Contrast-enhanced CT-based radiomics nomogram for differentiation of low-risk and high-risk thymomas_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

REN Qinglin ^1,2 , HE Wenbo ² , YUE Jiarui ² , XIAO Hongbi ² ,  SHU Yusheng ²

1. Dalian Medical University, Dalian, 116000, Liaoning, P. R. China;
2. Department of Thoracic Surgery, Northern Jiangsu People's Hospital, Yangzhou, 225000, Jiangsu, P. R. China;

Corresponding author：

SHU Yusheng, Email: shuyusheng65@163.com

Keywords：

Thymoma; radiomics; contrast-enhanced CT; nomogram

DOI：

10.7507/1007-4848.202308054

Video：

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Objective To develop a radiomics nomogram based on contrast-enhanced CT (CECT) for preoperative prediction of high-risk and low-risk thymomas. Methods Clinical data of patients with thymoma who underwent surgical resection and pathological confirmation at Northern Jiangsu People's Hospital from January 2018 to February 2023 were retrospectively analyzed. Feature selection was performed using the Pearson correlation coefficient and least absolute shrinkage and selection operator (LASSO) method. An ExtraTrees classifier was used to construct the radiomics signature model and the radiomics signature. Univariate and multivariable logistic regression was applied to analyze clinical-radiological characteristics and identify variables for developing a clinical model. The radiomics nomogram model was developed by combining the radiomics signature and clinical features. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value, and positive predictive value. Calibration curves and decision curves were plotted to assess model accuracy and clinical values. Results A total of 120 patients including 59 females and 61 males with an average age of 56.30±12.10 years. There were 84 patients in the training group and 36 in the validation group, 62 in the low-risk thymoma group and 58 in the high-risk thymoma group. Radiomics features (1 038 in total) were extracted from the arterial phase of CECT scans, among which 6 radiomics features were used to construct the radiomics signature. The radiomics nomogram model, combining clinical-radiological characteristics and the radiomics signature, achieved an AUC of 0.872 in the training group and 0.833 in the validation group. Decision curve analysis demonstrated better clinical efficacy of the radiomics nomogram than the radiomics signature and clinical model. Conclusion The radiomics nomogram based on CECT showed good diagnostic value in distinguishing high-risk and low-risk thymoma, which may provide a noninvasive and efficient method for clinical decision-making.

Citation： REN Qinglin, HE Wenbo, YUE Jiarui, XIAO Hongbi, SHU Yusheng. Contrast-enhanced CT-based radiomics nomogram for differentiation of low-risk and high-risk thymomas. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2024, 31(8): 1109-1118. doi: 10.7507/1007-4848.202308054 Copy

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2.	Marx A, Chan JK, Coindre JM, et al. The 2015 World Health Organization classification of tumors of the thymus: Continuity and changes. J Thorac Oncol, 2015, 10(10): 1383-1395.
3.	Meurgey A, Girard N, Merveilleux du Vignaux C, et al. Assessment of the ITMIG statement on the WHO histological classification and of the eighth TNM staging of thymic epithelial tumors of a series of 188 thymic epithelial tumors. J Thorac Oncol, 2017, 12(10): 1571-1581.
4.	Carter BW, Benveniste MF, Madan R, et al. IASLC/ITMIG staging system and lymph node map for thymic epithelial neoplasms. Radiographics, 2017, 37(3): 758-776.
5.	Giaccone G, Wilmink H, Paul MA, et al. Systemic treatment of malignant thymoma: A decade experience at a single institution. Am J Clin Oncol, 2006, 29(4): 336-344.
6.	Roden AC, Yi ES, Jenkins SM, et al. Reproducibility of 3 histologic classifications and 3 staging systems for thymic epithelial neoplasms and its effect on prognosis. Am J Surg Pathol, 2015, 39(4): 427-441.
7.	Kondo K, Yoshizawa K, Tsuyuguchi M, et al. WHO histologic classification is a prognostic indicator in thymoma. Ann Thorac Surg, 2004, 77(4): 1183-1188.
8.	Chen G, Marx A, Chen WH, et al. New WHO histologic classification predicts prognosis of thymic epithelial tumors: A clinicopathologic study of 200 thymoma cases from China. Cancer, 2002, 95(2): 420-429.
9.	Ettinger DS, Wood DE, Akerley W, et al. Non-small cell lung cancer, version 3.2022, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw, 2022, 20(5): 497-530.
10.	Falkson CB, Bezjak A, Darling G, et al. The management of thymoma: A systematic review and practice guideline. J Thorac Oncol, 2009, 4(7): 911-919.
11.	Diakos CI, Charles KA, McMillan DC, et al. Cancer-related inflammation and treatment effectiveness. Lancet Oncol, 2014, 15(11): e493-e503.
12.	Mantovani A, Allavena P, Sica A, et al. Cancer-related inflammation. Nature, 2008, 454(7203): 436-444.
13.	Takahashi Y, Horio H, Hato T, et al. Prognostic significance of preoperative neutrophil-lymphocyte ratios in patients with stage Ⅰ non-small cell lung cancer after complete resection. Ann Surg Oncol, 2015, 22(Suppl 3): S1324-S1331.
14.	Templeton AJ, McNamara MG, Šeruga B, et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: A systematic review and meta-analysis. J Natl Cancer Inst, 2014, 106(6): dju124.
15.	Seki S, Koyama H, Ohno Y, et al. Diffusion-weighted MR imaging vs. multi-detector row CT: Direct comparison of capability for assessment of management needs for anterior mediastinal solitary tumors. Eur J Radiol, 2014, 83(5): 835-842.
16.	Han X, Gao W, Chen Y, et al. Relationship between computed tomography imaging features and clinical characteristics, Masaoka-Koga stages, and World Health Organization histological classifications of thymoma. Front Oncol, 2019, 9: 1041.
17.	Jeong YJ, Lee KS, Kim J, et al. Does CT of thymic epithelial tumors enable us to differentiate histologic subtypes and predict prognosis?AJR Am J Roentgenol, 2004, 183(2): 283-289.
18.	Sui H, Liu L, Li X, et al. CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions. J Thorac Dis, 2019, 11(5): 1809-1818.
19.	Mayerhoefer ME, Materka A, Langs G, et al. Introduction to Radiomics. J Nucl Med, 2020, 61(4): 488-495.
20.	Zheng YM, Li J, Liu S, et al. MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland. Eur Radiol, 2021, 31(6): 4042-4052.
21.	Li J, Zhang T, Ma J, et al. Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors. Front Oncol, 2022, 12: 934735.
22.	Marx A, Hohenberger P, Hoffmann H, et al. The autoimmune regulator AIRE in thymoma biology: Autoimmunity and beyond. J Thorac Oncol, 2010, 5(10 Suppl 4): S266-S272.
23.	Masaoka A, Monden Y, Nakahara K, et al. Follow-up study of thymomas with special reference to their clinical stages. Cancer, 1981, 48(11): 2485-2492.
24.	Conforti F, Pala L, Giaccone G, et al. Thymic epithelial tumors: From biology to treatment. Cancer Treat Rev, 2020, 86: 102014.
25.	Xiao G, Rong WC, Hu YC, et al. MRI radiomics analysis for predicting the pathologic classification and TNM staging of thymic epithelial tumors: A pilot study. AJR Am J Roentgenol, 2020, 214(2): 328-340.
26.	Iannarelli A, Sacconi B, Tomei F, et al. Analysis of CT features and quantitative texture analysis in patients with thymic tumors: Correlation with grading and staging. Radiol Med, 2018, 123(5): 345-350.
27.	Liu J, Yin P, Wang S, et al. CT-based radiomics signatures for predicting the risk categorization of thymic epithelial tumors. Front Oncol, 2021, 11: 628534.
28.	Araujo-Filho JAB, Mayoral M, Zheng J, et al. CT radiomic features for predicting resectability and TNM staging in thymic epithelial tumors. Ann Thorac Surg, 2022, 113(3): 957-965.
29.	Feng XL, Wang SZ, Chen HH, et al. Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study. Lung Cancer, 2022, 166: 150-160.
30.	Shang L, Wang F, Gao Y, et al. Machine-learning classifiers based on non-enhanced computed tomography radiomics to differentiate anterior mediastinal cysts from thymomas and low-risk from high-risk thymomas: A multi-center study. Front Oncol, 2022 Nov 24: 12: 1043163.
31.	Shu Z, Mao D, Song Q, et al. Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer. Eur Radiol, 2022, 32(2): 1002-1013.

1. Scorsetti M, Leo F, Trama A, et al. Thymoma and thymic carcinomas. Crit Rev Oncol Hematol, 2016, 99: 332-350.
2. Marx A, Chan JK, Coindre JM, et al. The 2015 World Health Organization classification of tumors of the thymus: Continuity and changes. J Thorac Oncol, 2015, 10(10): 1383-1395.
3. Meurgey A, Girard N, Merveilleux du Vignaux C, et al. Assessment of the ITMIG statement on the WHO histological classification and of the eighth TNM staging of thymic epithelial tumors of a series of 188 thymic epithelial tumors. J Thorac Oncol, 2017, 12(10): 1571-1581.
4. Carter BW, Benveniste MF, Madan R, et al. IASLC/ITMIG staging system and lymph node map for thymic epithelial neoplasms. Radiographics, 2017, 37(3): 758-776.
5. Giaccone G, Wilmink H, Paul MA, et al. Systemic treatment of malignant thymoma: A decade experience at a single institution. Am J Clin Oncol, 2006, 29(4): 336-344.
6. Roden AC, Yi ES, Jenkins SM, et al. Reproducibility of 3 histologic classifications and 3 staging systems for thymic epithelial neoplasms and its effect on prognosis. Am J Surg Pathol, 2015, 39(4): 427-441.
7. Kondo K, Yoshizawa K, Tsuyuguchi M, et al. WHO histologic classification is a prognostic indicator in thymoma. Ann Thorac Surg, 2004, 77(4): 1183-1188.
8. Chen G, Marx A, Chen WH, et al. New WHO histologic classification predicts prognosis of thymic epithelial tumors: A clinicopathologic study of 200 thymoma cases from China. Cancer, 2002, 95(2): 420-429.
9. Ettinger DS, Wood DE, Akerley W, et al. Non-small cell lung cancer, version 3.2022, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw, 2022, 20(5): 497-530.
10. Falkson CB, Bezjak A, Darling G, et al. The management of thymoma: A systematic review and practice guideline. J Thorac Oncol, 2009, 4(7): 911-919.
11. Diakos CI, Charles KA, McMillan DC, et al. Cancer-related inflammation and treatment effectiveness. Lancet Oncol, 2014, 15(11): e493-e503.
12. Mantovani A, Allavena P, Sica A, et al. Cancer-related inflammation. Nature, 2008, 454(7203): 436-444.
13. Takahashi Y, Horio H, Hato T, et al. Prognostic significance of preoperative neutrophil-lymphocyte ratios in patients with stage Ⅰ non-small cell lung cancer after complete resection. Ann Surg Oncol, 2015, 22(Suppl 3): S1324-S1331.
14. Templeton AJ, McNamara MG, Šeruga B, et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: A systematic review and meta-analysis. J Natl Cancer Inst, 2014, 106(6): dju124.
15. Seki S, Koyama H, Ohno Y, et al. Diffusion-weighted MR imaging vs. multi-detector row CT: Direct comparison of capability for assessment of management needs for anterior mediastinal solitary tumors. Eur J Radiol, 2014, 83(5): 835-842.
16. Han X, Gao W, Chen Y, et al. Relationship between computed tomography imaging features and clinical characteristics, Masaoka-Koga stages, and World Health Organization histological classifications of thymoma. Front Oncol, 2019, 9: 1041.
17. Jeong YJ, Lee KS, Kim J, et al. Does CT of thymic epithelial tumors enable us to differentiate histologic subtypes and predict prognosis?AJR Am J Roentgenol, 2004, 183(2): 283-289.
18. Sui H, Liu L, Li X, et al. CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions. J Thorac Dis, 2019, 11(5): 1809-1818.
19. Mayerhoefer ME, Materka A, Langs G, et al. Introduction to Radiomics. J Nucl Med, 2020, 61(4): 488-495.
20. Zheng YM, Li J, Liu S, et al. MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland. Eur Radiol, 2021, 31(6): 4042-4052.
21. Li J, Zhang T, Ma J, et al. Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors. Front Oncol, 2022, 12: 934735.
22. Marx A, Hohenberger P, Hoffmann H, et al. The autoimmune regulator AIRE in thymoma biology: Autoimmunity and beyond. J Thorac Oncol, 2010, 5(10 Suppl 4): S266-S272.
23. Masaoka A, Monden Y, Nakahara K, et al. Follow-up study of thymomas with special reference to their clinical stages. Cancer, 1981, 48(11): 2485-2492.
24. Conforti F, Pala L, Giaccone G, et al. Thymic epithelial tumors: From biology to treatment. Cancer Treat Rev, 2020, 86: 102014.
25. Xiao G, Rong WC, Hu YC, et al. MRI radiomics analysis for predicting the pathologic classification and TNM staging of thymic epithelial tumors: A pilot study. AJR Am J Roentgenol, 2020, 214(2): 328-340.
26. Iannarelli A, Sacconi B, Tomei F, et al. Analysis of CT features and quantitative texture analysis in patients with thymic tumors: Correlation with grading and staging. Radiol Med, 2018, 123(5): 345-350.
27. Liu J, Yin P, Wang S, et al. CT-based radiomics signatures for predicting the risk categorization of thymic epithelial tumors. Front Oncol, 2021, 11: 628534.
28. Araujo-Filho JAB, Mayoral M, Zheng J, et al. CT radiomic features for predicting resectability and TNM staging in thymic epithelial tumors. Ann Thorac Surg, 2022, 113(3): 957-965.
29. Feng XL, Wang SZ, Chen HH, et al. Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study. Lung Cancer, 2022, 166: 150-160.
30. Shang L, Wang F, Gao Y, et al. Machine-learning classifiers based on non-enhanced computed tomography radiomics to differentiate anterior mediastinal cysts from thymomas and low-risk from high-risk thymomas: A multi-center study. Front Oncol, 2022 Nov 24: 12: 1043163.
31. Shu Z, Mao D, Song Q, et al. Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer. Eur Radiol, 2022, 32(2): 1002-1013.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Contrast-enhanced CT-based radiomics nomogram for differentiation of low-risk and high-risk thymomas

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