-
肺结节为小的局灶性、类圆形肉芽肿性疾病,其影像学表现为密度增高的阴影,可单发或多发,且不伴有肺不张、肺门肿大和胸腔积液。孤立性肺结节(solitary pulmonary nodule,SPN)无典型症状,常为单个、边界清楚、密度增高、直径≤3 cm且周围被含气肺组织包绕的软组织影[1-2]。CT可用于肺部小结节的检查。随着肺癌CT筛查的普及,检测到的结节数量逐渐增加。目前临床应用的定量预测模型可有助于辨别结节的良恶性。结合临床和CT对结节特征的描述,Brock University(简称Brock)、Mayo Clinic(简称Mayo)和Veterans Association(简称VA)等肺癌预测模型的应用为临床分析提供了极大的便利[3-5]。CT容易诊断具有良性钙化特征的结节,但对非钙化结节的诊断容易出现疑问,也不能反映SPN的代谢及病理生理学信息,PET/CT可同时进行PET和CT扫描,获得两者的融合图像,与单纯的PET相比,PET/CT可从解剖学方面对病变精确定位,既改善了PET图像的分辨率,又缩短了患者的检查时间,一次检查可同时获得CT解剖图像和PET功能图像,两种信息互补,提高了对SPN定性诊断的准确率。近年来,18F-FDG作为常用的肿瘤显像剂,能提供病灶部位代谢信息,可用于临床良恶性疾病鉴别诊断,具有良好的诊断效能,Herder预测模型是基于加入18F-FDG对正电子发射的亲合力进行18F-FDG PET/CT分析[5-7]。临床预测的不同模型适用的患者群体有所差异,Brock模型和Herder模型虽然被广泛应用,但各模型的优势缺乏相关实验的比较验证。因此,本研究重点比较不同模型在招募的患者群体中预测肺结节恶性风险的效能,为临床的合理应用提供依据。
-
120例患者中,最终确诊49例(40.8%)患有恶性结节,其中38例为原发性肺癌、11例为转移性疾病;71例(59.2%)为良性病变为,其中61例根据放射学稳定性和监测尺寸减少确诊、另外10例通过组织病理学确诊。
每个模型的ROC曲线见图1。各模型在不同条件下ROC曲线的AUC值详见表1,在不受排除标准限制的总队列中,与受排除标准限制的队列相比,每个模型的AUC值(95%CI,n)均有所下降,但两类队列的AUC值比较差异无统计学意义(Z=21.357,P=0.121)。总队列的3种不同模型的恶性概率分布见图2,其中Herder模型的数据显示为接受18F-FDG PET/CT扫描的患者。
图 1 各模型对肺结节恶性风险预测的ROC曲线 图中,A:Brock University模型;B:Veterans Association模型;C:Herder模型。
Figure 1. The receiver operating characteristic curve of risk prediction for pulmonary nodules in various models
模型名称 AUC(95%CI,n) 受排除标准限值的队列中 不受排除标准限值的总队列中 结节≤10 mm的患者中 Brcok模型 0.887(0.845~0.931,n=84)a 0.869(0.818~0.908,n=120)b 0.846(0.758~0.912,n=52)d VA模型 0.772(0.652~0.801,n=89) 0.758(0.643~0.795,n=120) 0.536(0.387~0.728,n=52) Herder模型 0.937(0.869~0.962,n=47) 0.923(0.870~0.968,n=59)c − 注:表中,ROC:受试者工作特征;AUC:曲线下面积;Brock:Brock University;VA:Veterans Association;−:无此项数据;a:与VA模型相比,Brock模型预测效能更优(Z=6.483,P=0.006),与Herder模型相比,Herder模型的预测效能更优(Z=10.645,P=0.023);b:与VA模型相比,Brock模型预测效能更优(Z=6.483,P=0.006);c:与Brock模型和VA模型相比,Herder模型的预测效能更优(Z=9.860、6.694,均P=0.070);d:与VA模型相比,Brock模型的预测效能更优(Z=8.768,P=0.0026)。 表 1 各模型在不同条件下ROC曲线的AUC结果
Table 1. The areas under the receiver operating characteristic curve of risk prediction for pulmonary nodules in various models
图 2 各模型预测肺结节恶性风险的癌变概率 图中,Brock模型:Brock University模型;VA模型:Veterans Association模型。
Figure 2. Prediction of cancer probability of malignant risk of pulmonary nodules by various models
此外,我们还比较了52例结节≤10 mm的患者(不受排除标准限制)基于CT预测模型的分析效能(表1)。
基于CT和18F-FDG PET/CT的肺癌风险预测模型对肺结节恶性风险的验证研究
Verification of malignant risk of pulmonary nodules based on CT and 18F-FDG PET/CT prediction model
-
摘要:
目的 比较基于CT的Brock模型、VA模型和基于18F-FDG PET/CT的Herder模型预测肺结节恶性风险的效能,验证模型的预测准确率。 方法 回顾性分析2009年7月至2016年7月行CT检查并经病理确诊或随访确诊的120例肺结节患者,其中可能为恶性的59例患者接受了18F-FDG PET/CT检查。绘制3种模型的受试者工作特征(ROC)曲线并计算曲线下面积(AUC)。测算肺结节(直径4~30 mm)的恶性风险。根据每个模型基于排除标准的队列和所有患者总队列的AUC,验证模型的准确性。采用MedCalc软件进行相关性分析,DeLong方法进行两条ROC的比较。 结果 120例肺结节患者中,49例患者(40.8%)患有恶性结节(31.6%原发性肺癌、8.2%转移性疾病)。在受排除标准限制的队列中,Brock和VA模型的AUC分别为 0.887和0.758,两者间的差异有统计学意义(Z=6.483,P=0.006)。 在接受18F-FDG PET/CT检查的患者中,Herder模型的AUC为0.937。当对队列中的所有患者(即包括原模型纳入标准之外的患者)测试模型时,每个模型的AUC值均有所降低,但两类队列比较差异无统计学意义(Z=21.357,P=0.121)。 对于≤10 mm的结节,Brock和VA 模型的AUC值分别为0.846和0.536,Brock模型明显优于VA 模型(Z=8.768,P=0.0026)。 结论 Brock模型可预测CT扫描中检测到的肺结节恶变的可能性,在接受18F-FDG PET/CT进行肺结节评估的患者中,Herder模型的预测效能最高。 -
关键词:
- 孤立性肺结节 /
- 正电子发射断层显像术 /
- 体层摄影术,X线计算机 /
- 肺癌风险预测模型
Abstract:Objective To compare the efficacy of the CT and 18F-FDG PET/CT models in predicting the malignant risk of pulmonary nodules and to verify the predictive accuracy of model. Methods A retrospective analysis of 120 patients with pulmonary nodules confirmed by pathological diagnosis or follow-up were conducted in this study. Among these patients, 59 patients with suspected malignancy received 18F-FDG PET/CT. The corresponding receiver operating characteristic curve for each model was plotted, and the area under the curve(AUC) was calculated. The malignant risk of patients with pulmonary nodules(4–30 mm in diameter) was measured. Model accuracy was verified based on the exclusion criteria for each model and the total cohort of all patients. MedCalc software was used for correlation analysis, and DeLong method was used for two-way comparison. Results All 120 patients with pulmonary nodules were examined. Among them, 49(40.8%) had malignant nodules(31.6% primary lung cancer and 8.2% metastatic disease). The AUC of the Brock and VA models were 0.887 and 0.758, respectively, the difference was statistically significant(Z=6.483, P=0.006). In patients receiving 18F-FDG PET/CT, the AUC of the Herder model was 0.937, which was significantly more accurate than those of the other two models. When testing the model for all patients in the cohort(i.e., patients including the original model’s inclusion criteria), the AUC value decreased but was not significant. For the Herder model, the AUC was 0.923, and the two types of cohorts were not significant(Z=21.357, P=0.121). For subcentimeter nodules, the AUC values for the Brock and VA models were 0.846 and 0.536, respectively, and the Brock model was significantly better than the VA model(Z=8.768, P=0.0026). Conclusion The Brock model showed good accuracy and was used to predict the likelihood of malignancy in nodules detected by CT scan. The Herder model was the most accurate for patients who underwent 18F-FDG PET/CT for nodule evaluation. -
表 1 各模型在不同条件下ROC曲线的AUC结果
Table 1. The areas under the receiver operating characteristic curve of risk prediction for pulmonary nodules in various models
模型名称 AUC(95%CI,n) 受排除标准限值的队列中 不受排除标准限值的总队列中 结节≤10 mm的患者中 Brcok模型 0.887(0.845~0.931,n=84)a 0.869(0.818~0.908,n=120)b 0.846(0.758~0.912,n=52)d VA模型 0.772(0.652~0.801,n=89) 0.758(0.643~0.795,n=120) 0.536(0.387~0.728,n=52) Herder模型 0.937(0.869~0.962,n=47) 0.923(0.870~0.968,n=59)c − 注:表中,ROC:受试者工作特征;AUC:曲线下面积;Brock:Brock University;VA:Veterans Association;−:无此项数据;a:与VA模型相比,Brock模型预测效能更优(Z=6.483,P=0.006),与Herder模型相比,Herder模型的预测效能更优(Z=10.645,P=0.023);b:与VA模型相比,Brock模型预测效能更优(Z=6.483,P=0.006);c:与Brock模型和VA模型相比,Herder模型的预测效能更优(Z=9.860、6.694,均P=0.070);d:与VA模型相比,Brock模型的预测效能更优(Z=8.768,P=0.0026)。 -
[1] Harzheim D, Eberhardt R, Hoffmann H, et al. The Solitary Pulmonary Nodule[J]. Respiration, 2015, 90(2): 160−172. DOI: 10.1159/000430996 [2] 张晓辉, 陈成, 曾辉, 等. 孤立性肺结节恶性概率估算临床预测模型的建立[J]. 实用癌症杂志, 2016, 31(1): 59−62. DOI: 10.3969/j.issn.1001−5930.2016.01.018
Zhang XH, Chen C, Zeng H, et al. Establishment of Clinical Prediction Model to Estimate the Probability of Malignancy in Patients with Solitary Pulmonary Nodules[J]. Pract J Cancer, 2016, 31(1): 59−62. DOI: 10.3969/j.issn.1001−5930.2016.01.018[3] Gould MK, Ananth L, Barnett PG. A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules[J]. Chest, 2007, 131(2): 383−388. DOI: 10.1378/chest.06−1261 [4] Mc Williams A, Tammemagi MC, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on first screening CT[J]. N Engl J Med, 2013, 369(10): 910−919. DOI: 10.1056/NEJMoa1214726 [5] Herder GJ, van Tinteren H, Golding RP, et al. Clinical prediction model to characterize pulmonary nodules: validation and added value of 18F-fluorodeoxyglucose positron emission tomography[J]. Chest, 2005, 128(4): 2490−2496. DOI: 10.1378/chest.128.4.2490 [6] Chandarana H, Heacock L, Rakheja R, et al. Pulmonary nodules in patients with primary malignancy: comparison of hybrid PET/MR and PET/CT imaging[J]. Radiology, 2013, 268(3): 874−881. DOI: 10.1148/radiol.13130620 [7] Li W, Pang H, Liu Q, et al. The role of 18F-FDG PET or 18F-FDG PET/CT in the evaluation of solitary pulmonary nodules[J]. Eur J Radiol, 2015, 84(10): 2032−2037. DOI: 10.1016/j.ejrad.2015.06.008 [8] Ma ZJ, Yang GZ, Wang J. Analysis of 96 cases of solitary pulmonary nodule diagnosed by MSCT[J]. Chinese-German J Clin Oncol, 2014, 13(3): 115−118. [9] Rauscher I, Eiber M, Fürst S, et al. PET/MR imaging in the detection and characterization of pulmonary lesions: technical and diagnostic evaluation in comparison to PET/CT[J]. J Nucl Med, 2014, 55(5): 724−729. DOI: 10.2967/jnumed.113.129247 [10] Quint LE, Park CH, Iannettoni MD. Solitary pulmonary nodules in patients with extrapulmonary neoplasms[J]. Radiology, 2000, 217(1): 257−261. DOI: 10.1148/radiology.217.1.r00oc20257 [11] Mehta HJ, Ravenel JG, Shaftman SR, et al. The utility of nodule volume inthe context of malignancy prediction for small pulmonary nodules[J]. Chest, 2014, 145(3): 464−472. DOI: 10.1378/chest.13−0708 [12] Al-Ameri A, Malhotra P, Thygesen H, et al. Risk of malignancy in pulmonary nodules: A validation study of four prediction models[J]. Lung Cancer, 2015, 89(1): 27−30. DOI: 10.1016/j.lungcan.2015.03.018 [13] Zhao M, Chang B, Wei Z, et al. The role of 18F-FDG uptake features in the differential diagnosis of solitary pulmonary lesions with PET/CT[J]. World J Surg Oncol, 2015, 13: 271. DOI: 10.1186/s12957−015−0679−2