Construction of a predictive model for poorly differentiated adenocarcinoma in pulmonary nodules using CT combined with tumor markers_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

JIANG Jie , LIU Feng , WANG Bo , WANG Qin ,  ZHONG Jian

Department of Thoracic Surgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, P. R. China;

Corresponding author：

ZHONG Jian, Email: 1162014036@qq.com

Keywords：

Computed tomography; tumor markers; pulmonary nodules; poorly differentiated adenocarcinoma; predictive model; nomogram

DOI：

10.7507/1007-4848.202408069

Video：

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Objective To establish and internally validate a predictive model for poorly differentiated adenocarcinoma based on CT imaging and tumor marker results. Methods Patients with solid and partially solid lung nodules who underwent lung nodule surgery at the Department of Thoracic Surgery, the Affiliated Brain Hospital of Nanjing Medical University in 2023 were selected and randomly divided into a training set and a validation set at a ratio of 7:3. Patients' CT features, including average density value, maximum diameter, pleural indentation sign, and bronchial inflation sign, as well as patient tumor marker results, were collected. Based on postoperative pathological results, patients were divided into a poorly differentiated adenocarcinoma group and a non-poorly differentiated adenocarcinoma group. Univariate analysis and logistic regression analysis were performed on the training set to establish the predictive model. The receiver operating characteristic (ROC) curve was used to evaluate the model's discriminability, the calibration curve to assess the model's consistency, and the decision curve to evaluate the clinical value of the model, which was then validated in the validation set. Results A total of 299 patients were included, with 103 males and 196 females, with a median age of 57.00 (51.00, 67.25) years. There were 211 patients in the training set and 88 patients in the validation set. Multivariate analysis showed that carcinoembryonic antigen (CEA) value [OR=1.476, 95%CI (1.184, 1.983), P=0.002], cytokeratin 19 fragment antigen (CYFRA21-1) value [OR=1.388, 95%CI (1.084, 1.993), P=0.035], maximum tumor diameter [OR=6.233, 95%CI (1.069, 15.415), P=0.017], and average density [OR=1.083, 95%CI (1.020, 1.194), P=0.040] were independent risk factors for solid and partially solid lung nodules as poorly differentiated adenocarcinoma. Based on this, a predictive model was constructed with an area under the ROC curve of 0.896 [95%CI (0.810, 0.982)], a maximum Youden index corresponding cut-off value of 0.103, sensitivity of 0.750, and specificity of 0.936. Using the Bootstrap method for 1000 samplings, the calibration curve predicted probability was consistent with actual risk. Decision curve analysis indicated positive benefits across all prediction probabilities, demonstrating good clinical value. Conclusion For patients with solid and partially solid lung nodules, preoperative use of CT to measure tumor average density value and maximum diameter, combined with tumor markers CEA and CYFRA21-1 values, can effectively predict whether it is poorly differentiated adenocarcinoma, allowing for early intervention.

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11.	刘苗苗, 南岩东, 陈艳丽, 等. 血清肿瘤标志物和自身抗体与肺癌患者临床病理特征的相关性研究. 中华肺部疾病杂志(电子版), 2018, 11(2): 176-181.Liu MM, Nan YD, Chen YL, et al. Correlation of serum tumor markers and autoantibodies with clinicopathological features in lung cancer patients. Chin J Lung Dis (electronic edition), 2018, 11(2): 176-181.
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13.	张东军, 刘丽伟, 刘洋, 等. 胸膜凹陷征在胸膜下磨玻璃结节诊断中的价值. 东南大学学报(医学版), 2020, 39(3): 297-300.Zhang DJ, Liu LW, Liu Y, et al. Value of pleural indentation in the diagnosis of subpleural ground glass nodules. J Southeast Univ (Med Sci Ed), 2020, 39(3): 297-300.
14.	黄定品, 傅钢泽, 项益岚, 等. 纯磨玻璃肺小腺癌内异常空气支气管征与病理亚型的相关性. 医学影像学杂志, 2019, 29(12): 2047-2050.Huang DP, Fu GZ, Xiang YL, et al. Correlation between abnormal air bronchograms and pathological subtypes in small lung adenocarcinoma with pure ground glass nodule. J Med Imaging, 2019, 29(12): 2047-2050.
15.	黄丽珍, 车建华, 段相会, 等. 非小细胞肺癌患者血清CYFRA21-1、VEGF及CEA的表达及与临床病理特征的相关性研究. 现代生物医学进展, 2018, 18(7): 1344-1347.Huang LZ, Che JH, Duan XH, et al. The expression of serum CYFRA21-1, VEGF and CEA in patients with non-small cell lung cancer and their correlation with clinicopathological features. Prog Mod Biomed, 2018, 18(7): 1344-1347.
16.	金文霞, 李世良. 肺癌患者血清肿瘤标志物表达变化与临床病理特征的相关性分析. 实用癌症杂志, 2024, 39(2): 222-224, 236.Jin WX, Li SL. Correlation analysis between changes in serum tumor markers expression and clinical pathological characteristics in lung cancer patients. Pract J Cancer, 2024, 39(2): 222-224, 236.
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18.	Lee KH, Goo JM, Park SJ, et al. Correlation between the size of the solid component on thin-section CT and the invasive component on pathology in small lung adenocarcinomas manifesting as ground-glass nodules. J Thorac Oncol, 2014, 9(1): 74-82.

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024, 74(3): 229-263.
2. WHO Classification of Tumours Editorial Board. WHO classification of tumours. Thoracic tumours. 5th ed. Lyon: IARC Press, 2021.
3. 李媛, 谢惠康, 武春燕. WHO胸部肿瘤分类(第5版)中肺肿瘤部分解读. 中国癌症杂志, 2021, 31(7): 574-580.Li Y, Xie HK, Wu CY. Interpretation of lung tumours in the WHO classification of thoracic tumours (5th edition). China Oncol, 2021, 31(7): 574-580.
4. Tang ZM, Ling ZG, Wang CM, et al. Serum tumor-associated autoantibodies as diagnostic biomarkers for lung cancer: A systematic review and meta-analysis. PLoS One, 2017, 12(7): e0182117.
5. 贾金芳, 黄静, 朱晓莉. 肿瘤相关自身抗体对恶性肺结节的诊断价值. 中国肿瘤临床, 2020, 47(6): 271-276.Jia JF, Huang J, Zhu XL. Diagnostic value of tumor-associated autoantibodies for malignant pulmonary nodules. Chin J Clin Oncol, 2020, 47(6): 271-276.
6. Zhang R, Ma L, Li W, et al. Diagnostic value of multiple tumor-associated autoantibodies in lung cancer. Onco Targets Ther, 2019, 12: 457-469.
7. Huang S, Yang J, Shen N, et al. Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective. Semin Cancer Biol, 2023, 89: 30-37.
8. Bidzińska J, Szurowska E. See lung cancer with an AI. Cancers (Basel), 2023, 15(4): 1321.
9. Imielinski M, Berger AH, Hammerman PS, et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell, 2012, 150(6): 1107-1120.
10. 王璐, 刘杰克, 徐富阳, 等. 基于低剂量CT定量-定性特征预测低分化浸润性非黏液肺腺癌. 临床放射学杂志, 2023, 42(12): 1887-1894.Wang L, Liu JK, Xu FY, et al. Quantitative-semanticfeatures of low-dose CT for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma. J Clin Radiol, 2023, 42(12): 1887-1894.
11. 刘苗苗, 南岩东, 陈艳丽, 等. 血清肿瘤标志物和自身抗体与肺癌患者临床病理特征的相关性研究. 中华肺部疾病杂志(电子版), 2018, 11(2): 176-181.Liu MM, Nan YD, Chen YL, et al. Correlation of serum tumor markers and autoantibodies with clinicopathological features in lung cancer patients. Chin J Lung Dis (electronic edition), 2018, 11(2): 176-181.
12. Tan EM. Autoantibodies as reporters identifying aberrant cellular mechanisms in tumorigenesis. J Clin Invest, 2001, 108(10): 1411-1415.
13. 张东军, 刘丽伟, 刘洋, 等. 胸膜凹陷征在胸膜下磨玻璃结节诊断中的价值. 东南大学学报(医学版), 2020, 39(3): 297-300.Zhang DJ, Liu LW, Liu Y, et al. Value of pleural indentation in the diagnosis of subpleural ground glass nodules. J Southeast Univ (Med Sci Ed), 2020, 39(3): 297-300.
14. 黄定品, 傅钢泽, 项益岚, 等. 纯磨玻璃肺小腺癌内异常空气支气管征与病理亚型的相关性. 医学影像学杂志, 2019, 29(12): 2047-2050.Huang DP, Fu GZ, Xiang YL, et al. Correlation between abnormal air bronchograms and pathological subtypes in small lung adenocarcinoma with pure ground glass nodule. J Med Imaging, 2019, 29(12): 2047-2050.
15. 黄丽珍, 车建华, 段相会, 等. 非小细胞肺癌患者血清CYFRA21-1、VEGF及CEA的表达及与临床病理特征的相关性研究. 现代生物医学进展, 2018, 18(7): 1344-1347.Huang LZ, Che JH, Duan XH, et al. The expression of serum CYFRA21-1, VEGF and CEA in patients with non-small cell lung cancer and their correlation with clinicopathological features. Prog Mod Biomed, 2018, 18(7): 1344-1347.
16. 金文霞, 李世良. 肺癌患者血清肿瘤标志物表达变化与临床病理特征的相关性分析. 实用癌症杂志, 2024, 39(2): 222-224, 236.Jin WX, Li SL. Correlation analysis between changes in serum tumor markers expression and clinical pathological characteristics in lung cancer patients. Pract J Cancer, 2024, 39(2): 222-224, 236.
17. 翁绳和. 肺腺癌分化程度的相关因素分析. 中国医药科学, 2020, 10(4): 243-246.Weng SH. Analysis on related factors of differentiation degree of lung adenocarcinoma. China Med Pharm, 2020, 10(4): 243-246.
18. Lee KH, Goo JM, Park SJ, et al. Correlation between the size of the solid component on thin-section CT and the invasive component on pathology in small lung adenocarcinomas manifesting as ground-glass nodules. J Thorac Oncol, 2014, 9(1): 74-82.

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