Radiomics model based on CT images for distinguishing invasive lung adenocarcinoma with micropapillary or solid structure_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

 WANG Fen ¹ , ZHANG Teng ² , YUAN Mei ² ,  BO Genji ¹

1. Department of Medical Imaging, Huai'an First People's Hospital Affiliated to Nanjing Medical University, Huai'an, 223300, Jiangsu, P. R. China;
2. Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, P. R. China;

Corresponding author：

Keywords：

Radiomics; micropapillary; solid; adenocarcinoma; computed tomography

DOI：

10.7507/1007-4848.202308057

Video：

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Objective To investigate the radiomics features to distinguish invasive lung adenocarcinoma with micropapillary or solid structure. Methods A retrospective analysis was conducted on patients who received surgeries and pathologically confirmed invasive lung adenocarcinoma in our hospital from April 2016 to August 2019. The dataset was randomly divided into a training set [including a micropapillary/solid structure positive group (positive group) and a micropapillary/solid structure negative group (negative group)] and a testing set (including a positive group and a negative group) with a ratio of 7∶3. Two radiologists drew regions of interest on preoperative high-resolution CT images to extract radiomics features. Before analysis, the intraclass correlation coefficient was used to determine the stable features, and the training set data were balanced using synthetic minority oversampling technique. After mean normalization processing, further radiomics features selection was conducted using the least absolute shrinkage and selection operator algorithm, and a 5-fold cross validation was performed. Receiver operating characteristic (ROC) curves were depicted on the training and testing sets to evaluate the diagnostic performance of the radiomics model. Results A total of 340 patients were enrolled, including 178 males and 162 females with an average age of 60.31±6.69 years. There were 238 patients in the training set, including 120 patients in the positive group and 118 patients in the negative group. There were 102 patients in the testing set, including 52 patients in the positive group and 50 patients in the negative group. The radiomics model contained 107 features, with the final 2 features selected for the radiomics model, that is, Original_ glszm_ SizeZoneNonUniformityNormalized and Original_ shape_ SurfaceVolumeRatio. The areas under the ROC curve of the training and the testing sets of the radiomics model were 0.863 (95%CI 0.815-0.912) and 0.857 (95%CI 0.783-0.932), respectively. The sensitivity was 91.7% and 73.7%, the specificity was 78.8% and 84.0%, and the accuracy was 85.3% and 78.4%, respectively. Conclusion There are differences in radiomics features between invasive pulmonary adenocarcinoma with or without micropapillary and solid structures, and the radiomics model is demonstrated to be with good diagnostic value.

Citation： WANG Fen, ZHANG Teng, YUAN Mei, BO Genji. Radiomics model based on CT images for distinguishing invasive lung adenocarcinoma with micropapillary or solid structure. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2024, 31(1): 65-70. doi: 10.7507/1007-4848.202308057 Copy

1.	Luo J, Wang R, Han B, et al. Solid predominant histologic subtype and early recurrence predict poor postrecurrence survival in patients with stageⅠlung adenocarcinoma. Oncotarget, 2017, 8(4): 7050-7058.
2.	Choi SH, Jeong JY, Lee SY, et al. Clinical implication of minimal presence of solid or micropapillary subtype in early-stage lung adenocarcinoma. Thorac Cancer, 2021, 12(2): 235-244.
3.	Chen T, Luo J, Gu H, et al. Impact of solid minor histologic subtype in postsurgical prognosis of stageⅠlung adenocarcinoma. Ann Thorac Surg, 2018, 105(1): 302-308.
4.	Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
5.	Wilson R, Devaraj A. Radiomics of pulmonary nodules and lung cancer. Transl Lung Cancer Res, 2017, 6(1): 86-91.
6.	He B, Song Y, Wang L, et al. A machine learning-based prediction of the micropapillary/solid growth pattern in invasive lung adenocarcinoma with radiomics. Transl Lung Cancer Res, 2021, 10(2): 955-964.
7.	Chen LW, Yang SM, Wang HJ, et al. Prediction of micropapillary and solid pattern in lung adenocarcinoma using radiomic values extracted from near-pure histopathological subtypes. Eur Radiol, 2021, 31(7): 5127-5138.
8.	Park S, Lee SM, Noh HN, et al. Differentiation of predominant subtypes of lung adenocarcinoma using a quantitative radiomics approach on CT. Eur Radiol, 2020, 30(9): 4883-4892.
9.	Chen LW, Yang SM, Chuang CC, et al. Solid attenuation components attention deep learning model to predict micropapillary and solid patterns in lung adenocarcinomas on computed tomography. Ann Surg Oncol, 2022, 29(12): 7473-7482.
10.	Hattori A, Matsunaga T, Takamochi K, et al. Surgical resection for clinical-stageⅠradiological pure-solid lung cancer that met the current high risk criteria. Jpn J Clin Oncol, 2017, 47(7): 630-638.
11.	Hattori A, Suzuki K, Takamochi K, et al. Prognostic impact of a ground-glass opacity component in clinical stage ⅠA non-small cell lung cancer. J Thorac Cardiovasc Surg, 2021, 161(4): 1469-1480.
12.	Lefebvre TL, Ueno Y, Dohan A, et al. Development and validation of multiparametric MRI-based radiomics models for preoperative risk stratification of endometrial cancer. Radiology, 2022, 305(2): 375-386.
13.	Perez-Johnston R, Araujo-Filho JA, Connolly JG, et al. CT-based radiogenomic analysis of clinical stageⅠlung adenocarcinoma with histopathologic features and oncologic outcomes. Radiology, 2022, 303(3): 664-672.
14.	van Timmeren JE, Cester D, Tanadini-Lang S, et al. Radiomics in medical imaging: "How-to" guide and critical reflection. Insights Imaging, 2020, 11(1): 91.
15.	Wang T, She Y, Yang Y, et al. Radiomics for survival risk stratification of clinical and pathologic stageⅠA pure-solid non-small cell lung cancer. Radiology, 2022, 302(2): 425-434.
16.	Meng X, Gao D, Jin H, et al. Factors affecting volume reduction velocity for arteriovenous malformations after treatment with dose-stage stereotactic radiosurgery. Front Oncol, 2021, 11: 769533.

1. Luo J, Wang R, Han B, et al. Solid predominant histologic subtype and early recurrence predict poor postrecurrence survival in patients with stageⅠlung adenocarcinoma. Oncotarget, 2017, 8(4): 7050-7058.
2. Choi SH, Jeong JY, Lee SY, et al. Clinical implication of minimal presence of solid or micropapillary subtype in early-stage lung adenocarcinoma. Thorac Cancer, 2021, 12(2): 235-244.
3. Chen T, Luo J, Gu H, et al. Impact of solid minor histologic subtype in postsurgical prognosis of stageⅠlung adenocarcinoma. Ann Thorac Surg, 2018, 105(1): 302-308.
4. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
5. Wilson R, Devaraj A. Radiomics of pulmonary nodules and lung cancer. Transl Lung Cancer Res, 2017, 6(1): 86-91.
6. He B, Song Y, Wang L, et al. A machine learning-based prediction of the micropapillary/solid growth pattern in invasive lung adenocarcinoma with radiomics. Transl Lung Cancer Res, 2021, 10(2): 955-964.
7. Chen LW, Yang SM, Wang HJ, et al. Prediction of micropapillary and solid pattern in lung adenocarcinoma using radiomic values extracted from near-pure histopathological subtypes. Eur Radiol, 2021, 31(7): 5127-5138.
8. Park S, Lee SM, Noh HN, et al. Differentiation of predominant subtypes of lung adenocarcinoma using a quantitative radiomics approach on CT. Eur Radiol, 2020, 30(9): 4883-4892.
9. Chen LW, Yang SM, Chuang CC, et al. Solid attenuation components attention deep learning model to predict micropapillary and solid patterns in lung adenocarcinomas on computed tomography. Ann Surg Oncol, 2022, 29(12): 7473-7482.
10. Hattori A, Matsunaga T, Takamochi K, et al. Surgical resection for clinical-stageⅠradiological pure-solid lung cancer that met the current high risk criteria. Jpn J Clin Oncol, 2017, 47(7): 630-638.
11. Hattori A, Suzuki K, Takamochi K, et al. Prognostic impact of a ground-glass opacity component in clinical stage ⅠA non-small cell lung cancer. J Thorac Cardiovasc Surg, 2021, 161(4): 1469-1480.
12. Lefebvre TL, Ueno Y, Dohan A, et al. Development and validation of multiparametric MRI-based radiomics models for preoperative risk stratification of endometrial cancer. Radiology, 2022, 305(2): 375-386.
13. Perez-Johnston R, Araujo-Filho JA, Connolly JG, et al. CT-based radiogenomic analysis of clinical stageⅠlung adenocarcinoma with histopathologic features and oncologic outcomes. Radiology, 2022, 303(3): 664-672.
14. van Timmeren JE, Cester D, Tanadini-Lang S, et al. Radiomics in medical imaging: "How-to" guide and critical reflection. Insights Imaging, 2020, 11(1): 91.
15. Wang T, She Y, Yang Y, et al. Radiomics for survival risk stratification of clinical and pathologic stageⅠA pure-solid non-small cell lung cancer. Radiology, 2022, 302(2): 425-434.
16. Meng X, Gao D, Jin H, et al. Factors affecting volume reduction velocity for arteriovenous malformations after treatment with dose-stage stereotactic radiosurgery. Front Oncol, 2021, 11: 769533.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Radiomics model based on CT images for distinguishing invasive lung adenocarcinoma with micropapillary or solid structure

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