The role of radiomics in diagnosing gastrointestinal stromal tumors: a review

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Abstract

Gastrointestinal stromal tumors are the most common mesenchymal neoplasms of the gastrointestinal tract originating from the interstitial cells of Cajal, accounting for approximately 80% of all primary gastric tumors. Despite their widespread use, traditional diagnostic methods for gastrointestinal stromal tumors, such as computed tomography, endoscopic examination, endoscopic ultrasound, and fine-needle aspiration biopsy, have several limitations, including diagnostic uncertainty and limited capabilities of biopsy.

Radiomics, which involves analyzing texture features in medical images, is considered an innovative approach, with the potential to enhance diagnostic accuracy in gastrointestinal stromal tumors detection. This method allows for the interpretation of tissue changes through the mathematical processing of images, revealing information beyond the human eye’s ability to detect, which can be beneficial for the early detection of tumors.

This article assesses the advantages and disadvantages of current methods for diagnosing gastrointestinal stromal tumors and the potential of radiomics to improve diagnostic outcomes. The review allows to determine the best applications and promising directions for future research in this crucial field.

About the authors

Elina A. Martirosyan

A.V. Vishnevsky National Medical Research Center of Surgery; S.S. Yudin City Clinical Hospital

Author for correspondence.
Email: robatik2009@mail.ru
ORCID iD: 0000-0002-1854-9638
SPIN-code: 8006-8917
Russian Federation, Moscow; Moscow

Grigory G. Karmazanovsky

A.V. Vishnevsky National Medical Research Center of Surgery; The Russian National Research Medical University named N.I. Pirogov

Email: karmazanovsky@ixv.ru
ORCID iD: 0000-0002-9357-0998
SPIN-code: 5964-2369

MD, Dr. Sci. (Medicine), Professor, academician of the Russian Academy of Sciences

Russian Federation, Moscow; Moscow

Evgeny V. Kondratyev

A.V. Vishnevsky National Medical Research Center of Surgery

Email: kondratev@ixv.ru
ORCID iD: 0000-0001-7070-3391
SPIN-code: 2702-6526

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Elena A. Sokolova

A.V. Vishnevsky National Medical Research Center of Surgery

Email: elena83.sokolova@yandex.ru
ORCID iD: 0000-0002-5667-7833
SPIN-code: 9197-6568
Russian Federation, Moscow

Valentin A. Nechaev

S.S. Yudin City Clinical Hospital

Email: nechaevva1@zdrav.mos.ru
ORCID iD: 0000-0002-6716-5593
SPIN-code: 2527-0130

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Evgeniya S. Kuzmina

S.S. Yudin City Clinical Hospital

Email: kuz011@mail.ru
ORCID iD: 0009-0007-2856-5176
SPIN-code: 9668-5733
Russian Federation, Moscow

Vsevolod N. Galkin

S.S. Yudin City Clinical Hospital

Email: galkinvn2@zdrav.mos.ru
ORCID iD: 0000-0002-6619-6179
SPIN-code: 3148-4843

MD, Dr. Sci. (Medicine), Professor

Russian Federation, Moscow

Andrey V. Glotov

A.V. Vishnevsky National Medical Research Center of Surgery

Email: andrew.glotov@mail.ru
ORCID iD: 0000-0002-6904-9318
SPIN-code: 4947-4382
Russian Federation, Moscow

References

  1. Miettinen M, Lasota J. Gastrointestinal stromal tumors: pathology and prognosis at different sites. Seminars in Diagnostic Pathology. 2006;23(2):70–83. doi: 10.1053/j.semdp.2006.09.001
  2. Søreide K, Sandvik OM, Søreide JA, et al. Global epidemiology of gastrointestinal stromal tumours (GIST): a systematic review of population-based cohort studies. Cancer Epidemiology. 2016;40:39–46. doi: 10.1016/j.canep.2015.10.031
  3. Corless CL, Barnett CM, Heinrich MC. Gastrointestinal stromal tumours: origin and molecular oncology. Nature Reviews Cancer. 2011;11(12):865–878. doi: 10.1038/nrc3143
  4. Menge F, Jakob J, Kasper B, et al. Clinical presentation of gastrointestinal stromal tumors. Visceral Medicine. 2018;34(5):335–340. doi: 10.1159/000494303
  5. Korzheva IYu, Chernekhovskaya NE, Volova AV, et al. Gastrointestinal stromal tumors — treatment and diagnostic algorithm. Experimental and Clinical Gastroenterology. 2023;(5):108–113. doi: 10.31146/1682-8658-ecg-213-5-108-113 EDN: KEHFAO
  6. Fletcher CDM, Berman JJ, Corless C, et al. Diagnosis of gastrointestinal stromal tumors: a consensus approach. Human Pathology. 2002;33(5):459–465. doi: 10.1053/hupa.2002.123545
  7. Joensuu H. Risk stratification of patients diagnosed with gastrointestinal stromal tumor. Human Pathology. 2008;39(10):1411–1419. doi: 10.1016/j.humpath.2008.06.025
  8. Hong X, Choi H, Loyer EM, et al. Gastrointestinal stromal tumor: role of CT in diagnosis and in response evaluation and surveillance after treatment with Imatinib. RadioGraphics. 2006;26(2):481–495. doi: 10.1148/rg.262055097
  9. Inoue A, Ota S, Yamasaki M, et al. Gastrointestinal stromal tumors: a comprehensive radiological review. Japanese Journal of Radiology. 2022;40(11):1105–1120. doi: 10.1007/s11604-022-01305-x EDN: DJOHPD
  10. Dimitrakopoulou-Strauss A, Ronellenfitsch U, Cheng C, et al. Imaging therapy response of gastrointestinal stromal tumors (GIST) with FDG PET, CT and MRI: a systematic review. Clinical and Translational Imaging. 2017;5(3):183–197. doi: 10.1007/s40336-017-0229-8 EDN: YEZTPW
  11. Bano S, Puri SK, Upreti L, et al. Gastrointestinal stromal tumors (GISTS): an imaging perspective. Japanese Journal of Radiology. 2011;30(2):105–115. doi: 10.1007/s11604-011-0020-0 EDN: XAZRKK
  12. Horton KM, Juluru K, Montogomery E, Fishman EK. Computed tomography imaging of gastrointestinal stromal tumors with pathology correlation. Journal of Computer Assisted Tomography. 2004;28(6):811–817. doi: 10.1097/00004728-200411000-00014
  13. Martirosyan EA, Karmazanovsky GG, Sokolova EA, et al. Submucosal gastric lesions: a CECT-based tool for differential diagnosis between gastrointestinal stromal tumor and leiomyoma. Medical Visualization. 2020;24(4):27–41. doi: 10.24835/1607-0763-2020-4-27-41 EDN: NDYYRO
  14. Choi YR, Kim SH, Kim S-A, et al. Differentiation of large (≥5 cm) gastrointestinal stromal tumors from benign subepithelial tumors in the stomach: radiologists’ performance using CT. European Journal of Radiology. 2014;83(2):250–260. doi: 10.1016/j.ejrad.2013.10.028
  15. Xu JX, Ding QL, Lu YF, et al. A scoring model for radiologic diagnosis of gastric leiomyomas (GLMs) with contrast-enhanced computed tomography (CE-CT): differential diagnosis from gastrointestinal stromal tumors (GISTs). European Journal of Radiology. 2021;134:109395. doi: 10.1016/j.ejrad.2020.109395 EDN: UNDSRT
  16. Yan M, Liu Y, You H, et al. Differentiation of small gastrointestinal stromal tumor and gastric leiomyoma with contrast-enhanced CT. Journal of Healthcare Engineering. 2023;2023:1–6. doi: 10.1155/2023/6423617 EDN: YWPQDU
  17. Chen Z, Yang J, Sun J, Wang P. Gastric gastrointestinal stromal tumours (2–5 cm): correlation of CT features with malignancy and differential diagnosis. European Journal of Radiology. 2020;123:108783. doi: 10.1016/j.ejrad.2019.108783 EDN: PEEWLT
  18. Iannicelli E, Carbonetti F, Federici GF, et al. Evaluation of the relationships between computed tomography features, pathological findings, and prognostic risk assessment in gastrointestinal stromal tumors. Journal of Computer Assisted Tomography. 2017;41(2):271–278. doi: 10.1097/rct.0000000000000499
  19. Joensuu H, Fletcher C, Dimitrijevic S, et al. Management of malignant gastrointestinal stromal tumours. The Lancet Oncology. 2002;3(11):655–664. doi: 10.1016/s1470-2045(02)00899-9
  20. Hunt GC, Smith PP, Faigel DO. Yield of tissue sampling for submucosal lesions evaluated by EUS. Gastrointestinal Endoscopy. 2003;57(1):68–72. doi: 10.1067/mge.2003.34
  21. Park JE, Kim D, Kim HS, et al. Quality of science and reporting of radiomics in oncologic studies: Room for improvement according to radiomics quality score and TRIPOD statement. European Radiology. 2020;30(1):523–536. doi: 10.1007/s00330-019-06360-z EDN: RYQMNX
  22. Aerts HJWL, Velazquez ER, Leijenaar RT, et al. Corrigendum: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications. 2014;5:4644. doi: 10.1038/ncomms5644
  23. Scapicchio C, Gabelloni M, Barucci A, et al. A deep look into radiomics. La radiologia medica. 2021;126(10):1296–1311. doi: 10.1007/s11547-021-01389-x EDN: CFTFXK
  24. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–577. doi: 10.1148/radiol.2015151169
  25. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology. 2017;14(12):749–762. doi: 10.1038/nrclinonc.2017.141
  26. Yip SS, Aerts HJ. Applications and limitations of radiomics. Physics in Medicine and Biology. 2016;61(13):R150–R166. doi: 10.1088/0031-9155/61/13/R150
  27. Ganeshan B, Abaleke S, Young RC, et al. 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. 2010;10(1):137–143. doi: 10.1102/1470-7330.2010.0021
  28. Bachet JB, Hostein I, Le Cesne A, et al. Prognosis and predictive value of KIT exon 11 deletion in GISTs. British Journal of Cancer. 2009;101(1):7–11. doi: 10.1038/sj.bjc.6605117 EDN: NXUXXL
  29. Wang M, Xu J, Zhao W, et al. Prognostic value of mutational characteristics in gastrointestinal stromal tumors: a single-center experience in 275 cases. Medical Oncology. 2013;31(1):819. doi: 10.1007/s12032-013-0819-x EDN: AAFFTP
  30. Liu X, Yin Y, Wang X, et al. Gastrointestinal stromal tumors: associations between contrast-enhanced CT images and KIT exon 11 gene mutation. Annals of Translational Medicine. 2021;9(19):1496–1496. doi: 10.21037/atm-21-3811 EDN: XKKHIX
  31. Xu F, Ma X, Wang Y, et al. CT texture analysis can be a potential tool to differentiate gastrointestinal stromal tumors without KIT exon 11 mutation. European Journal of Radiology. 2018;107:90–97. doi: 10.1016/j.ejrad.2018.07.025
  32. Guo C, Zhou H, Chen X, Feng Z. Computed tomography texture-based models for predicting KIT exon 11 mutation of gastrointestinal stromal tumors. Heliyon. 2023;9(10):e20983. doi: 10.1016/j.heliyon.2023.e20983 EDN: OMPRCQ
  33. Zhang QW, Zhang RY, Yan ZB, et al. Personalized radiomics signature to screen for KIT-11 mutation genotypes among patients with gastrointestinal stromal tumors: a retrospective multicenter study. Journal of Translational Medicine. 2023;21(1):726. doi: 10.1186/s12967-023-04520-w EDN: MZEKJH
  34. Künstlinger H, Huss S, Merkelbach-Bruse S, et al. Gastrointestinal stromal tumors with KIT exon 9 mutations. American Journal of Surgical Pathology. 2013;37(11):1648–1659. doi: 10.1097/pas.0b013e3182986b88
  35. Gastrointestinal Stromal Tumor Meta-Analysis Group (MetaGIST). Comparison of two doses of Imatinib for the treatment of unresectable or metastatic gastrointestinal stromal tumors: a meta-analysis of 1 640 patients. Journal of Clinical Oncology. 2010;28(7):1247–1253. doi: 10.1200/JCO.2009.24.2099
  36. Wei Y, Lu Z, Ren Y. Predictive value of a radiomics nomogram model based on contrast-enhanced computed tomography for KIT exon 9 gene mutation in gastrointestinal stromal tumors. Technology in Cancer Research & Treatment. 2023;22:153303382311812. doi: 10.1177/15330338231181260 EDN: XTXBRZ
  37. Serrano C, Álvarez R, Carrasco JA, et al. SEOM-GEIS clinical guideline for gastrointestinal stromal tumors (2022). Clinical and Translational Oncology. 2023;25(9):2707–2717. doi: 10.1007/s12094-023-03177-7 EDN: AKQBYR
  38. Jakob J, Salameh R, Wichmann D, et al. Needle tract seeding and abdominal recurrence following pre-treatment biopsy of gastrointestinal stromal tumors (GIST): results of a systematic review. BMC Surgery. 2022;22(1):202. doi: 10.1186/s12893-022-01648-2 EDN: ACKOXX
  39. Zhang L, Kang L, Li G, et al. Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors. La radiologia medica. 2020;125(5):465–473. doi: 10.1007/s11547-020-01138-6 EDN: QGFJNL
  40. Wang C, Li H, Jiaerken Y, et al. Building CT radiomics-based models for preoperatively predicting malignant potential and mitotic count of gastrointestinal stromal tumors. Translational Oncology. 2019;12(9):1229–1236. doi: 10.1016/j.tranon.2019.06.005
  41. Chen T, Ning Z, Xu L, et al. Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively. European Radiology. 2019;29(3):1074–1082. doi: 10.1007/s00330-018-5629-2 EDN: RGIYYW
  42. Chu H, Pang P, He J, et al. Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors. Scientific Reports. 2021;11(1):12009. doi: 10.1038/s41598-021-91508-5 EDN: UBTXKP
  43. Zhou C, Duan X, Zhang X, et al. Predictive features of CT for risk stratifications in patients with primary gastrointestinal stromal tumour. European Radiology. 2015;26(9):3086–3093. doi: 10.1007/s00330-015-4172-7 EDN: YZBWHO
  44. O’Neill AC, Shinagare AB, Kurra V, et al. Assessment of metastatic risk of gastric gist based on treatment-naïve CT features. European Journal of Surgical Oncology (EJSO). 2016;42(8):1222–1228. doi: 10.1016/j.ejso.2016.03.032
  45. Li H, Ren G, Cai R, et al. A correlation research of ki67 index, CT features, and risk stratification in gastrointestinal stromal tumor. Cancer Medicine. 2018;7(9):4467–4474. doi: 10.1002/cam4.1737
  46. Yang L, Ma CF, Li Y, et al. Application of radiomics in predicting the preoperative risk stratification of gastric stromal tumors. Diagnostic and Interventional Radiology. 2022;28(6):532–539. doi: 10.5152/dir.2022.21033 EDN: DOWKAB
  47. Wang Y, Wang Y, Ren J, et al. Malignancy risk of gastrointestinal stromal tumors evaluated with noninvasive radiomics: a multi-center study. Frontiers in Oncology. 2022;12:966743. doi: 10.3389/fonc.2022.966743 EDN: DICNWT
  48. Lin JX, Wang FH, Wang ZK, et al. Prediction of the mitotic index and preoperative risk stratification of gastrointestinal stromal tumors with CT radiomic features. La radiologia medica. 2023;128(6):644–654. doi: 10.1007/s11547-023-01637-2 EDN: PBJVAL
  49. Wang P, Yan J, Qiu H, et al. A radiomics-clinical combined nomogram-based on non-enhanced CT for discriminating the risk stratification in GISTS. Journal of Cancer Research and Clinical Oncology. 2023;149(14):12993–13003. doi: 10.1007/s00432-023-05170-7 EDN: SCPQMA
  50. Fu J, Fang M, Dong D, et al. Heterogeneity of metastatic gastrointestinal stromal tumor on texture analysis: DWI texture as potential biomarker of overall survival. European Journal of Radiology. 2020;125:108825. doi: 10.1016/j.ejrad.2020.108825 EDN: QLLUWS
  51. Yang L, Zheng T, Dong Y, et al. MRI texture-based models for predicting mitotic index and risk classification of gastrointestinal stromal tumors. Journal of Magnetic Resonance Imaging. 2021;53(4):1054–1065. doi: 10.1002/jmri.27390 EDN: CHGAIP
  52. Mao H, Zhang B, Zou M, et al. MRI-based Radiomics models for predicting risk classification of gastrointestinal stromal tumors. Frontiers in Oncology. 2021;11:631927. doi: 10.3389/fonc.2021.631927 EDN: DHDMQA
  53. Yang L, Du D, Zheng T, et al. Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI. Frontiers in Oncology. 2022;12:948557. doi: 10.3389/fonc.2022.948557 EDN: NGMQKM
  54. Zhuo M, Guo J, Tang Y, et al. Ultrasound radiomics model-based nomogram for predicting the risk stratification of gastrointestinal stromal tumors. Frontiers in Oncology. 2022;12:905036. doi: 10.3389/fonc.2022.905036 EDN: TZAWCU
  55. Jia X, Wan L, Chen X, et al. Risk stratification for 1- to 2-cm gastric gastrointestinal stromal tumors: visual assessment of CT and EUS high-risk features versus CT radiomics analysis. European Radiology. 2022;33(4):2768–2778. doi: 10.1007/s00330-022-09228-x EDN: TJNIGC
  56. Seidal T., Edvardsson H. Expression of c-KIT (CD117) and KI67 provides information about the possible cell of origin and clinical course of gastrointestinal stromal tumours. Histopathology. 1999;34(5):416–424. doi: 10.1046/j.1365-2559.1999.00643.x
  57. Li J, Wang AR, Chen XD, et al. Ki67 for evaluating the prognosis of gastrointestinal stromal tumors: a systematic review and meta-analysis. Oncology Letters. 2022;23(6):189. doi: 10.3892/ol.2022.13309 EDN: YOADXG
  58. Zhang Q, Gao Y, Zhang R, et al. Personalized CT-based radiomics nomogram preoperative predicting ki-67 expression in gastrointestinal stromal tumors: a multicenter development and validation cohort. Clinical and Translational Medicine. 2020;9(1):12. doi: 10.1186/s40169-020-0263-4 EDN: GQEWHE
  59. Feng Q, Tang B, Zhang Y, Liu X. Prediction of the ki-67 expression level and prognosis of gastrointestinal stromal tumors based on CT radiomics nomogram. International Journal of Computer Assisted Radiology and Surgery. 2022;17(6):1167–1175. doi: 10.1007/s11548-022-02575-6 EDN: WMCKNC
  60. Liu M, Bian J. Radiomics signatures based on contrast-enhanced CT for preoperative prediction of the ki-67 proliferation state in gastrointestinal stromal tumors. Japanese Journal of Radiology. 2023;41(7):741–751. doi: 10.1007/s11604-023-01391-5 EDN: MGYVDQ
  61. Zhang XD, Zhang L, Gong T, et al. A combined radiomic model distinguishing GISTs from leiomyomas and schwannomas in the stomach based on endoscopic ultrasonography images. J Appl Clin Med Phys. 2023;24(7):e14023. doi: 10.1002/acm2.14023 EDN: GVPUPG
  62. Starmans MP, Timbergen MJ, Vos M, et al. Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach. Journal of Digital Imaging. 2022;35(2):127–136. doi: 10.1007/s10278-022-00590-2 EDN: FZSWKG
  63. Wang FH, Zheng HL, Li JT, et al. Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features. La radiologia medica. 2022;127(10):1085–1097. doi: 10.1007/s11547-022-01549-7 EDN: MDONPE
  64. Ao W, Cheng G, Lin B, et al. A novel CT-based radiomic nomogram for predicting the recurrence and metastasis of gastric stromal tumors. American journal of cancer research. 2021;11(6):3123–3134.

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