Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors

  • 8 Accesses

Abstract

Purpose

The pathological risk degree of gastrointestinal stromal tumors (GISTs) has become an issue of great concern. Computed tomography (CT) is beneficial for showing adjacent tissues in detail and determining metastasis or recurrence of GISTs, but its function is still limited. Radiomics has recently shown a great potential in aiding clinical decision-making. The purpose of our study is to develop and validate CT-based radiomics models for GIST risk stratification.

Methods

Three hundred and sixty-six patients clinically suspected of primary GISTs from January 2013 to February 2018 were retrospectively enrolled, among which data from 140 patients were eventually analyzed after exclusion. Data from patient CT images were partitioned based on the National Institutes of Health Consensus Classification, including tumor segmentation, radiomics feature extraction and selection. A radiomics model was then proposed and validated.

Results

The radiomics signature demonstrated discriminative performance for advanced and nonadvanced GISTs with an area under the curve (AUC) of 0.935 [95% confidence interval (CI) 0.870–1.000] and an accuracy of 90.2% for validation cohort. The radiomics signature demonstrated favorable performance for the risk stratification of GISTs with an AUC of 0.809 (95% CI 0.777–0.841) and an accuracy of 67.5% for the validation cohort. Radiomics analysis could capture features of the four risk categories of GISTs. Meanwhile, this CT-based radiomics signature showed good diagnostic accuracy to distinguish between nonadvanced and advanced GISTs, as well as the four risk stratifications of GISTs.

Conclusion

Our findings highlight the potential of a quantitative radiomics analysis as a complementary tool to achieve an accurate diagnosis for GISTs.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Winer JH, Raut CP (2011) Management of recurrent gastrointestinal stromal tumors. J Surg Oncol 104:915–920

  2. 2.

    DeMatteo RP, Lewis JJ, Leung D, Mudan SS, Woodruff JM, Brennan MF (2000) Two hundred gastrointestinal stromal tumors: recurrence patterns and prognostic factors for survival. Ann Surg 231:51–58

  3. 3.

    Liegl-Atzwanger BFJ, Fletcher CD (2010) Gastrointestinal stromal tumors. Virchows Arch 456:111–127

  4. 4.

    Kindblem LGRH, Aldenborg F (1998) Gastrointestinal pacemaker cell tumor (GIPACT): gastrointestinal stromal tumors show phenotypic characteristics of the intestinal cells of Cajal. AJR Am J Roentgenol 152:1259–1269

  5. 5.

    Sircar K, Hewlett BR, Huizinga JD, Chorneyko K, Berezin I, Riddell RH (1999) Interstitial cells of Cajal as precursors of gastrointestinal stromal tumors. Am J Surg Pathol 23:377–389

  6. 6.

    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 279:577–580

  7. 7.

    Heinrich MC, Corless CL, Duensing A, McGreevey L, Chen CJ, Joseph N, Singer S, Griffith DJ, Haley A, Town A, Demetri GD, Fletcher CD, Fletcher JA (2003) PDGFRA activating mutations in gastrointestinal stromal tumors. Science 299:708–710

  8. 8.

    Joensuu H (2008) Risk stratification of patients diagnosed with gastrointestinal stromal tumor. Hum Pathol 39:1411–1419

  9. 9.

    Tirumani SHJJ, Krajewski KM (2013) Imatinib and beyond in gastrointestinal stromal tumors: a radiologist’s perspective. AJR Am J Roentgenol 201:801–810

  10. 10.

    Kim HC, Lee JM, Kim KW, Park SH, Kim SH, Lee JY, Han JK, Choi BI (2004) Gastrointestinal stromal tumors of the stomach: CT findings and prediction of malignancy. AJR Am J Roentgenol 183:893–898

  11. 11.

    Wang JK (2017) Predictive value and modeling analysis of MSCT signs in gastrointestinal stromal tumors (GISTs) to pathological risk degree. Eur Rev Med Pharmacol Sci 21:999–1005

  12. 12.

    Burkill GJ, Badran M, Al-Muderis O, Meirion Thomas J, Judson IR, Fisher C, Moskovic EC (2003) Malignant gastrointestinal stromal tumor: distribution, imaging features, and pattern of metastatic spread. Radiology 226:527–532

  13. 13.

    Sandrasegaran K, Rajesh A, Rushing DA, Rydberg J, Akisik FM, Henley JD (2005) Gastrointestinal stromal tumors: CT and MRI findings. Eur Radiol 15:1407–1414

  14. 14.

    Da Ronch T, Modesto A, Bazzocchi M (2006) Gastrointestinal stromal tumour: spiral computed tomography features and pathologic correlation. Radiol Med 111:661–673

  15. 15.

    Bartolotta TV, Taibbi A, Galia M, Cannella I, Lo Re G, Sparacia G, Midiri M, Lagalla R (2006) Gastrointestinal stromal tumour: 40-row multislice computed tomography findings. Radiol Med 111:651–660

  16. 16.

    Vernuccio F, Taibbi A, Picone D, Grutta LA, Midiri M, Lagalla R, Lo Re G, Bartolotta TV (2016) Imaging of gastrointestinal stromal tumors: from diagnosis to evaluation of therapeutic response. Anticancer Res 36:2639–2648

  17. 17.

    Zhou C, Duan X, Zhang X, Hu H, Wang D, Shen J (2016) Predictive features of CT for risk stratifications in patients with primary gastrointestinal stromal tumour. Eur Radiol 26:3086–3093

  18. 18.

    Gillies RJ, Kinahan P, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

  19. 19.

    Coroller TP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RT, Hermann G, Lambin P, Haibe-Kains B, Mak RH, Aerts HJ (2015) CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 114:345–350

  20. 20.

    Liang C, Huang Y, He L, Chen X, Ma Z, Dong D, Tian J, Liang C, Liu Z (2016) The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I–II and stage III–IV colorectal cancer. Oncotarget 7:31401–31412

  21. 21.

    Lu WCW (2016) Positron emission tomography/computerized tomography for tumor response assessment—a review of clinical practices and radiomics studies. Transl Cancer Res 5:364–370

  22. 22.

    Zhou Y, He L, Huang Y, Chen S, Wu P, Ye W, Liu Z, Liang C (2017) CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma. Abdom Radiol (NY) 42:1695–1704

  23. 23.

    AertsHJ VE, Leijenaar RT (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:1–8

  24. 24.

    Feng C, Lu F, Shen Y, Li A, Yu H, Tang H, Li Z, Hu D (2018) Tumor heterogeneity in gastrointestinal stromal tumors of the small bowel: volumetric CT texture analysis as a potential biomarker for risk stratification. Cancer Imaging 18:46

  25. 25.

    Xu F, Ma X, Wang Y, Tian Y, Tang W, Wang M, Wei R, Zhao X (2018) CT texture analysis can be a potential tool to differentiate gastrointestinal stromal tumors without KIT exon 11 mutation. Eur J Radiol 107:90–97

  26. 26.

    Chalkidou A, O’Doherty MJ, Marsden PK (2015) False discovery rates in PET and CT studies with texture features: a systematic review. PLoS ONE 10:e0124165

  27. 27.

    Cui ZXZ, Su M (2016) Disrupted white matter connectivity underlying developmental dyslexia: a machine learning approach. Hum Brain Mapp 37:1443–1458

  28. 28.

    Tirumani SH, Baheti AD, Tirumani H, O’Neill A, Jagannathan JP (2017) Update on gastrointestinal stromal tumors for radiologists. Korean J Radiol 18:84–93

  29. 29.

    Joensuu HHP, Corless CL (2013) Gastrointestinal stromal tumour. Lancet 382:973–983

  30. 30.

    Kang TWKS, Jang KM (2015) Gastrointestinal stromal tumours: correlation of modified NIH risk stratification with diffusion-weighted MR imaging as an imaging biomarker. Eur J Radiol 84:33–40

  31. 31.

    O’Neill ACSA, Kurra V (2016) Assessment of metastatic risk of gastric GIST based on treatment-naïve CT features. Eur J Surg Oncol 42:1222–1228

  32. 32.

    Ba-SsalamahA MD, Schernthaner R (2013) Texture-based classification of different gastric tumors at contrast-enhanced CT. Eur J Radiol 82:537–543

  33. 33.

    Ma Z, Fang M, Huang Y, He L, Chen X, Liang C, Huang X, Cheng Z, Dong D, Liang C, Xie J, Tian J, Liu Z (2017) CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma. Eur J Radiol 91:142–147

  34. 34.

    Ng FKR, Ganeshan B (2013) Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis. Eur J Radiol 82:342–348

  35. 35.

    Blay JYL (2016) Adjuvant imatinib treatment in gastrointestinal stromal tumor: which risk stratification criteria and for how long? A case report. Anti-Cancer Drug 27:71–75

  36. 36.

    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. J Natl Compr Canc Netw 8(Suppl 2):S1–S41 (quiz S42–S44)

  37. 37.

    Rutkowski P, Przybyl J, Zdzienicki M (2013) Extended adjuvant therapy with imatinib in patients with gastrointestinal stromal tumors: recommendations for patient selection, risk assessment, and molecular response monitoring. Mol Diagn Ther 17:9–19

  38. 38.

    Jones RL (2014) Practical aspects of risk assessment in gastrointestinal stromal tumors. J Gastrointest Cancer 45:262–267

  39. 39.

    Chen T, Ning Z, Xu L, Feng X, Han S, Roth HR, Xiong W, Zhao X, Hu Y, Liu H, Yu J, Zhang Y, Li Y, Xu Y, Mori K, Li G (2019) Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively. Eur Radiol 29:1074–1082

  40. 40.

    Joensuu H, Vehtari A, Riihimaki J, Nishida T, Steigen SE, Brabec P, Plank L, Nilsson B, Cirilli C, Braconi C, Bordoni A, Magnusson MK, Linke Z, Sufliarsky J, Federico M, Jonasson JG, Dei Tos AP, Rutkowski P (2012) Risk of recurrence of gastrointestinal stromal tumour after surgery: an analysis of pooled population-based cohorts. Lancet Oncol 13:265–274

  41. 41.

    Tameem HZ, Selva LE, Sinha US (2007) Texture measure from low resolution MR images to determine trabecular bone integrity in osteoporosis. Conf Proc IEEE Eng Med Biol Soc 2007:2027–2030

  42. 42.

    Joensuu H (2013) Gastrointestinal stromal tumors: risk assessment and adjuvant therapy. Hematol Oncol Clin North Am 27:889–904

Download references

Funding

None.

Author information

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by LK, GL, XZ, JR, ZS, JL and SY. The first draft of the manuscript was written by LZ, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Correspondence to Lijing Zhang.

Ethics declarations

Conflict of interest

All the authors declare that they have no conflict of interest.

Ethical standards

This article does not contain any studies with human participants or animals performed by any of the authors.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (Cangzhou Central Hospital + ER3N) and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

For this type of study, formal consent is not required.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, L., Kang, L., Li, G. et al. Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors. Radiol med (2020). https://doi.org/10.1007/s11547-020-01138-6

Download citation

Keywords

  • Computed tomography
  • Gastrointestinal stromal tumors
  • Radiomics
  • Diagnosis