Verification, comparison and melioration of different prediction models for solitary pulmonary nodule_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

BAO Tong ¹ , XIAO Fei ¹ , GUO Yongqing ¹ , SHI Bin ¹ , SONG Zhiyi ¹ , LIANG Chaoyang ¹ , SUN Hongliang ² ,  LIU Deruo ¹

1. Department of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, 100029, P.R.China;
2. Department of Radiology, China-Japan Friendship Hospital, Beijing, 100029, P.R.China;

Corresponding author：

LIU Deruo, Email: deruoliu@vip.sina.com

Keywords：

Solitary pulmonary nodule; lung cancer; prediction model

DOI：

10.7507/1007-4848.201711079

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To identify risk factors that affect the verification of malignancy in patients with solitary pulmonary nodule (SPN) and verify different prediction models for malignant probability of SPN. Methods We retrospectively analyzed the clinical data of 117 SPN patients with definite postoperative pathological diagnosis who underwent surgical procedure in China-Japan Friendship Hospital from March to September 2017. There were 59 males and 58 females aged 59.10±11.31 years ranging from 24 to 83 years. Imaging features of the nodule including maximum diameter, location, spiculation, lobulation, calcification and serum level of CEA and Cyfra21-1 were assessed as potential risk factors. Univariate analysis was used to establish statistical correlation between risk factors and postoperative pathological diagnosis. Receiver operating characteristic (ROC) curve was drawn by different predictive models for the malignant probability of SPN to get areas under the curves (AUC), sensitivity, specificity, positive predictive values, negative predictive values for each model. The predictive effectiveness of each model was statistically assessed subsequently. Results Among 117 patients, 93 (79.5%) were malignant and 24 (20.5%) were benign. Statistical difference was found between the benign and malignant group in age, maximum diameter, serum level of CEA and Cyfra21-1, spiculation, lobulation and calcification of the nodules. The AUC value was 0.813±0.051 (Mayo model), 0.697±0.066 (VA model) and 0.854±0.045 (Peking University People's Hospital model), respectively. Conclusion Age, maximum diameter of the nodule, serum level of CEA and Cyfra21-1, spiculation, lobulation and calcification are potential independent risk factors associated with the malignant probability of SPN. Peking University People's Hospital model is of high accuracy and clinical value for patients with SPN. Adding serum index into the prediction model as a new risk factor and adjusting the weight of age in the model may improve the accuracy of prediction for SPN.

Citation： BAO Tong, XIAO Fei, GUO Yongqing, SHI Bin, SONG Zhiyi, LIANG Chaoyang, SUN Hongliang, LIU Deruo. Verification, comparison and melioration of different prediction models for solitary pulmonary nodule. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2018, 25(6): 471-476. doi: 10.7507/1007-4848.201711079 Copy

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2.	Tan BB, Flaherty KR, Kazerooni EA, et al. The solitary pulmonary nodule. Chest, 2003, 123(1 Suppl): 89S-96S.
3.	Nair A, Hansell DM. European and North American lung cancer screening experience and implications for pulmonary nodule management. Eur Radiol, 2011, 21(12): 2445-2454.
4.	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.
5.	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.
6.	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.
7.	Li Y, Wang J. A mathematical model for predicting malignancy of solitary pulmonary nodules. World J Surg, 2012, 36(4): 830-835.
8.	Schultz EM, Sanders GD, Trotter PR, et al. Validation of two models to estimate the probability of malignancy in patients with solitary pulmonary nodules. Thorax, 2008, 63(4): 335-341.
9.	Mery CM, Pappas AN, Bueno R, et al. Relationship between a history of antecedent cancer and the probability of malignancy for a solitary pulmonary nodule. Chest, 2004, 125(6): 2175-2181.
10.	Mikita K, Saito H, Sakuma Y, et al. Growth rate of lung cancer recognized as small solid nodule on initial CT findings. Eur J Radiol, 2012, 81(4): e548-e553.
11.	Gould MK, Fletcher J, Iannettoni MD, et al. Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines (2nd edition). Chest, 2007, 132(3 Suppl): 108S-130S.
12.	Yonemori K, Tateishi U, Uno H, et al. Development and validation of diagnostic prediction model for solitary pulmonary nodules. Respirology, 2007, 12(6): 856-862.
13.	Swensen SJ, Viggiano RW, Midthun DE, et al. Lung nodule enhancement at CT: multicenter study. Radiology, 2000, 214(1): 73-80.
14.	Albert RH, Russell JJ. Evaluation of the solitary pulmonary nodule. Am Fam Physician, 2009, 80(8): 827-831.
15.	Ambrosini V, Nicolini S, Caroli P, et al. PET/CT imaging in different types of lung cancer: an overview. Eur J Radiol, 2012, 81(5): 988-1001.
16.	Shen J, Liu Z, Todd NW, et al. Diagnosis of lung cancer in individuals with solitary pulmonary nodules by plasma microRNA biomarkers. BMC Cancer, 2011, 11: 374.
17.	Vineis P, Hoek G, Krzyzanowski M, et al. Air pollution and risk of lung cancer in a prospective study in Europe. Int J Cancer, 2006, 119(1): 169-174.
18.	Dela Cruz CS, Tanoue LT, Matthay RA. Lung cancer: epidemiology, etiology, and prevention. Clin Chest Med, 2011, 32(4): 605-644.

1. Wahidi MM, Govert JA, Goudar RK, et al. Evidence for the treatment of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines (2nd edition). Chest, 2007, 132(3 Suppl): 94S-107S.
2. Tan BB, Flaherty KR, Kazerooni EA, et al. The solitary pulmonary nodule. Chest, 2003, 123(1 Suppl): 89S-96S.
3. Nair A, Hansell DM. European and North American lung cancer screening experience and implications for pulmonary nodule management. Eur Radiol, 2011, 21(12): 2445-2454.
4. 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.
5. 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.
6. 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.
7. Li Y, Wang J. A mathematical model for predicting malignancy of solitary pulmonary nodules. World J Surg, 2012, 36(4): 830-835.
8. Schultz EM, Sanders GD, Trotter PR, et al. Validation of two models to estimate the probability of malignancy in patients with solitary pulmonary nodules. Thorax, 2008, 63(4): 335-341.
9. Mery CM, Pappas AN, Bueno R, et al. Relationship between a history of antecedent cancer and the probability of malignancy for a solitary pulmonary nodule. Chest, 2004, 125(6): 2175-2181.
10. Mikita K, Saito H, Sakuma Y, et al. Growth rate of lung cancer recognized as small solid nodule on initial CT findings. Eur J Radiol, 2012, 81(4): e548-e553.
11. Gould MK, Fletcher J, Iannettoni MD, et al. Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines (2nd edition). Chest, 2007, 132(3 Suppl): 108S-130S.
12. Yonemori K, Tateishi U, Uno H, et al. Development and validation of diagnostic prediction model for solitary pulmonary nodules. Respirology, 2007, 12(6): 856-862.
13. Swensen SJ, Viggiano RW, Midthun DE, et al. Lung nodule enhancement at CT: multicenter study. Radiology, 2000, 214(1): 73-80.
14. Albert RH, Russell JJ. Evaluation of the solitary pulmonary nodule. Am Fam Physician, 2009, 80(8): 827-831.
15. Ambrosini V, Nicolini S, Caroli P, et al. PET/CT imaging in different types of lung cancer: an overview. Eur J Radiol, 2012, 81(5): 988-1001.
16. Shen J, Liu Z, Todd NW, et al. Diagnosis of lung cancer in individuals with solitary pulmonary nodules by plasma microRNA biomarkers. BMC Cancer, 2011, 11: 374.
17. Vineis P, Hoek G, Krzyzanowski M, et al. Air pollution and risk of lung cancer in a prospective study in Europe. Int J Cancer, 2006, 119(1): 169-174.
18. Dela Cruz CS, Tanoue LT, Matthay RA. Lung cancer: epidemiology, etiology, and prevention. Clin Chest Med, 2011, 32(4): 605-644.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Verification, comparison and melioration of different prediction models for solitary pulmonary nodule

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