Advances of chest CT-based radiomics in the individualized diagnosis and treatment of non-small cell lung cancer_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHAO Ke ¹ , RAO Ke ² ,  LI Shanqing ¹

1. Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, P. R. China;
2. Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, P. R. China;

Corresponding author：

LI Shanqing, Email: lishanqing@pumch.cn

Keywords：

Radiomics; non-small cell lung cancer; precision medicine; adjuvant diagnosis; individualized diagnosis and treatment; review

DOI：

10.7507/1007-4848.202106109

Video：

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Lung cancer is one of the leading causes of cancer deaths worldwide. Many options including surgery, radiotherapy, chemotherapy, targeted therapy and immunotherapy have been applied in the treatment for lung cancer patients. However, how to develop individualized treatment plans for patients and accurately determine the prognosis of patients is still a very difficult clinical problem. In recent years, radiomics, as an emerging method for medical image analysis, has gradually received the attention from researchers. It is based on the assumption that medical images contain a vast amount of biological information about patients that is difficult to identify with naked eyes but can be accessed by computer. One of the most common uses of radiomics is the diagnosis and treatment of non-small cell lung cancer (NSCLC). In this review, we reviewed the current researches on chest CT-based radiomics in the diagnosis and treatment of NSCLC and provided a brief summary of the current state of research in this field, covering various aspects of qualitative diagnosis, efficacy prediction, and prognostic analysis of lung cancer. We also briefly described the main current technical limitations of this technology with the aim of gaining a broader understanding of its potential role in the diagnosis and treatment of NSCLC and advancing its development as a tool for individualized management of NSCLC patients.

Citation： ZHAO Ke, RAO Ke, LI Shanqing. Advances of chest CT-based radiomics in the individualized diagnosis and treatment of non-small cell lung cancer. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(6): 782-787. doi: 10.7507/1007-4848.202106109 Copy

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2.	Allemani C, Matsuda T, Di Carlo V, et al. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): Analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet, 2018, 391(10125): 1023-1075.
3.	Kumar V, Gu Y, Basu S, et al. Radiomics: The process and the challenges. Magn Reson Imaging, 2012, 30(9): 1234-1248.
4.	Court L E, Fave X, Mackin D, et al. Computational resources for radiomics. Transl Cancer Res, 2016, 5(4): 340-348.
5.	Haubold J, Demircioglu A, Gratz M, et al. Non-invasive tumor decoding and phenotyping of cerebral gliomas utilizing multiparametric 18 F-FET PET-MRI and MR Fingerprinting. Eur J Nucl Med Mol Imaging, 2020, 47(6): 1435-1445.
6.	Chitalia RD, Rowland J, McDonald ES, et al. Imaging phenotypes of breast cancer heterogeneity in preoperative breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) scans predict 10-year recurrence. Clin Cancer Res, 2020, 26(4): 862-869.
7.	Li Y, Jiang J, Lu J, et al. Radiomics: A novel feature extraction method for brain neuron degeneration disease using 18 F-FDG PET imaging and its implementation for Alzheimer's disease and mild cognitive impairment. Ther Adv Neurol Disord, 2019, 12: 1756286419838682.
8.	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.
9.	Kamiya A, Murayama S, Kamiya H, et al. Kurtosis and skewness assessments of solid lung nodule density histograms: Differentiating malignant from benign nodules on CT. Jpn J Radiol, 2014, 32(1): 14-21.
10.	Choi W, Oh JH, Riyahi S, et al. Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer. Med Phys, 2018, 45(4): 1537-1549.
11.	Shen Y, Xu F, Zhu W, et al. Multiclassifier fusion based on radiomics features for the prediction of benign and malignant primary pulmonary solid nodules. Ann Transl Med, 2020, 8(5): 171.
12.	Cufer T, Ovcaricek T, O'Brien ME. Systemic therapy of advanced non-small cell lung cancer: Major-developments of the last 5-years. Eur J Cancer, 2013, 49(6): 1216-1225.
13.	Liu J, Cui J, Liu F, et al. Multi-subtype classification model for non-small cell lung cancer based on radiomics: SLS model. Med Phys, 2019, 46(7): 3091-3100.
14.	Travis WD, Brambilla E, Noguchi M, et al. International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol, 2011, 6(2): 244-285.
15.	Luo T, Xu K, Zhang Z, et al. Radiomic features from computed tomography to differentiate invasive pulmonary adenocarcinomas from non-invasive pulmonary adenocarcinomas appearing as part-solid ground-glass nodules. Chin J Cancer Res, 2019, 31(2): 329-338.
16.	She Y, Zhang L, Zhu H, et al. The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules. Eur Radiol, 2018, 28(12): 5121-5128.
17.	Yang X, Dong X, Wang J, et al. Computed tomography-based radiomics signature: A Potential indicator of epidermal growth factor receptor mutation in pulmonary adenocarcinoma appearing as a subsolid nodule. Oncologist, 2019, 24(11): e1156.
18.	Liu G, Xu Z, Ge Y, et al. 3D radiomics predicts EGFR mutation, exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma. Transl Lung Cancer Res, 2020, 9(4): 1212-1224.
19.	Song L, Zhu Z, Mao L, et al. Clinical, conventional CT and radiomic feature-based machine learning models for predicting ALK rearrangement status in lung adenocarcinoma patients. Front Oncol, 2020, 10: 369.
20.	Yoon HJ, Sohn I, Cho JH, et al. Decoding tumor phenotypes for ALK, ROS1, and RET fusions in lung adenocarcinoma using a radiomics approach. medicine (Baltimore), 2015, 94(41): e1753.
21.	Ettinger DS, Wood DE, Aggarwal C, et al. NCCN guidelines insights: Non-small cell lung cancer, version 1. 2020. J Natl Compr Canc Netw, 2019, 17(12): 1464-1472.
22.	Darling GE, Allen MS, Decker PA, et al. Randomized trial of mediastinal lymph node sampling versus complete lymphadenectomy during pulmonary resection in the patient with N0 or N1 (less than hilar) non-small cell carcinoma: Results of the American College of Surgery Oncology Group Z0030 trial. J Thorac Cardiovasc Surg, 2011, 141(3): 662-670.
23.	He L, Huang Y, Yan L, et al. Radiomics-based predictive risk score: A scoring system for preoperatively predicting risk of lymph node metastasis in patients with resectable non-small cell lung cancer. Chin J Cancer Res, 2019, 31(4): 641-652.
24.	Cong M, Feng H, Ren JL, et al. Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage ⅠA non-small cell lung cancer. Lung Cancer, 2020, 139: 73-79.
25.	Dercle L, Fronheiser M, Lu L, et al. Identification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics. Clin Cancer Res, 2020, 26(9): 2151-2162.
26.	Rizvi NA, Mazières J, Planchard D, et al. Activity and safety of nivolumab, an anti-PD-1 immune checkpoint inhibitor, for patients with advanced, refractory squamous non-small-cell lung cancer (CheckMate 063): A phase 2, single-arm trial. Lancet Oncol, 2015, 16(3): 257-265.
27.	Takada K, Okamoto T, Shoji F, et al. Clinical significance of PD-L1 protein expression in surgically resected primary lung adenocarcinoma. J Thorac Oncol, 2016, 11(11): 1879-1890.
28.	Yoon J, Suh YJ, Han K, et al. Utility of CT radiomics for prediction of PD-L1 expression in advanced lung adenocarcinomas. Thorac Cancer, 2020, 11(4): 993-1004.
29.	Trebeschi S, Drago SG, Birkbak NJ, et al. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann Oncol, 2019, 30(6): 998-1004.
30.	Asamura H, Chansky K, Crowley J, et al. The International Association for the Study of Lung Cancer lung cancer staging project: Proposals for the revision of the N descriptors in the forthcoming 8th edition of the TNM classification for lung cancer. J Thorac Oncol, 2015, 10(12): 1675-1684.
31.	Huang Y, Liu Z, He L, et al. Radiomics Signature: A potential biomarker for the prediction of disease-free survival in early-stage (Ⅰ or Ⅱ) non-small cell lung cancer. Radiology, 2016, 281(3): 947-957.
32.	van Timmeren JE, van Elmpt W, Leijenaar RTH, et al. Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer patients: Evaluation of the added prognostic value for overall survival and locoregional recurrence. Radiother Oncol, 2019, 136: 78-85.
33.	Kadoya N, Tanaka S, Kajikawa T, et al. Homology-based radiomic features for prediction of the prognosis of lung cancer based on CT-based radiomics. Med Phys, 2020, 47(5): 2197-2205.
34.	Ger RB, Zhou S, Chi PM, et al. Comprehensive investigation on controlling for CT imaging variabilities in radiomics studies. Sci Rep, 2018, 8(1): 13047.
35.	Owens CA, Peterson CB, Tang C, et al. Lung tumor segmentation methods: Impact on the uncertainty of radiomics features for non-small cell lung cancer. PLoS One, 2018, 13(10): e0205003.
36.	Parmar C, Rios Velazquez E, Leijenaar R, et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One, 2014, 9(7): e102107.
37.	Coroller TP, Agrawal V, Huynh E, et al. Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC. J Thorac Oncol, 2017, 12(3): 467-476.
38.	Sun W, Jiang M, Dang J, et al. Effect of machine learning methods on predicting NSCLC overall survival time based on radiomics analysis. Radiat Oncol, 2018, 13(1): 197.

1. Gao S, Li N, Wang S, et al. Lung cancer in People's Republic of China. J Thorac Oncol, 2020, 15(10): 1567-1576.
2. Allemani C, Matsuda T, Di Carlo V, et al. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): Analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet, 2018, 391(10125): 1023-1075.
3. Kumar V, Gu Y, Basu S, et al. Radiomics: The process and the challenges. Magn Reson Imaging, 2012, 30(9): 1234-1248.
4. Court L E, Fave X, Mackin D, et al. Computational resources for radiomics. Transl Cancer Res, 2016, 5(4): 340-348.
5. Haubold J, Demircioglu A, Gratz M, et al. Non-invasive tumor decoding and phenotyping of cerebral gliomas utilizing multiparametric 18 F-FET PET-MRI and MR Fingerprinting. Eur J Nucl Med Mol Imaging, 2020, 47(6): 1435-1445.
6. Chitalia RD, Rowland J, McDonald ES, et al. Imaging phenotypes of breast cancer heterogeneity in preoperative breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) scans predict 10-year recurrence. Clin Cancer Res, 2020, 26(4): 862-869.
7. Li Y, Jiang J, Lu J, et al. Radiomics: A novel feature extraction method for brain neuron degeneration disease using 18 F-FDG PET imaging and its implementation for Alzheimer's disease and mild cognitive impairment. Ther Adv Neurol Disord, 2019, 12: 1756286419838682.
8. 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.
9. Kamiya A, Murayama S, Kamiya H, et al. Kurtosis and skewness assessments of solid lung nodule density histograms: Differentiating malignant from benign nodules on CT. Jpn J Radiol, 2014, 32(1): 14-21.
10. Choi W, Oh JH, Riyahi S, et al. Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer. Med Phys, 2018, 45(4): 1537-1549.
11. Shen Y, Xu F, Zhu W, et al. Multiclassifier fusion based on radiomics features for the prediction of benign and malignant primary pulmonary solid nodules. Ann Transl Med, 2020, 8(5): 171.
12. Cufer T, Ovcaricek T, O'Brien ME. Systemic therapy of advanced non-small cell lung cancer: Major-developments of the last 5-years. Eur J Cancer, 2013, 49(6): 1216-1225.
13. Liu J, Cui J, Liu F, et al. Multi-subtype classification model for non-small cell lung cancer based on radiomics: SLS model. Med Phys, 2019, 46(7): 3091-3100.
14. Travis WD, Brambilla E, Noguchi M, et al. International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol, 2011, 6(2): 244-285.
15. Luo T, Xu K, Zhang Z, et al. Radiomic features from computed tomography to differentiate invasive pulmonary adenocarcinomas from non-invasive pulmonary adenocarcinomas appearing as part-solid ground-glass nodules. Chin J Cancer Res, 2019, 31(2): 329-338.
16. She Y, Zhang L, Zhu H, et al. The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules. Eur Radiol, 2018, 28(12): 5121-5128.
17. Yang X, Dong X, Wang J, et al. Computed tomography-based radiomics signature: A Potential indicator of epidermal growth factor receptor mutation in pulmonary adenocarcinoma appearing as a subsolid nodule. Oncologist, 2019, 24(11): e1156.
18. Liu G, Xu Z, Ge Y, et al. 3D radiomics predicts EGFR mutation, exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma. Transl Lung Cancer Res, 2020, 9(4): 1212-1224.
19. Song L, Zhu Z, Mao L, et al. Clinical, conventional CT and radiomic feature-based machine learning models for predicting ALK rearrangement status in lung adenocarcinoma patients. Front Oncol, 2020, 10: 369.
20. Yoon HJ, Sohn I, Cho JH, et al. Decoding tumor phenotypes for ALK, ROS1, and RET fusions in lung adenocarcinoma using a radiomics approach. medicine (Baltimore), 2015, 94(41): e1753.
21. Ettinger DS, Wood DE, Aggarwal C, et al. NCCN guidelines insights: Non-small cell lung cancer, version 1. 2020. J Natl Compr Canc Netw, 2019, 17(12): 1464-1472.
22. Darling GE, Allen MS, Decker PA, et al. Randomized trial of mediastinal lymph node sampling versus complete lymphadenectomy during pulmonary resection in the patient with N0 or N1 (less than hilar) non-small cell carcinoma: Results of the American College of Surgery Oncology Group Z0030 trial. J Thorac Cardiovasc Surg, 2011, 141(3): 662-670.
23. He L, Huang Y, Yan L, et al. Radiomics-based predictive risk score: A scoring system for preoperatively predicting risk of lymph node metastasis in patients with resectable non-small cell lung cancer. Chin J Cancer Res, 2019, 31(4): 641-652.
24. Cong M, Feng H, Ren JL, et al. Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage ⅠA non-small cell lung cancer. Lung Cancer, 2020, 139: 73-79.
25. Dercle L, Fronheiser M, Lu L, et al. Identification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics. Clin Cancer Res, 2020, 26(9): 2151-2162.
26. Rizvi NA, Mazières J, Planchard D, et al. Activity and safety of nivolumab, an anti-PD-1 immune checkpoint inhibitor, for patients with advanced, refractory squamous non-small-cell lung cancer (CheckMate 063): A phase 2, single-arm trial. Lancet Oncol, 2015, 16(3): 257-265.
27. Takada K, Okamoto T, Shoji F, et al. Clinical significance of PD-L1 protein expression in surgically resected primary lung adenocarcinoma. J Thorac Oncol, 2016, 11(11): 1879-1890.
28. Yoon J, Suh YJ, Han K, et al. Utility of CT radiomics for prediction of PD-L1 expression in advanced lung adenocarcinomas. Thorac Cancer, 2020, 11(4): 993-1004.
29. Trebeschi S, Drago SG, Birkbak NJ, et al. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann Oncol, 2019, 30(6): 998-1004.
30. Asamura H, Chansky K, Crowley J, et al. The International Association for the Study of Lung Cancer lung cancer staging project: Proposals for the revision of the N descriptors in the forthcoming 8th edition of the TNM classification for lung cancer. J Thorac Oncol, 2015, 10(12): 1675-1684.
31. Huang Y, Liu Z, He L, et al. Radiomics Signature: A potential biomarker for the prediction of disease-free survival in early-stage (Ⅰ or Ⅱ) non-small cell lung cancer. Radiology, 2016, 281(3): 947-957.
32. van Timmeren JE, van Elmpt W, Leijenaar RTH, et al. Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer patients: Evaluation of the added prognostic value for overall survival and locoregional recurrence. Radiother Oncol, 2019, 136: 78-85.
33. Kadoya N, Tanaka S, Kajikawa T, et al. Homology-based radiomic features for prediction of the prognosis of lung cancer based on CT-based radiomics. Med Phys, 2020, 47(5): 2197-2205.
34. Ger RB, Zhou S, Chi PM, et al. Comprehensive investigation on controlling for CT imaging variabilities in radiomics studies. Sci Rep, 2018, 8(1): 13047.
35. Owens CA, Peterson CB, Tang C, et al. Lung tumor segmentation methods: Impact on the uncertainty of radiomics features for non-small cell lung cancer. PLoS One, 2018, 13(10): e0205003.
36. Parmar C, Rios Velazquez E, Leijenaar R, et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One, 2014, 9(7): e102107.
37. Coroller TP, Agrawal V, Huynh E, et al. Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC. J Thorac Oncol, 2017, 12(3): 467-476.
38. Sun W, Jiang M, Dang J, et al. Effect of machine learning methods on predicting NSCLC overall survival time based on radiomics analysis. Radiat Oncol, 2018, 13(1): 197.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Advances of chest CT-based radiomics in the individualized diagnosis and treatment of non-small cell lung cancer

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