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Contrast-Enhanced CT Texture Analysis: a New Set of Predictive Factors for Small Cell Lung Cancer

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Abstract

Purpose

The purpose of this study was to investigate the relationship between x-ray computed tomography (CT) texture features of small cell lung cancer (SCLC) and the survival of the patients.

Procedures

Eighty-eight patients with unresectable SCLCs (extended stage, 57; limited stage, 31) underwent platinum-based chemotherapy at our institution between January 2010 and 2015. All the patients were followed up for at least 18 months or until death. The CT texture features of tumor tissue were extracted from contrast-enhanced CT images taken before antitumor treatment. Receiver operating characteristic (ROC) curve analysis was used to calculate the optimal cutoff values of each texture parameter, based on which the patients were dichotomized into two subgroups to evaluate the prognostic value of each feature. Kaplan–Meier survival analysis and the log rank test were performed to compare the differences of 18-month overall survival (OS) and 6-month event-free survival (EFS) in dichotomized subgroups. Multivariate Cox regression analysis was performed to determine if the features could be taken as independent prognostic factors.

Results

A total number of 35 CT texture features were extracted from six matrixes. Four of them (GLCM-Contrast, GLCM-Dissimilarity, Histo-Energy, and Histo-Entropy) were shown to be significantly related to 18-month OS, and two (GLCM-Energy and GLCM-Entropy) were shown to be significantly related to 6-month EFS. Cox regression suggested that GLCM-Dissimilarity was independently associated with OS, while GLCM-Energy were independently associated with EFS.

Conclusions

The texture features of contrast-enhanced computed tomography image could potentially serve as radiological prognostic biomarkers for SCLC patients.

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References

  1. 1.

    Herbst RS, Heymach JV, Lippman SM (2008) Lung cancer. New Engl J Med 359:1367–1380

  2. 2.

    Fruh M, De Ruysscher D, Popat S et al (2013) Small-cell lung cancer (SCLC): ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 24(Suppl 6):vi99–v105

  3. 3.

    Institute NC Small Cell Lung Cancer Treatment (PDQ®)—Health Professional Version

  4. 4.

    Kalemkerian GP, Akerley W, Bogner P, Borghaei H, Chow LQM, Downey RJ, Gandhi L, Ganti AKP, Govindan R, Grecula JC, Hayman J, Heist RS, Horn L, Jahan T, Koczywas M, Loo BW Jr, Merritt RE, Moran CA, Niell HB, O’Malley J, Patel JD, Ready N, Rudin CM, Williams CC Jr, Gregory K, Hughes M (2013) Small cell lung cancer. J Nat Comprehensive Cancer Network 11:78–98

  5. 5.

    Simon GR, Turrisi A (2007) Management of small cell lung cancer: ACCP evidence-based clinical practice guidelines (2nd edition). Chest 132:324s–339s

  6. 6.

    Chan BA, Coward JI (2013) Chemotherapy advances in small-cell lung cancer. J Thorac Dis 5(Suppl 5):S565–S578

  7. 7.

    van Meerbeeck JP, Fennell DA, De Ruysscher DK (2011) Small-cell lung cancer. Lancet 378:1741–1755

  8. 8.

    Shepherd FA, Crowley J, Van Houtte P et al (2007) The International Association for the Study of Lung Cancer lung cancer staging project: proposals regarding the clinical staging of small cell lung cancer in the forthcoming (seventh) edition of the tumor, node, metastasis classification for lung cancer. J Thorac Oncol 2:1067–1077

  9. 9.

    Auperin A, Arriagada R, Pignon JP et al (1999) Prophylactic cranial irradiation for patients with small-cell lung cancer in complete remission. Prophylactic Cranial Irradiation Overview Collaborative Group New Engl J Med 341:476–484

  10. 10.

    (ACS). ACS (2014) Cancer facts & figures 2014.

  11. 11.

    Schnipper LE, Davidson NE, Wollins DS, Tyne C, Blayney DW, Blum D, Dicker AP, Ganz PA, Hoverman JR, Langdon R, Lyman GH, Meropol NJ, Mulvey T, Newcomer L, Peppercorn J, Polite B, Raghavan D, Rossi G, Saltz L, Schrag D, Smith TJ, Yu PP, Hudis CA, Schilsky RL (2015) American Society of Clinical Oncology statement: a conceptual framework to assess the value of cancer treatment options. J Clin Oncol 33:2563–2577

  12. 12.

    Chute JP, Chen T, Feigal E, Simon R, Johnson BE (1999) Twenty years of phase III trials for patients with extensive-stage small-cell lung cancer: perceptible progress. J Clin Oncol 17:1794–1801

  13. 13.

    Lucia F, Visvikis D, Desseroit MC, Miranda O, Malhaire JP, Robin P, Pradier O, Hatt M, Schick U (2018) Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy. Eur J Nucl Med Molr imaging 45:768–786

  14. 14.

    Lovinfosse P, Polus M, Van Daele D et al (2018) FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer. Eur J Nucl Med Molr imaging 45:365–375

  15. 15.

    Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K (2012) Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol 67:157–164

  16. 16.

    Goh V, Ganeshan B, Nathan P, Juttla JK, Vinayan A, Miles KA (2011) Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology 261:165–171

  17. 17.

    Miles KA, Ganeshan B, Griffiths MR, Young RCD, Chatwin CR (2009) Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology 250:444–452

  18. 18.

    Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V (2013) Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 266:177–184

  19. 19.

    Zhang H, Graham CM, Elci O, Griswold ME, Zhang X, Khan MA, Pitman K, Caudell JJ, Hamilton RD, Ganeshan B, Smith AD (2013) Locally advanced squamous cell carcinoma of the head and neck: CT texture and histogram analysis allow independent prediction of overall survival in patients treated with induction chemotherapy. Radiology 269:801–809

  20. 20.

    Ahn SY, Park CM, Park SJ, Kim HJ, Song C, Lee SM, McAdams HP, Goo JM (2015) Prognostic value of computed tomography texture features in non-small cell lung cancers treated with definitive concomitant chemoradiotherapy. Investig Radiol 50:719–725

  21. 21.

    Win T, Miles KA, Janes SM, Ganeshan B, Shastry M, Endozo R, Meagher M, Shortman RI, Wan S, Kayani I, Ell PJ, Groves AM (2013) Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with non-small cell lung cancer. Clin Cancer Res 19:3591–3599

  22. 22.

    Ganeshan B, Goh V, Mandeville HC, Ng QS, Hoskin PJ, Miles KA (2013) Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 266:326–336

  23. 23.

    NCCN Guidelines-Small Cell Lung Cancer

  24. 24.

    Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, Pellot-Barakat C, Soussan M, Frouin F, Buvat I (2018) LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78:4786–4789

  25. 25.

    Kirienko M, Cozzi L, Rossi A, Voulaz E, Antunovic L, Fogliata A, Chiti A, Sollini M (2018) Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging 45:1649–1660

  26. 26.

    Ishizuka M, Nagata H, Takagi K, Iwasaki Y, Shibuya N, Kubota K (2016) Clinical significance of the C-reactive protein to albumin ratio for survival after surgery for colorectal Cancer. Ann Surg Oncol 23:900–907

  27. 27.

    Youden WJ (1950) Index for rating diagnostic tests. Cancer 3:32–35

  28. 28.

    Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K (2012) Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 22:796–802

  29. 29.

    Ha S, Choi H, Cheon GJ, Kang KW, Chung JK, Kim EE, Lee DS (2014) Autoclustering of non-small cell lung carcinoma subtypes on 18F-FDG PET using texture analysis: a preliminary result. Nucl Med Mol Imaging 48:278–286

  30. 30.

    Lovinfosse P, Janvary ZL, Coucke P, Jodogne S, Bernard C, Hatt M, Visvikis D, Jansen N, Duysinx B, Hustinx R (2016) FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy. Eur J Nucl Med Mol Imaging 43:1453–1460

  31. 31.

    Ravanelli M, Farina D, Morassi M, Roca E, Cavalleri G, Tassi G, Maroldi R (2013) Texture analysis of advanced non-small cell lung cancer (NSCLC) on contrast-enhanced computed tomography: prediction of the response to the first-line chemotherapy. Eur Radiol 23:3450–3455

  32. 32.

    Zhou M, Leung A, Echegaray S, Gentles A, Shrager JB, Jensen KC, Berry GJ, Plevritis SK, Rubin DL, Napel S, Gevaert O (2018) Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology 286:307–315

  33. 33.

    Mack JW, Smith TJ (2012) Reasons why physicians do not have discussions about poor prognosis, why it matters, and what can be improved. J Clin Oncol 30:2715–2717

  34. 34.

    Huskamp HA, Keating NL, Malin JL, Zaslavsky AM, Weeks JC, Earle CC, Teno JM, Virnig BA, Kahn KL, He Y, Ayanian JZ (2009) Discussions with physicians about hospice among patients with metastatic lung cancer. Arch Intern Med 169:954–962

  35. 35.

    Ganeshan B, Miles KA (2013) Quantifying tumour heterogeneity with CT. Cancer Imaging 13:140–149

  36. 36.

    Bezy-Wendling J, Kretowski M, Rolland Y, Le Bidon W (2001) Toward a better understanding of texture in vascular CT scan simulated images. IEEE T Bio-Med Eng 48:120–124

  37. 37.

    Nelson DA, Tan TT, Rabson AB, Anderson D, Degenhardt K, White E (2004) Hypoxia and defective apoptosis drive genomic instability and tumorigenesis. Genes Dev 18:2095–2107

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Correspondence to Xuelei Ma.

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Chen, C., Ou, X., Li, H. et al. Contrast-Enhanced CT Texture Analysis: a New Set of Predictive Factors for Small Cell Lung Cancer. Mol Imaging Biol (2019). https://doi.org/10.1007/s11307-019-01419-1

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Key words

  • CT
  • Texture analysis
  • Small cell lung cancer
  • Survival
  • Biomarker