Advertisement

Abdominal Radiology

, Volume 44, Issue 7, pp 2346–2356 | Cite as

Feasibility of using computed tomography texture analysis parameters as imaging biomarkers for predicting risk grade of gastrointestinal stromal tumors: comparison with visual inspection

  • In Young Choi
  • Suk Keu YeomEmail author
  • Jaehyung Cha
  • Sang Hoon Cha
  • Seung Hwa Lee
  • Hwan Hoon Chung
  • Chang Min Lee
  • Jungwoo Choi
Hollow Organ GI

Abstract

Purpose

To evaluate the feasibility of using computed tomography texture analysis (CTTA) parameters for predicting malignant risk grade and mitosis index of gastrointestinal stromal tumors (GISTs), compared with visual inspection.

Method and materials

CTTA was performed on portal phase CT images of 145 surgically confirmed GISTs (mean size: 42.9 ± 37.5 mm), using TexRAD software. Mean, standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis of CTTA parameters, on spatial scaling factor (SSF), 2–6 were compared by risk grade, mitosis rate, and the presence or absence of necrosis on visual inspection. CTTA parameters were correlated with risk grade. Diagnostic performance was evaluated with receiver operating characteristic curve analysis. Enhancement pattern, necrosis, heterogeneity, calcification, growth pattern, and mucosal ulceration were subjectively evaluated by two observers.

Results

Three to four parameters at different scales were significantly different according to the risk grade, mitosis rate, and the presence or absence of necrosis (p < 0.041). MPP at fine or medium scale (r = − 0.547 to − 393) and kurtosis at coarse scale (r = 0.424–0.454) correlated significantly with risk grade (p < 0.001). HG-GIST was best differentiated from LG-GIST by MPP at SSF 2 (AUC, 0.782), and kurtosis at SSF 4 (AUC, 0.779) (all p < 0.001). CT features predictive of HG-GIST were density lower than or equal to that of the erector spinae muscles on enhanced images (OR 2.1; p = 0.037; AUC, 0.59), necrosis (OR, 6.1; p < 0.001; AUC, 0.70), heterogeneity (OR, 4.3; p < 0.001; AUC, 0.67), and mucosal ulceration (OR, 3.3; p = 0.002; AUC, 0.62).

Conclusion

Using TexRAD, MPP and kurtosis are feasible in predicting risk grade and mitosis index of GISTs. CTTA demonstrated meaningful accuracy in preoperative risk stratification of GISTs.

Keywords

Computed tomography texture analysis Gastrointestinal stromal tumor Mitosis rate Risk stratification 

Notes

Acknowledgements

This research was supported by a Korea University Ansan Hospital Grant (O1801331) and Korea University Grant (K1422331).

Compliance with ethical standards

Conflicts of interest

The scientific guarantor of this publication is Suk Keu Yeom. The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. The authors state that this work has not received any funding.

References

  1. 1.
    Hirota S, Isozaki K, Moriyama Y, Hashimoto K, Nishida T, Ishiguro S, Kawano K, Hanada M, Kurata A, Takeda M, Muhammad Tunio G, Matsuzawa Y, Kanakura Y, Shinomura Y, Kitamura Y (1998) Gain-of-function mutations of c-kit in human gastrointestinal stromal tumors. Science 279 (5350):577-580CrossRefGoogle Scholar
  2. 2.
    Park CH, Kim GH, Lee BE, Song GA, Park DY, Choi KU, Kim DH, Jeon TY (2017) Two staging systems for gastrointestinal stromal tumors in the stomach: which is better? BMC gastroenterology 17 (1):141.  https://doi.org/10.1186/s12876-017-0705-7 CrossRefGoogle Scholar
  3. 3.
    Sepe PS, Brugge WR (2009) A guide for the diagnosis and management of gastrointestinal stromal cell tumors. Nature reviews Gastroenterology & hepatology 6 (6):363-371.  https://doi.org/10.1038/nrgastro.2009.43 CrossRefGoogle Scholar
  4. 4.
    Liu S, Pan X, Liu R, Zheng H, Chen L, Guan W, Wang H, Sun Y, Tang L, Guan Y, Ge Y, He J, Zhou Z (2018) Texture analysis of CT images in predicting malignancy risk of gastrointestinal stromal tumours. Clinical radiology 73 (3):266-274.  https://doi.org/10.1016/j.crad.2017.09.003 CrossRefGoogle Scholar
  5. 5.
    Phongkitkarun S, Phaisanphrukkun C, Jatchavala J, Sirachainan E (2008) Assessment of gastrointestinal stromal tumors with computed tomography following treatment with imatinib mesylate. World journal of gastroenterology 14 (6):892-898CrossRefGoogle Scholar
  6. 6.
    Miettinen M, Sobin LH, Lasota J (2005) Gastrointestinal stromal tumors of the stomach: a clinicopathologic, immunohistochemical, and molecular genetic study of 1765 cases with long-term follow-up. The American journal of surgical pathology 29 (1):52-68CrossRefGoogle Scholar
  7. 7.
    Bertin M, Angriman I, Scarpa M, Mencarelli R, Ranzato R, Ruffolo C, Polese L, Iacobone M, D’Amico DF (2007) Prognosis of gastrointestinal stromal tumors. Hepato-gastroenterology 54 (73):124-128Google Scholar
  8. 8.
    Scarpa M, Bertin M, Ruffolo C, Polese L, D’Amico DF, Angriman I (2008) A systematic review on the clinical diagnosis of gastrointestinal stromal tumors. Journal of surgical oncology 98 (5):384-392.  https://doi.org/10.1002/jso.21120 CrossRefGoogle Scholar
  9. 9.
    Eckardt AJ, Adler A, Gomes EM, Jenssen C, Siebert C, Gottschalk U, Koch M, Rocken C, Rosch T (2012) Endosonographic large-bore biopsy of gastric subepithelial tumors: a prospective multicenter study. European journal of gastroenterology & hepatology 24 (10):1135-1144.  https://doi.org/10.1097/meg.0b013e328356eae2 CrossRefGoogle Scholar
  10. 10.
    Demetri GD, von Mehren M, Antonescu CR, DeMatteo RP, Ganjoo KN, Maki RG, Pisters PW, Raut CP, Riedel RF, Schuetze S, Sundar HM, Trent JC, Wayne JD (2010) NCCN Task Force report: update on the management of patients with gastrointestinal stromal tumors. Journal of the National Comprehensive Cancer Network : JNCCN 8 Suppl 2:S1-41; quiz S42-44Google Scholar
  11. 11.
    Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. Radiographics : a review publication of the Radiological Society of North America, Inc 37 (5):1483-1503.  https://doi.org/10.1148/rg.2017170056
  12. 12.
    Lubner MG, Stabo N, Abel EJ, Del Rio AM, Pickhardt PJ (2016) CT Textural Analysis of Large Primary Renal Cell Carcinomas: Pretreatment Tumor Heterogeneity Correlates With Histologic Findings and Clinical Outcomes. AJR American journal of roentgenology 207 (1):96-105.  https://doi.org/10.2214/ajr.15.15451 CrossRefGoogle Scholar
  13. 13.
    Zhang GM, Sun H, Shi B, Jin ZY, Xue HD (2017) Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma. Abdominal radiology 42 (2):561-568.  https://doi.org/10.1007/s00261-016-0897-2 CrossRefGoogle Scholar
  14. 14.
    Andersen MB, Harders SW, Ganeshan B, Thygesen J, Torp Madsen HH, Rasmussen F (2016) CT texture analysis can help differentiate between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer. Acta radiologica 57 (6):669-676.  https://doi.org/10.1177/0284185115598808 CrossRefGoogle Scholar
  15. 15.
    Skogen K, Schulz A, Dormagen JB, Ganeshan B, Helseth E, Server A (2016) Diagnostic performance of texture analysis on MRI in grading cerebral gliomas. European journal of radiology 85 (4):824-829.  https://doi.org/10.1016/j.ejrad.2016.01.013 CrossRefGoogle Scholar
  16. 16.
    Haider MA, Vosough A, Khalvati F, Kiss A, Ganeshan B, Bjarnason GA (2017) CT texture analysis: a potential tool for prediction of survival in patients with metastatic clear cell carcinoma treated with sunitinib. Cancer imaging : the official publication of the International Cancer Imaging Society 17 (1):4.  https://doi.org/10.1186/s40644-017-0106-8 CrossRefGoogle Scholar
  17. 17.
    Jalil O, Afaq A, Ganeshan B, Patel UB, Boone D, Endozo R, Groves A, Sizer B, Arulampalam T (2017) Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy. Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland 19 (4):349-362.  https://doi.org/10.1111/codi.13496 CrossRefGoogle Scholar
  18. 18.
    Cannella R, Borhani AA, Minervini MI, Tsung A, Furlan A (2018) Evaluation of texture analysis for the differential diagnosis of focal nodular hyperplasia from hepatocellular adenoma on contrast-enhanced CT images. Abdominal radiology.  https://doi.org/10.1007/s00261-018-1788-5
  19. 19.
    Digumarthy SR, Padole AM, Lo Gullo R, Singh R, Shepard JO, Kalra MK (2018) CT texture analysis of histologically proven benign and malignant lung lesions. Medicine 97 (26):e11172.  https://doi.org/10.1097/md.0000000000011172 CrossRefGoogle Scholar
  20. 20.
    Frood R, Palkhi E, Barnfield M, Prestwich R, Vaidyanathan S, Scarsbrook A (2018) Can MR textural analysis improve the prediction of extracapsular nodal spread in patients with oral cavity cancer? European radiology.  https://doi.org/10.1007/s00330-018-5524-x
  21. 21.
    Sandrasegaran K, Lin Y, Asare-Sawiri M, Taiyini T, Tann M (2018) CT texture analysis of pancreatic cancer. European radiology.  https://doi.org/10.1007/s00330-018-5662-1
  22. 22.
    Ytre-Hauge S, Dybvik JA, Lundervold A, Salvesen OO, Krakstad C, Fasmer KE, Werner HM, Ganeshan B, Hoivik E, Bjorge L, Trovik J, Haldorsen IS (2018) Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer. Journal of magnetic resonance imaging : JMRI.  https://doi.org/10.1002/jmri.26184
  23. 23.
    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 (1):326-336.  https://doi.org/10.1148/radiol.12112428 CrossRefGoogle Scholar
  24. 24.
    Cai PQ, Lv XF, Tian L, Luo ZP, Mitteer RA, Jr., Fan Y, Wu YP (2015) CT Characterization of Duodenal Gastrointestinal Stromal Tumors. AJR American journal of roentgenology 204 (5):988-993.  https://doi.org/10.2214/ajr.14.12870 CrossRefGoogle Scholar
  25. 25.
    Guo C, Zhuge X, Wang Z, Wang Q, Sun K, Feng Z, Chen X (2018) Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade. Abdominal radiology.  https://doi.org/10.1007/s00261-018-1763-1
  26. 26.
    Maldonado FJ, Sheedy SP, Iyer VR, Hansel SL, Bruining DH, McCollough CH, Harmsen WS, Barlow JM, Fletcher JG (2018) Reproducible imaging features of biologically aggressive gastrointestinal stromal tumors of the small bowel. Abdominal radiology 43 (7):1567-1574.  https://doi.org/10.1007/s00261-017-1370-6 CrossRefGoogle Scholar
  27. 27.
    Tateishi U, Hasegawa T, Satake M, Moriyama N (2003) Gastrointestinal stromal tumor. Correlation of computed tomography findings with tumor grade and mortality. Journal of computer assisted tomography 27 (5):792-798Google Scholar
  28. 28.
    Pinaikul S, Woodtichartpreecha P, Kanngurn S, Leelakiatpaiboon S (2014) 1189 Gastrointestinal stromal tumor (GIST): computed tomographic features and correlation of CT findings with histologic grade. Journal of the Medical Association of Thailand = Chotmaihet thangphaet 97 (11):1189-1198Google Scholar
  29. 29.
    Iannarelli A, Sacconi B, Tomei F, Anile M, Longo F, Bezzi M, Napoli A, Saba L, Anzidei M, D’Ovidio G, Scipione R, Catalano C (2018) Analysis of CT features and quantitative texture analysis in patients with thymic tumors: correlation with grading and staging. La Radiologia medica 123 (5):345-350.  https://doi.org/10.1007/s11547-017-0845-4 CrossRefGoogle Scholar
  30. 30.
    Schieda N, Thornhill RE, Al-Subhi M, McInnes MD, Shabana WM, van der Pol CB, Flood TA (2015) Diagnosis of Sarcomatoid Renal Cell Carcinoma With CT: Evaluation by Qualitative Imaging Features and Texture Analysis. AJR American journal of roentgenology 204 (5):1013-1023.  https://doi.org/10.2214/ajr.14.13279 CrossRefGoogle Scholar
  31. 31.
    Novitsky YW, Kercher KW, Sing RF, Heniford BT (2006) Long-term outcomes of laparoscopic resection of gastric gastrointestinal stromal tumors. Annals of surgery 243 (6):738-745; discussion 745-737.  https://doi.org/10.1097/01.sla.0000219739.11758.27.
  32. 32.
    Matsuhashi N, Osada S, Yamaguchi K, Okumura N, Tanaka Y, Imai H, Sasaki Y, Nonaka K, Takahashi T, Futamura M, Yoshida K (2013) Long-term outcomes of treatment of gastric gastrointestinal stromal tumor by laparoscopic surgery: review of the literature and our experience. Hepato-gastroenterology 60 (128):2011-2015Google Scholar
  33. 33.
    Bischof DA, Kim Y, Dodson R, Carolina Jimenez M, Behman R, Cocieru A, Blazer DG, 3rd, Fisher SB, Squires MH, 3rd, Kooby DA, Maithel SK, Groeschl RT, Clark Gamblin T, Bauer TW, Karanicolas PJ, Law C, Quereshy FA, Pawlik TM (2014) Open versus minimally invasive resection of gastric GIST: a multi-institutional analysis of short- and long-term outcomes. Annals of surgical oncology 21 (9):2941-2948.  https://doi.org/10.1245/s10434-014-3733-3 CrossRefGoogle Scholar
  34. 34.
    Otani Y, Furukawa T, Yoshida M, Saikawa Y, Wada N, Ueda M, Kubota T, Mukai M, Kameyama K, Sugino Y, Kumai K, Kitajima M (2006) Operative indications for relatively small (2-5 cm) gastrointestinal stromal tumor of the stomach based on analysis of 60 operated cases. Surgery 139 (4):484-492.  https://doi.org/10.1016/j.surg.2005.08.011 CrossRefGoogle Scholar
  35. 35.
    Lopez RL, del Muro XG (2012) Management of localized gastrointestinal stromal tumors and adjuvant therapy with imatinib. Anti-cancer drugs 23 Suppl:S3-6.  https://doi.org/10.1097/cad.0b013e3283559fab CrossRefGoogle Scholar
  36. 36.
    Miles KA, Ganeshan B, Griffiths MR, Young RC, Chatwin CR (2009) Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology 250 (2):444-452.  https://doi.org/10.1148/radiol.2502071879 CrossRefGoogle Scholar
  37. 37.
    Bashir U, Siddique MM, McLean E, Goh V, Cook GJ (2016) Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges. AJR American journal of roentgenology 207 (3):534-543.  https://doi.org/10.2214/ajr.15.15864 CrossRefGoogle Scholar
  38. 38.
    Tsujikawa T, Rahman T, Yamamoto M, Yamada S, Tsuyoshi H, Kiyono Y, Kimura H, Yoshida Y, Okazawa H (2017) (18)F-FDG PET radiomics approaches: comparing and clustering features in cervical cancer. Annals of nuclear medicine 31 (9):678-685.  https://doi.org/10.1007/s12149-017-1199-7 CrossRefGoogle Scholar
  39. 39.
    Khene ZE, Bensalah K, Largent A, Shariat S, Verhoest G, Peyronnet B, Acosta O, DeCrevoisier R, Mathieu R (2018) Role of quantitative computed tomography texture analysis in the prediction of adherent perinephric fat. World journal of urology 36 (10):1635-1642.  https://doi.org/10.1007/s00345-018-2292-9 CrossRefGoogle Scholar
  40. 40.
    Tsujikawa T, Yamamoto M, Shono K, Yamada S, Tsuyoshi H, Kiyono Y, Kimura H, Okazawa H, Yoshida Y (2017) Assessment of intratumor heterogeneity in mesenchymal uterine tumor by an (18)F-FDG PET/CT texture analysis. Annals of nuclear medicine 31 (10):752-757.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Radiology, Korea University Ansan HospitalKorea University College of MedicineAnsanRepublic of Korea
  2. 2.Department of Biostatistics, Korea University Ansan HospitalKorea University College of MedicineAnsanRepublic of Korea
  3. 3.Department of Surgery, Korea University Ansan HospitalKorea University College of MedicineAnsanRepublic of Korea
  4. 4.Department of Pathology, Korea University Ansan HospitalKorea University College of MedicineAnsanRepublic of Korea

Personalised recommendations