,侯芳婧,朱海涛,崔传亮,李晓婷,孙应实,高顺禹.基于增强CT影像组学特征预测难治性恶性黑色素瘤肺转移患者的免疫治疗疗效[J].中国医学影像技术,2021,37(2):225~229 |
| 基于增强CT影像组学特征预测难治性恶性黑色素瘤肺转移患者的免疫治疗疗效 |
| Radiomics features based on enhanced CT in predicting efficacy of immunotherapy in patients with refractory malignant melanoma lung metastasis |
| 投稿时间：2020-08-24 修订日期：2021-02-08 |
| DOI：10.13929/j.issn.1003-3289.2021.02.014 |
| 中文关键词: 黑色素瘤 肿瘤转移 免疫治疗 体层摄影术,X线计算机 影像组学 |
| 英文关键词:melanoma neoplasm metastasis immunotherapy tomography, X-ray computed radiomics |
| 基金项目:北京市医院管理中心“登峰”计划专项（DFL20191103）、北京市医院管理局“扬帆”计划重点医学专业发展计划（ZYLX201803）。 |
| 作者 | 单位 | E-mail | | 常燃 | 北京大学肿瘤医院暨北京市肿瘤防治研究所医学影像科, 恶性肿瘤发病机制及转化研究教育部重点实验室, 北京 100142
满洲里市人民医院放射科, 内蒙古 呼伦贝尔 021400 | | | 侯芳婧 | 北京大学肿瘤医院暨北京市肿瘤防治研究所医学影像科, 恶性肿瘤发病机制及转化研究教育部重点实验室, 北京 100142 | | | 朱海涛 | 北京大学肿瘤医院暨北京市肿瘤防治研究所医学影像科, 恶性肿瘤发病机制及转化研究教育部重点实验室, 北京 100142 | | | 崔传亮 | 北京大学肿瘤医院暨北京市肿瘤防治研究所肾癌黑色素瘤内科, 北京 100142 | | | 李晓婷 | 北京大学肿瘤医院暨北京市肿瘤防治研究所医学影像科, 恶性肿瘤发病机制及转化研究教育部重点实验室, 北京 100142 | | | 孙应实 | 北京大学肿瘤医院暨北京市肿瘤防治研究所医学影像科, 恶性肿瘤发病机制及转化研究教育部重点实验室, 北京 100142 | | | 高顺禹 | 北京大学肿瘤医院暨北京市肿瘤防治研究所医学影像科, 恶性肿瘤发病机制及转化研究教育部重点实验室, 北京 100142 | shunyuhome@163.com |
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| 中文摘要: |
| 目的 探讨基于胸部增强CT影像组学特征预测免疫治疗用于难治性恶性黑色素瘤肺转移疗效的价值。方法 回顾性分析49例难治性恶性黑色素瘤肺内转移患者，均接受程序性死亡受体（PD-1）单抗免疫治疗，采用实体瘤疗效评价标准（RECIST）1.1评价疗效，并将患者分为进展组（n=17）和未进展组。提取免疫治疗前增强CT图像中肺转移病灶信息，以3D-Slicer软件手动逐层勾画整个病灶并进行分割；采用Pyradiomics程序提取病灶形状特征、灰度一阶特征、纹理特征和小波特征，以Pearson相关性分析和递归式特征消除策略进行降维。以支持向量机（SVM）方法建立分类模型，预测病变进展的可能性。绘制受试者工作特征（ROC）曲线，评价模型预测进展组、非进展组的效能。结果 对每个靶病灶提取841个增强CT影像组学特征，最终筛选出3个影像组学纹理特征，分别为wavelet-HHH_glszm_Low Gray Level Zone Emphasis、wavelet-HHL_first order_Skewness和wavelet-LLL_gldm_Small Dependence High Gray Level Emphasis，用于构建影像组学模型。模型预测训练组病变进展的曲线下面积（AUC）为0.913，测试组为0.860；预测训练组病变进展的敏感度、特异度、准确率、阳性预测值和阴性预测值分别为83.3%、95.5%、91.2%、90.9%和91.3%，测试组分别为80.0%、80.0%、80.0%、66.7%和88.9%。结论 基于治疗前胸部增强CT影像组学特征建立的模型对恶性黑色素瘤肺转移免疫治疗疗效具有较好预测价值。 |
| 英文摘要: |
| Objective To explore the value of radiomics features based on chest enhanced CT in predicting the efficacy of immunotherapy in pulmonary metastasis of refractory malignant melanoma. Methods Data of 49 patients with intrapulmonary metastasis of refractory malignant melanoma treated with programmed death receptor (PD-1) monoclonal antibody were retrospectively analyzed. The patients were then divided into progression group (n=17) and non-progression group (n=32,including 16 stable cases and 16 partial remission cases) according to respond evaluation criteria of solid tumors (RECIST) 1.1 criteria. The whole lesion of lung metastasis was manually delineated on lung window enhanced CT images before immunotherapy with 3D-Slicer software. Pyradiomics program was used to extract the shape, first-order gray level, texture and wavelet features of the lesions. Then the features were reduced by Pearson correlation coefficient and recursive feature elimination. A classification model for predicting progress was established using support vector machine (SVM) method, and receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic efficacy of model for distinguishing progression and non-progression group. Results A total of 841 CT imaging features were extracted from each target lesion, and finally 3 texture features were selected after dimension reduction and were taken to establish the prediction model, including wavelet-HHH_glszm_Low Gray Level Zone Emphasis, wavelet-HHL_first order_Skewness and wavelet-LLL_gldm_Small Dependence High Gray Level Emphasis. The area under the curve (AUC) of the prediction model was 0.913 (95%CI) for the training group and 0.860 (95%CI) for the test group. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value for the training group was 83.3%,95.5%,91.2%,90.9% and 91.3%, of the test group was 80.0%, 80.0%,80.0%,66.7% and 88.9%, respectively. Conclusion The radiomics model based on enhanced chest CT before immunotherapy demonstrated good performance for predicting immunotherapy efficacy in patients with lung metastasis of malignant melanoma. |
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