| 江伟,徐文贵,张瑞平,朱磊.基于PET代谢参数构建孤立性肺良恶性病变预测模型[J].中国医学影像技术,2019,35(5):696~700 |
| 基于PET代谢参数构建孤立性肺良恶性病变预测模型 |
| Diagnostic models of solitary pulmonary mass lesion based on PET metabolic parameters |
| 投稿时间：2018-12-04 修订日期：2019-03-14 |
| DOI：10.13929/j.1003-3289.201812012 |
| 中文关键词: 肺肿瘤 氟脱氧葡萄糖F18 正电子发射断层显像术 支持向量机 |
| 英文关键词:lung neoplasms fludeoxyglucose F 18 positron-emission tomography support vector machine |
| 基金项目: |
| 作者 | 单位 | E-mail | | 江伟 | 天津医科大学肿瘤医院分子影像与核医学科, 国家肿瘤临床医学研究中心, 天津市"肿瘤防治"重点实验室, 天津市恶性肿瘤临床医学研究中心, 天津 300060 | | | 徐文贵 | 天津医科大学肿瘤医院分子影像与核医学科, 国家肿瘤临床医学研究中心, 天津市"肿瘤防治"重点实验室, 天津市恶性肿瘤临床医学研究中心, 天津 300060 | wenguixy@163.com | | 张瑞平 | 天津医科大学肿瘤医院放疗科, 国家肿瘤临床医学研究中心, 天津市"肿瘤防治"重点实验室, 天津市恶性肿瘤临床医学研究中心, 天津 300060 | | | 朱磊 | 天津医科大学肿瘤医院分子影像与核医学科, 国家肿瘤临床医学研究中心, 天津市"肿瘤防治"重点实验室, 天津市恶性肿瘤临床医学研究中心, 天津 300060 | |
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| 中文摘要: |
| 目的 基于PET代谢参数构建预测模型，探讨其鉴别诊断孤立性肺良恶性病变的价值。方法 回顾性分析接受18F-FDG PET/CT检查的135例孤立性肺病变患者，测量病灶代谢参数，包括肿瘤代谢体积（MTV）、最大标准化摄取值（SUVmax）、标准化摄取值峰值（SUVpeak）、平均标准化摄取值（SUVmean）和标准化糖酵解总量（SUVtlg），以及瘦体质量SUV（SUL），包括SULmax、SULpeak、SULmean和SULtlg。利用支持向量机（SVM）对PET代谢参数构建模型，以赤池信息准则筛选最优化模型。绘制ROC曲线，评价模型对肺良恶性病变的诊断价值，以置换检验进行内部验证。结果 最终获得2个最优化模型（AIC值均为－232.92），分别称为Mgroup A（纳入参数为MTV、SUVpeak和SUVtlg）和Mgroup B（纳入参数为MTV、SUVpeak和SULtlg）。Mgroup A模型诊断肺良恶性病变的AUC为0.865（P=0.021），灵敏度82.72%，特异度83.33%，准确率82.96%；Mgroup B模型的AUC为0.863（P=0.030），灵敏度82.72%，特异度83.33%，准确率82.96%；2个模型间AUC差异无统计学意义（P=0.294）。置换检验提示模型均稳定可靠。结论 基于PET代谢参数构建SVM模型对肺孤立性良恶性病变具有较好的鉴别诊断效能，但脂肪校正不能提高代谢参数的诊断效能。 |
| 英文摘要: |
| Objective To establish mathematical prediction models based on PET metabolic parameters, and to explore their value for differentiating benign and malignant solitary pulmonary lesions. Methods Data of 135 patients with solitary pulmonary lesions who underwent 18F-FDG PET/CT scan were retrospectively analyzed. PET metabolic parameters of the lesions were obtained, including metabolic tumor volume (MTV), maximum standardized uptake value (SUVmax), peak standardized uptake value (SUVpeak), mean standardized uptake value (SUVmean) and total lesion glycolysis of standardized uptake value (SUVtlg), as well as parameters of standardized uptake normalized to lean body mass (SUL), including SULmax, SULpeak, SULmean and SULtlg. The parameters above were used to establish support vector machine (SVM) models, which were selected according to the Akaike's information criterion (AIC). The diagnostic performances of the models were assessed with ROC curves. The permutation test was used for internal validation. Results Two sets of optimization models were obtained and recorded as Mgroup A (include MTV, SUVpeak and SUVtlg) and Mgroup B (include MTV, SUVpeak and SULtlg). AUC of Mgroup A model was 0.865 (P=0.021), with the sensitivity of 82.72%, specificity of 83.33% and diagnostic accuracy of 82.96%, of Mgroup B model was 0.863 (P=0.030), with the sensitivity of 82.72%, specificity of 83.33% and diagnostic accuracy of 82.96%, respectively. There was no statistically significant difference of AUC between the two models (P=0.294). Both models were reliable evaluated with the permutation test. Conclusion SVM models based on PET metabolic parameters can be used for differential diagnosis of benign and malignant solitary pulmonary lesions, whereas metabolic parameters corrected by lean body mass bring no remarkable improvement on diagnostic efficacy. |
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