Application of computed tomography‑based radiomics signature analysis in the prediction of the response of small cell lung cancer patients to first‑line chemotherapy

Wei,Haifeng; Yang,Fengchang; Liu,Zhe; Sun,Shuna; Xu,Fangwei; Liu,Peng; Li,Huifen; Liu,Qiao; Qiao,Xu; Wang,Ximing

doi:10.3892/etm.2019.7357

Application of computed tomography‑based radiomics signature analysis in the prediction of the response of small cell lung cancer patients to first‑line chemotherapy

Authors:
- Haifeng Wei
- Fengchang Yang
- Zhe Liu
- Shuna Sun
- Fangwei Xu
- Peng Liu
- Huifen Li
- Qiao Liu
- Xu Qiao
- Ximing Wang
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Affiliations: Diagnostic Room of Computer Tomography, Shandong Medical Imaging Research Institute, Shandong Provincial Key Laboratory of Diagnosis and Treatment of Cardio‑Cerebral Vascular Disease, Shandong University, Jinan, Shandong 250021, P.R. China, Department of Radiology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan, Shandong 250117, P.R. China, Department of Pharmacy, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250012, P.R. China, Department of Dermatology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250012, P.R. China, Department of Radiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250012, P.R. China, Department of Natural Drugs, School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250355, P.R. China, Key Laboratory of Experimental Teratology, Ministry of Education and Department of Molecular Medicine and Genetics, Shandong University School of Medicine, Jinan, Shandong 250012, P.R. China, Department of Biomedical Engineering, Shandong University, Jinan, Shandong 250061, P.R. China
Published online on: March 7, 2019 https://doi.org/10.3892/etm.2019.7357
Pages: 3621-3629
Copyright: © Wei et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The aim of the present study was to investigate the utility of a computed tomography (CT)‑based radiomics signature for the early prediction of the tumor response of small cell lung cancer (SCLC) patients to chemotherapy. A dataset including 92 patients from a clinical trial was retrospectively assembled. All of the patients received the standard first‑line regimen of etoposide and cisplatin. According to the Response Evaluation Criteria in Solid Tumors 1.1, the patients were divided into two groups: Response and no response groups. A total of 21 radiomics features were extracted from CT images prior to and after two cycles of chemotherapy and a radiomics signature was constructed via a binary logistic regression model. The area under the receiver operating characteristics curve (AUC) was determined to evaluate the performance of the radiomics signature to predict the response to chemotherapy. The clinicopathological factors associated with chemotherapy in patients with SCLC were also evaluated, and a predictive model was established using a binary logistic regression analysis. The 21 radiological features were used to establish a radiomics signature that was significantly associated with the efficacy of SCLC chemotherapy (P<0.05). The performance of the radiomics signature to predict the chemotherapy efficacy (AUC=0.797) was better than that of the model using clinicopathological parameters (AUC=0.670). Therefore, the present study demonstrated that radiomics features may be promising prognostic imaging biomarkers to predict the response of SCLC patients to chemotherapy and may thus be utilized to guide appropriate treatment planning.

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1	Siegel RL, Miller KD and Jemal A: Cancer Statistics, 2017. CA Cancer J Clin. 67:7–30. 2017. View Article : Google Scholar : PubMed/NCBI
2	Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ and He J: Cancer statistics in China, 2015. CA Cancer J Clin. 66:115–132. 2016. View Article : Google Scholar : PubMed/NCBI
3	van Meerbeeck JP, Fennell DA and De Ruysscher DK: Small-cell lung cancer. Lancet. 378:1741–1755. 2011. View Article : Google Scholar : PubMed/NCBI
4	Alvarado-Luna G and Morales-Espinosa D: Treatment for small cell lung cancer, where are we now?-a review. Transl Lung Cancer Res. 5:26–38. 2016.PubMed/NCBI
5	Bunn PA Jr, Minna JD, Augustyn A, Gazdar AF, Ouadah Y, Krasnow MA, Berns A, Brambilla E, Rekhtman N, Massion PP, et al: Small cell lung cancer: Can recent advances in biology and molecular biology be translated into improved outcomes? J Thorac Oncol. 11:453–474. 2016. View Article : Google Scholar : PubMed/NCBI
6	Polański R, Hodgkinson CL, Fusi A, Nonaka D, Priest L, Kelly P, Trapani F, Bishop PW, White A, Critchlow SE, et al: Activity of the monocarboxylate transporter 1 inhibitor AZD3965 in small cell lung cancer. Clin Cancer Res. 20:926–937. 2014. View Article : Google Scholar : PubMed/NCBI
7	Sen T, Tong P, Stewart CA, Cristea S, Valliani A, Shames DS, Redwood AB, Fan YH, Li L, Glisson BS, et al: CHK1 inhibition in small-cell lung cancer produces single-agent activity in biomarker-defined disease subsets and combination activity with cisplatin or olaparib. Cancer Res. 77:3870–3884. 2017. View Article : Google Scholar : PubMed/NCBI
8	Ross JS, Wang K, Elkadi OR, Tarasen A, Foulke L, Sheehan CE, Otto GA, Palmer G, Yelensky R, Lipson D, et al: Next-generation sequencing reveals frequent consistent genomic alterations in small cell undifferentiated lung cancer. J Clin Pathol. 67:772–776. 2014. View Article : Google Scholar : PubMed/NCBI
9	Qiu YF, Liu ZG, Yang WJ, Zhao Y, Tang J, Tang WZ, Jin Y, Li F, Zhong R and Wang H: Research progress in the treatment of small cell lung cancer. J Cancer. 8:29–38. 2017. View Article : Google Scholar : PubMed/NCBI
10	Lu HY, Wang XJ and Mao WM: Targeted therapies in small cell lung cancer. Oncol Lett. 5:3–11. 2013.PubMed/NCBI
11	Hiltermann TJ, Pore MM, van den Berg A, Timens W, Boezen HM, Liesker JJ, Schouwink JH, Wijnands WJ, Kerner GS, Kruyt FA, et al: Circulating tumor cells in small-cell lung cancer: A predictive and prognostic factor. Ann Oncol. 23:2937–2942. 2012. View Article : Google Scholar : PubMed/NCBI
12	Hou JM, Krebs MG, Lancashire L, Sloane R, Backen A, Swain RK, Priest LJ, Greystoke A, Zhou C, Morris K, et al: Clinical significance and molecular characteristics of circulating tumor cells and circulating tumor microemboli in patients with small-cell lung cancer. J Clin Oncol. 30:525–532. 2012. View Article : Google Scholar : PubMed/NCBI
13	Zhu LR, Li J, Chen P, Jiang Q and Tang XP: Clinical significance of plasma fibrinogen and D-dimer in predicting the chemotherapy efficacy and prognosis for small cell lung cancer patients. Clin Transl Oncol. 18:178–188. 2016. View Article : Google Scholar : PubMed/NCBI
14	Yang J, Jiao S, Kang J, Li R and Zhang G: Application of serum NY-ESO-1 antibody assay for early SCLC diagnosis. Int J Clin Exp Pathol. 8:14959–14964. 2015.PubMed/NCBI
15	Buil-Bruna N, López-Picazo JM, Moreno-Jiménez M, Martin-Algarra S, Ribba B and Trocóniz IF: A population pharmacodynamic model for lactate dehydrogenase and neuron specific enolase to predict tumor progression in small cell lung cancer patients. AAPS J. 16:609–619. 2014. View Article : Google Scholar : PubMed/NCBI
16	Gillies RJ, Kinahan PE and Hricak H: Radiomics: Images are more than pictures, they are data. Radiology. 278:563–577. 2016. View Article : Google Scholar : PubMed/NCBI
17	Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A and Aerts HJ: Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer. 48:441–446. 2012. View Article : Google Scholar : PubMed/NCBI
18	Chen B, Zhang R, Gan Y, Yang L and Li W: Development and clinical application of radiomics in lung cancer. Radiat Oncol. 12:1542017. View Article : Google Scholar : PubMed/NCBI
19	Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, et al: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 5:40062014. View Article : Google Scholar : PubMed/NCBI
20	Fave X, Zhang L, Yang J, Mackin D, Balter P, Gomez D, Followill D, Jones AK, Stingo F, Liao Z, et al: Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep. 7:5882017. View Article : Google Scholar : PubMed/NCBI
21	Wang H, Guo XH, Jia ZW, Li HK, Liang ZG, Li KC and He Q: Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image. Eur J Radiol. 74:124–129. 2010. View Article : Google Scholar : PubMed/NCBI
22	Ferreira Junior JR, Koenigkam-Santos M, Cipriano FEG, Fabro AT and Azevedo-Marques PM: Radiomics-based features for pattern recognition of lung cancer histopathology and metastases. Comput Methods Programs Biomed. 159:23–30. 2018. View Article : Google Scholar : PubMed/NCBI
23	Wu W, Parmar C, Grossmann P, Quackenbush J, Lambin P, Bussink J, Mak R and Aerts HJ: Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol. 6:712016. View Article : Google Scholar : PubMed/NCBI
24	Ganeshan B, Abaleke S, Young RC, Chatwin CR and Miles KA: Texture analysis of non-small cell lung cancer on unenhanced computed tomography: Initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging. 10:137–143. 2010. View Article : Google Scholar : PubMed/NCBI
25	Coroller TP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RT, Hermann G, Lambin P, Haibe-Kains B, Mak RH and Aerts HJ: CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol. 114:345–350. 2015. View Article : Google Scholar : PubMed/NCBI
26	Fried DV, Tucker SL, Zhou S, Liao Z, Mawlawi O, Ibbott G and Court LE: Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. Int J Radiat Oncol Biol Phys. 90:834–842. 2014. View Article : Google Scholar : PubMed/NCBI
27	Gevaert O, Xu J, Hoang CD, Leung AN, Xu Y, Quon A, Rubin DL, Napel S and Plevritis SK: Non-small cell lung cancer: Identifying prognostic imaging biomarkers by leveraging public gene expression microarray data-methods and preliminary results. Radiology. 264:387–396. 2012. View Article : Google Scholar : PubMed/NCBI
28	Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, et al: New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer. 45:228–247. 2009. View Article : Google Scholar : PubMed/NCBI
29	Ohno Y, Fujisawa Y, Koyama H, Kishida Y, Seki S, Sugihara N and Yoshikawa T: Dynamic contrast-enhanced perfusion area-detector CT assessed with various mathematical models: Its capability for therapeutic outcome prediction for non-small cell lung cancer patients with chemoradiotherapy as compared with that of FDG-PET/CT. Eur J Radiol. 86:83–91. 2017. View Article : Google Scholar : PubMed/NCBI
30	Zelen M: Keynote address on biostatistics and data retrieval. Cancer Chemother Rep 3. 4:31–42. 1973.PubMed/NCBI
31	Huang X, Cheng Z, Huang Y, Liang C, He L, Ma Z, Chen X, Wu X, Li Y, Liang C and Liu Z: CT-based radiomics signature to discriminate high-grade from low-grade colorectal adenocarcinoma. Acad Radiol. 25:1285–1297. 2018. View Article : Google Scholar : PubMed/NCBI
32	Haralick RM, Shanmugam K and Dinstein I: Textural features for image classification. IEEE Trans Syst Man Cybern. 3:610–621. 1973. View Article : Google Scholar
33	Kurani AS, Xu DH, Furst J and Raicu DS: Co-occurrence matrices for volumetric data. Proceedings of the 7th IASTED International Conference on Computer Graphics and Imaging. CGIM. (Kauai, Hawaii). 447–452. 2004.
34	Amadasun M and King R: Textural features corresponding to textural properties. IEEE Trans Systems Man Cybern. 19:1264–1274. 1989. View Article : Google Scholar
35	Xu R, Kido S, Suga K, Hirano Y, Tachibana R, Muramatsu K, Chagawa K and Tanaka S: Texture analysis on (18)F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions. Ann Nucl Med. 28:926–935. 2014. View Article : Google Scholar : PubMed/NCBI
36	van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S and Aerts HJWL: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77:e104–e107. 2017. View Article : Google Scholar : PubMed/NCBI
37	Pesch B, Kendzia B, Gustavsson P, Jöckel KH, Johnen G, Pohlabeln H, Olsson A, Ahrens W, Gross IM, Brüske I, et al: Cigarette smoking and lung cancer-relative risk estimates for the major histological types from a pooled analysis of case-control studies. Int J Cancer. 131:1210–1219. 2012. View Article : Google Scholar : PubMed/NCBI
38	Kang H, Lee HY, Lee KS and Kim JH: Imaging-based tumor treatment response evaluation: Review of conventional, new, and emerging concepts. Korean J Radiol. 13:371–390. 2012. View Article : Google Scholar : PubMed/NCBI
39	Chong Y, Kim JH, Lee HY, Ahn YC, Lee KS, Ahn MJ, Kim J, Shim YM, Han J and Choi YL: Quantitative CT variables enabling response prediction in neoadjuvant therapy with EGFR-TKIs: Are they different from those in neoadjuvant concurrent chemoradiotherapy? PLoS One. 9:e885982014. View Article : Google Scholar : PubMed/NCBI
40	Zhong Y, Yuan M, Zhang T, Zhang YD, Li H and Yu TF: Radiomics approach to prediction of occult mediastinal lymph node metastasis of lung adenocarcinoma. AJR Am J Roentgenol. 211:109–113. 2018. View Article : Google Scholar : PubMed/NCBI
41	Bogowicz M, Riesterer O, Ikenberg K, Stieb S, Moch H, Studer G, Guckenberger M and Tanadini-Lang S: Computed tomography radiomics predicts HPV status and local tumor control after definitive radiochemotherapy in head and neck squamous cell carcinoma. Int J Radiat Oncol Biol Phys. 99:921–928. 2017. View Article : Google Scholar : PubMed/NCBI
42	Avanzo M, Stancanello J and El Naqa I: Beyond imaging: The promise of radiomics. Phys Med. 38:122–139. 2017. View Article : Google Scholar : PubMed/NCBI
43	Ha S, Choi H, Cheon GJ, Kang KW, Chung JK, Kim EE and Lee DS: Autoclustering of non-small cell lung carcinoma subtypes on (18)F-FDG PET using texture analysis: A preliminary result. Nucl Med Mol Imaging. 48:278–286. 2014. View Article : Google Scholar : PubMed/NCBI
44	Patil R, Mahadevaiah G and Dekker A: An approach toward automatic classification of tumor histopathology of non-small cell lung cancer based on radiomic features. Tomography. 2:374–377. 2016. View Article : Google Scholar : PubMed/NCBI
45	Zhou Y, He L, Huang Y, Chen S, Wu P, Ye W, Liu Z and Liang C: CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma. Abdom Radiol (NY). 42:1695–1704. 2017. View Article : Google Scholar : PubMed/NCBI
46	Huang YQ, Liang CH, He L, Tian J, Liang CS, Chen X, Ma ZL and Liu ZY: Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol. 34:2157–2164. 2016. View Article : Google Scholar : PubMed/NCBI
47	Lee G, Lee HY, Park H, Schiebler ML, van Beek EJR, Ohno Y, Seo JB and Leung A: Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art. Eur J Radiol. 86:297–307. 2017. View Article : Google Scholar : PubMed/NCBI