新型非实性肺小结节恶性概率预测模型的构建与验证
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摘要
背景与目的数学预测模型是判断肺小结节恶性概率的有效工具。伴随肺癌流行病学趋势的改变,以非实性肺小结节为影像学表现的早期肺癌检出率逐年升高,准确鉴别并及时治疗干预可有效改善预后。本研究旨在专门针对非实性肺小结节构建新型恶性概率预测模型,为有创诊疗提供客观依据,并尽量避免不必要的侵袭性操作及其可能造成的严重后果。方法回顾性分析自2013年1月-2018年4月,单中心经穿刺活检或手术切除获得明确病理诊断的362例非实性肺小结节病例资料,包括临床基本资料、血清肿瘤标记物和影像学特征等。病例分两组,应用建模组数据做单因素分析和二分类Logistic回归,判定独立危险因素,建立预测模型;应用验证组数据验证模型预测价值并与其他模型比较。结果 362例非实性肺小结节病例中,313例(86.5%)确诊为非典型腺瘤样增生(atypical adenomatous hyperplasia, AAH)/原位腺癌(adenocarcinoma in situ, AIS)、微浸润腺癌(minimally invasive adenocarcinoma, MIA)或浸润性腺癌,49例诊断为良性病变。年龄、血清肿瘤标记物癌胚抗原(carcino-embryonic antigen, CEA)和Cyfra21-1、肿瘤实性成分比值(consolidation tumor ratio, CTR)、分叶征和钙化被确定为独立危险因素。模型受试者工作曲线下面积为0.894。预测灵敏度为87.6%,特异度为69.7%,阳性预测94.8%,阴性预测值为46.9%。经验证模型预测价值显著优于VA、Brock和GMUFH模型。结论本研究建立的新型非实性肺小结节恶性概率预测模型具备较高的诊断灵敏度和阳性预测值。经初步验证,其预测价值优于传统模型。未来经大样本验证后,可用作高危非实性肺小结节活检或手术切除前的初筛方法,具备临床应用价值。
Background and objective Mathematical predictive model is an effective method for preliminarily identifying the malignant pulmonary nodules. As the epidemiological trend of lung cancer changes, the detection rate of groundglass-opacity(GGO) like early stage lung cancer is increasing rapidly, timely and proper clinical management can effectively improve the patients' prognosis. Our study aims to establish a novel predictive model of malignancy for non-solid pulmonary nodules, which would provide an objective evidence for invasive procedure and avoid unnecessary operation and the consequences. Methods We retrospectively analyzed the basic demographics, serum tumor markers and imaging features of 362 cases of non-solid pulmonary nodule from January 2013 to April 2018. All nodules received biopsy or surgical resection, and got pathological diagnosis. Cases were randomly divided into two groups. The modeling group was used for univariate analysis and logistic regression to determine independent risk factors and establish the predictive model. Data of the validation group was used to validate the predictive value and make a comparison with other models. Results Of the 362 cases with nonsolid pulmonary nodule, 313(86.5%) cases were diagnosed as AAH/AIS, MIA or invasive adenocarcinoma, 49 cases were diagnosed as benign lesions. Age, serum tumor markers CEA and Cyfra21-1, consolidation tumor ratio value, lobulation and calcification were identified as independent risk factors. The AUC value of the ROC curve was 0.894, the predictive sensitivity and specificity were 87.6%, 69.7%, the positive and negative predictive value were 94.8%, 46.9%. The validated predictive value is significantly better than that of the VA, Brock and GMUFH models. Conclusion Proved with high predictive sensitivity and positive predictive value, this novel model could help enable preliminarily screening of "high-risk" non-solid pulmonarynodules before biopsy or surgical excision, and minimize unnecessary invasive procedure. This model achieved preferable predictive value, might have great potential for clinical application.
Background and objective Mathematical predictive model is an effective method for preliminarily identifying the malignant pulmonary nodules. As the epidemiological trend of lung cancer changes, the detection rate of groundglass-opacity(GGO) like early stage lung cancer is increasing rapidly, timely and proper clinical management can effectively improve the patients' prognosis. Our study aims to establish a novel predictive model of malignancy for non-solid pulmonary nodules, which would provide an objective evidence for invasive procedure and avoid unnecessary operation and the consequences. Methods We retrospectively analyzed the basic demographics, serum tumor markers and imaging features of 362 cases of non-solid pulmonary nodule from January 2013 to April 2018. All nodules received biopsy or surgical resection, and got pathological diagnosis. Cases were randomly divided into two groups. The modeling group was used for univariate analysis and logistic regression to determine independent risk factors and establish the predictive model. Data of the validation group was used to validate the predictive value and make a comparison with other models. Results Of the 362 cases with nonsolid pulmonary nodule, 313(86.5%) cases were diagnosed as AAH/AIS, MIA or invasive adenocarcinoma, 49 cases were diagnosed as benign lesions. Age, serum tumor markers CEA and Cyfra21-1, consolidation tumor ratio value, lobulation and calcification were identified as independent risk factors. The AUC value of the ROC curve was 0.894, the predictive sensitivity and specificity were 87.6%, 69.7%, the positive and negative predictive value were 94.8%, 46.9%. The validated predictive value is significantly better than that of the VA, Brock and GMUFH models. Conclusion Proved with high predictive sensitivity and positive predictive value, this novel model could help enable preliminarily screening of "high-risk" non-solid pulmonarynodules before biopsy or surgical excision, and minimize unnecessary invasive procedure. This model achieved preferable predictive value, might have great potential for clinical application.
引文
[1]MacMahon H, Naidich D P, Goo JM, et al. Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images:From the Fleischner Society 2017. Radiology, 2017, 284(1):228-243. doi:10.1148/radiol.2017161659
[2]National Lung Screening Trial Research T, Aberle DR, Adams AM, et al.Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5):395-409. doi:10.1056/NEJMoal 102873
[3]Bach PB, Mirkin JN, Oliver TK, et al. Benefits and harms of CT screening for lung cancer:a systematic review. JAMA, 2012, 307(22):2418-2429. doi:10.1001/jama.2012.5521
[4]Wood DE, Kazerooni EA, Baum SL, et al. Lung Cancer Screening, Version3.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw, 2018, 16(4):412-441. doi:10.6004/jnccn.2018.0020
[5]Gould MK, Donington J, Lynch WR, et al. Evaluation of individuals with pulmonary nodules:when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed:American College of Chest Physicians evidence-based clinical practice guidelines. Chest, 2013, 143(5 Suppl):e93S-e120S. doi:10.1378/chest.12-2351
[6]Phua CK, Sim WY, Sen Tee K, et al. Evaluation of pulmonary nodules in Asian population. J Thorac Dis, 2016, 8(5):950-957. doi:10.21037/jtd.2016.03.12
[7]Bai C, Choi CM, Chu CM, et al. Evaluation of Pulmonary Nodules:Clinical Practice Consensus Guidelines for Asia. Chest, 2016, 150(4):877-893. doi:10.1016/j.chest.2016.02.650
[8]Zhang RF, Zhang Y, Wen FB, et al. Analysis of pathological types and clinical epidemiology of 6,058 patients with lung cancer. Zhongguo Fei Ai Za Zhi,2016, 19(3):129-135.[张仁锋,张岩,温丰标,等. 6,058例肺癌患者病理类型和临床流行病学特征的分析.中国肺癌杂志,2016, 19(3):129-135.] doi:10.3779/j.issn.1009-3419.2016.03.03
[9]Madsen PH, Holdgaard PC, Christensen JB, et al. Clinical utility of F-18FDG PET-CT in the initial evaluation of lung cancer. Eur J Nucl Med Mol Imaging, 2016, 43(11):2084-2097. doi:10.1007/s00259-016-3407-4
[10]Wood DE, Eapen GA, Ettinger DS, et al. Lung cancer screening. J Natl Compr Canc Netw, 2012, 10(2):240-265.
[11]Rami-Porta R, Bole jack V, Crowley J, et al. The IASLC Lung Cancer Staging Project:Proposals for the Revisions of the T Descriptors in the Forthcoming Eighth Edition of the TNM Classification for Lung Cancer. J Thorac Oncol,2015,10(7):990-1003. doi:10.1097/JT0.0000000000000559
[12]Swensen SJ, Silverstein MD, Ilstrup DM, et al.The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med, 1997, 157(8):849-855.
[13]Gould MK, Ananth L, Barnett PG, et al. A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules. Chest, 2007, 131(2):383-388. doi:10.1378/chest.06-1261
[14]McWilliams A, Tammemagi MC, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med, 2013,369(10):910-919. doi:10.1056/NEJMoa1214726
[15]Li Y, Chen KZ, Sui XZ, et al. Establishment of a mathematical prediction model to evaluate the probability of malignancy or benign in patients with solitary pulmonary nodules. Beijing Da Xue Xue Bao Yi Xue Ban, 2011,43(3):450-454.[李运,陈克终,隋锡朝,等.孤立性肺结节良恶性判断数学预测模型的建立.北京大学学报(医学版),2011,43(3):450-454.] doi:10.3969/j.issn. 1671-167X.2011.03.027
[16]Zhang M, Zhuo N, Guo Z, et al. Establishment of a mathematic model for predicting malignancy in solitary pulmonary nodules. J Thorac Dis, 2015,7(10):1833-1841. doi:10.3978/j.issn.2072-1439.2015.10.56
[17]Sawada S, Yamashita N, Sugimoto R, et al. Long-term outcomes of patients with ground-glass opacities detected using CT scanning. Chest, 2017,151(2):308-315. doi:10.1016/j.chest.2016.07.007
[18]Chang B, Hwang JH, Choi YH, et al. Natural history of pure ground-glass opacity lung nodules detected by low-dose CT scan. Chest, 2013, 143(1):172-178. doi:10.1378/chest.11-2501
[19]Moon Y, Sung SW, Lee KY, et al. Clinicopathological characteristics and prognosis of non-lepidic invasive adenocarcinoma presenting as ground glass opacity nodule. J Thorac Dis, 2016, 8(9):2562-2570. doi:10.21037/jtd.2016.08.46
[20]Hattori A, Matsunaga T, Takamochi K, et al. Neither maximum tumor size nor solid component size is prognostic in part-solid lung cancer:impact of tumor size should be applied exclusively to solid lung cancer. Ann Thorac Surg,2016, 102(2):407-415. doi:10.1016/j.athoracsur.2016.02.074
[21]Xiao F, Liu D, Guo Y, et al. Novel and convenient method to evaluate the character of solitary pulmonary nodule-comparison of three mathematical prediction models and further stratification of risk factors. PloS one, 2013,8(10):e78271.doi:10.1371/journal.pone.0078271
[22]Hu H, Wang Q, Tang H, et al. Multi-slice computed tomography characteristics of solitary pulmonary ground-glass nodules:Differences between malignant and benign. Thorac Cancer, 2016, 7(1):80-87. doi:10. 1111/1759-7714.12280
[23]Takenaka T, Yamazaki K, Miura N, et al. The prognostic impact of tumor volume in patients with clinical stage la non-small cell lung cancer. J Thorac Oncol, 2016, 11(7):1074-1080. doi:10.1016/j.jtho.2016.02.005
[24]Hanagiri T, Sugaya M, Takenaka M, et al. Preoperative CYFRA 21-1 and CEA as prognostic factors in patients with stageⅠnon-small cell lung cancer.Lung cancer, 2011, 74(1):112-117. doi:10.1016/j.lungcan.2011.02.001
[25]Okamura K, Takayama K, Izumi M, et al. Diagnostic value of CEA and CYFRA 21-1 tumor markers in primary lung cancer. Lung Cancer, 2013,80(1):45-49. doi:10.1016/j.lungcan.2013.01.002
[26]Xiang YW, Sun YF, Gao W, et al. Establishment of a predicting model to evaluate the probability of malignancy or benign in patients with solid solitary pulmonary nodules. Zhonghua Yi Xue Za Zhi, 2016, 96(17):1354-1358.[项杨威,孙益峰,高文,等.实性孤立性肺结节良恶性预测模型的建立.中华医学杂志,2016, 96(17):1354-1358.] doi:10.3760/cma.j.issn.0376-2491.2016.17.011
[27]Li X, Zhang Q,Jin X, et al. Combining serum miRNAs, CEA, andCYFRA21-1 with imaging and clinical features to distinguish benign and malignant pulmonary nodules:a pilot study. World J Surg Oncol, 2017,15(1):107. doi:10.1186/s12957-017-1171-y
[28]Zheng B, Zhou X, Chen J, et al. A Modified Model for Preoperatively Predicting Malignancy of Solitary Pulmonary Nodules:An Asia Cohort Study. Ann Thorac Surg, 2015, 100(1):288-294. doi:10.1016/j.athoracsur.2015.03.071
[2]National Lung Screening Trial Research T, Aberle DR, Adams AM, et al.Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5):395-409. doi:10.1056/NEJMoal 102873
[3]Bach PB, Mirkin JN, Oliver TK, et al. Benefits and harms of CT screening for lung cancer:a systematic review. JAMA, 2012, 307(22):2418-2429. doi:10.1001/jama.2012.5521
[4]Wood DE, Kazerooni EA, Baum SL, et al. Lung Cancer Screening, Version3.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw, 2018, 16(4):412-441. doi:10.6004/jnccn.2018.0020
[5]Gould MK, Donington J, Lynch WR, et al. Evaluation of individuals with pulmonary nodules:when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed:American College of Chest Physicians evidence-based clinical practice guidelines. Chest, 2013, 143(5 Suppl):e93S-e120S. doi:10.1378/chest.12-2351
[6]Phua CK, Sim WY, Sen Tee K, et al. Evaluation of pulmonary nodules in Asian population. J Thorac Dis, 2016, 8(5):950-957. doi:10.21037/jtd.2016.03.12
[7]Bai C, Choi CM, Chu CM, et al. Evaluation of Pulmonary Nodules:Clinical Practice Consensus Guidelines for Asia. Chest, 2016, 150(4):877-893. doi:10.1016/j.chest.2016.02.650
[8]Zhang RF, Zhang Y, Wen FB, et al. Analysis of pathological types and clinical epidemiology of 6,058 patients with lung cancer. Zhongguo Fei Ai Za Zhi,2016, 19(3):129-135.[张仁锋,张岩,温丰标,等. 6,058例肺癌患者病理类型和临床流行病学特征的分析.中国肺癌杂志,2016, 19(3):129-135.] doi:10.3779/j.issn.1009-3419.2016.03.03
[9]Madsen PH, Holdgaard PC, Christensen JB, et al. Clinical utility of F-18FDG PET-CT in the initial evaluation of lung cancer. Eur J Nucl Med Mol Imaging, 2016, 43(11):2084-2097. doi:10.1007/s00259-016-3407-4
[10]Wood DE, Eapen GA, Ettinger DS, et al. Lung cancer screening. J Natl Compr Canc Netw, 2012, 10(2):240-265.
[11]Rami-Porta R, Bole jack V, Crowley J, et al. The IASLC Lung Cancer Staging Project:Proposals for the Revisions of the T Descriptors in the Forthcoming Eighth Edition of the TNM Classification for Lung Cancer. J Thorac Oncol,2015,10(7):990-1003. doi:10.1097/JT0.0000000000000559
[12]Swensen SJ, Silverstein MD, Ilstrup DM, et al.The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med, 1997, 157(8):849-855.
[13]Gould MK, Ananth L, Barnett PG, et al. A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules. Chest, 2007, 131(2):383-388. doi:10.1378/chest.06-1261
[14]McWilliams A, Tammemagi MC, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med, 2013,369(10):910-919. doi:10.1056/NEJMoa1214726
[15]Li Y, Chen KZ, Sui XZ, et al. Establishment of a mathematical prediction model to evaluate the probability of malignancy or benign in patients with solitary pulmonary nodules. Beijing Da Xue Xue Bao Yi Xue Ban, 2011,43(3):450-454.[李运,陈克终,隋锡朝,等.孤立性肺结节良恶性判断数学预测模型的建立.北京大学学报(医学版),2011,43(3):450-454.] doi:10.3969/j.issn. 1671-167X.2011.03.027
[16]Zhang M, Zhuo N, Guo Z, et al. Establishment of a mathematic model for predicting malignancy in solitary pulmonary nodules. J Thorac Dis, 2015,7(10):1833-1841. doi:10.3978/j.issn.2072-1439.2015.10.56
[17]Sawada S, Yamashita N, Sugimoto R, et al. Long-term outcomes of patients with ground-glass opacities detected using CT scanning. Chest, 2017,151(2):308-315. doi:10.1016/j.chest.2016.07.007
[18]Chang B, Hwang JH, Choi YH, et al. Natural history of pure ground-glass opacity lung nodules detected by low-dose CT scan. Chest, 2013, 143(1):172-178. doi:10.1378/chest.11-2501
[19]Moon Y, Sung SW, Lee KY, et al. Clinicopathological characteristics and prognosis of non-lepidic invasive adenocarcinoma presenting as ground glass opacity nodule. J Thorac Dis, 2016, 8(9):2562-2570. doi:10.21037/jtd.2016.08.46
[20]Hattori A, Matsunaga T, Takamochi K, et al. Neither maximum tumor size nor solid component size is prognostic in part-solid lung cancer:impact of tumor size should be applied exclusively to solid lung cancer. Ann Thorac Surg,2016, 102(2):407-415. doi:10.1016/j.athoracsur.2016.02.074
[21]Xiao F, Liu D, Guo Y, et al. Novel and convenient method to evaluate the character of solitary pulmonary nodule-comparison of three mathematical prediction models and further stratification of risk factors. PloS one, 2013,8(10):e78271.doi:10.1371/journal.pone.0078271
[22]Hu H, Wang Q, Tang H, et al. Multi-slice computed tomography characteristics of solitary pulmonary ground-glass nodules:Differences between malignant and benign. Thorac Cancer, 2016, 7(1):80-87. doi:10. 1111/1759-7714.12280
[23]Takenaka T, Yamazaki K, Miura N, et al. The prognostic impact of tumor volume in patients with clinical stage la non-small cell lung cancer. J Thorac Oncol, 2016, 11(7):1074-1080. doi:10.1016/j.jtho.2016.02.005
[24]Hanagiri T, Sugaya M, Takenaka M, et al. Preoperative CYFRA 21-1 and CEA as prognostic factors in patients with stageⅠnon-small cell lung cancer.Lung cancer, 2011, 74(1):112-117. doi:10.1016/j.lungcan.2011.02.001
[25]Okamura K, Takayama K, Izumi M, et al. Diagnostic value of CEA and CYFRA 21-1 tumor markers in primary lung cancer. Lung Cancer, 2013,80(1):45-49. doi:10.1016/j.lungcan.2013.01.002
[26]Xiang YW, Sun YF, Gao W, et al. Establishment of a predicting model to evaluate the probability of malignancy or benign in patients with solid solitary pulmonary nodules. Zhonghua Yi Xue Za Zhi, 2016, 96(17):1354-1358.[项杨威,孙益峰,高文,等.实性孤立性肺结节良恶性预测模型的建立.中华医学杂志,2016, 96(17):1354-1358.] doi:10.3760/cma.j.issn.0376-2491.2016.17.011
[27]Li X, Zhang Q,Jin X, et al. Combining serum miRNAs, CEA, andCYFRA21-1 with imaging and clinical features to distinguish benign and malignant pulmonary nodules:a pilot study. World J Surg Oncol, 2017,15(1):107. doi:10.1186/s12957-017-1171-y
[28]Zheng B, Zhou X, Chen J, et al. A Modified Model for Preoperatively Predicting Malignancy of Solitary Pulmonary Nodules:An Asia Cohort Study. Ann Thorac Surg, 2015, 100(1):288-294. doi:10.1016/j.athoracsur.2015.03.071