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Radiomics in gastrointestinal stromal tumours: an up-to-date review

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Abstract

Gastrointestinal stromal tumours are rare mesenchymal neoplasms originating from the Cajal cells and represent the most common sarcomas in the gastroenteric tract. Symptoms may be absent or non-specific, ranging from fatigue and weight loss to acute abdomen. Nowadays endoscopy, echoendoscopy, contrast-enhanced computed tomography, magnetic resonance imaging and positron emission tomography are the main methods for diagnosis. Because of their rarity, these neoplasms may not be included immediately in the differential diagnosis of a solitary abdominal mass. Radiomics is an emerging technique that can extract medical imaging information, not visible to the human eye, transforming it into quantitative data. The purpose of this review is to demonstrate how radiomics can improve the already known imaging techniques by providing useful tools for the diagnosis, treatment, and prognosis of these tumours.

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References

  1. Dei S, Molli T, Alessandro C, et al Linee guida SARCOMI DEI TESSUTI MOLLI E GIST In collaborazione con. Published online (2020)

  2. Mazur MT, Clark HB. Gastric stromal tumors: reappraisal of histogenesis. Am J Surg Pathol. 1983;76:507–19.

    Article  Google Scholar 

  3. Kindblom LG, Remotti HE, Aldenborg F, Meis-Kindblom JM. Gastrointestinal pacemaker cell tumor (GIPACT): gastrointestinal stromal tumors show phenotypic characteristics of the interstitial cells of Cajal. Am J Pathol. 1998;152(5):1259–69.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Akahoshi K, Oya M, Koga T, Shiratsuchi Y. Current clinical management of gastrointestinal stromal tumor. World J Gastroenterol. 2018;24(26):2806–17.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Schaefer IM, Mariño-Enríquez A, Fletcher JA. What is new in gastrointestinal stromal tumor? Adv Anat Pathol. 2017;24(5):259–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Liegl-Atzwanger B, Fletcher JA, Fletcher CD. Gastrointestinal stromal tumors. Virchows Arch. 2010;456(2):111–27 (Epub 2010 Feb 18).

    Article  PubMed  Google Scholar 

  7. Boikos SA, Pappo AS, Killian JK, LaQuaglia MP, Weldon CB, George S, Trent JC, von Mehren M, Wright JA, Schiffman JD, Raygada M, Pacak K, Meltzer PS, Miettinen MM, Stratakis C, Janeway KA, Helman LJ. Molecular subtypes of KIT/PDGFRA wild-type gastrointestinal stromal tumors: a report from the national institutes of health gastrointestinal stromal tumor clinic. JAMA Oncol. 2016;2(7):922–8.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Fletcher CD, Berman JJ, Corless C, Gorstein F, Lasota J, Longley BJ, Miettinen M, O’Leary TJ, Remotti H, Rubin BP, Shmookler B, Sobin LH, Weiss SW. Diagnosis of gastrointestinal stromal tumors: a consensus approach. Int J Surg Pathol. 2002;10(2):81–9.

    Article  PubMed  Google Scholar 

  9. Miettinen M, Lasota J. Gastrointestinal stromal tumors: pathology and prognosis at different sites. Semin Diagn Pathol. 2006;23(2):70–83.

    Article  PubMed  Google Scholar 

  10. Nishida T, Blay JY, Hirota S, Kitagawa Y, Kang YK. The standard diagnosis, treatment, and follow-up of gastrointestinal stromal tumors based on guidelines. Gastric Cancer. 2016;19(1):3–14.

    Article  CAS  PubMed  Google Scholar 

  11. Poveda A, García Del Muro X, López-Guerrero JA, Cubedo R, Martínez V, Romero I, Serrano C, Valverde C, Martín-Broto J; GEIS (Grupo Español de Investigación en Sarcomas/Spanish Group for Sarcoma Research). GEIS guidelines for gastrointestinal sarcomas (GIST). Cancer Treat Rev. 2017: 107–119.

  12. Serrano C, George S. Recent advances in the treatment of gastrointestinal stromal tumors. Ther Adv Med Oncol. 2014;6(3):115–27.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Nishida T. Therapeutic strategies for wild-type gastrointestinal stromal tumor: is it different from KIT or PDGFRA-mutated GISTs? Transl Gastroenterol Hepatol. 2017;16(2):92.

    Article  Google Scholar 

  14. Inoue A, Ota S, Yamasaki M, Batsaikhan B, Furukawa A, Watanabe Y. Gastrointestinal stromal tumors: a comprehensive radiological review. Jpn J Radiol. 2022;40(11):1105–20.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Riphaus A, Pech O, Steckstor M, Adam B. Gastrointestinal stromal tumors (GIST)– endoscopic image, endoscopic ultrasound, and value of endoscopic-guided fine-needle aspiration, video journal and encyclopedia of GI. Endoscopy. 2013;1(1):183–4.

    Google Scholar 

  16. Joensuu H, Fletcher C, Dimitrijevic S, Silberman S, Roberts P, Demetri G. Management of malignant gastrointestinal stromal tumours. Lancet Oncol. 2002;3(11):655–64.

    Article  CAS  PubMed  Google Scholar 

  17. Danti G, Addeo G, Cozzi D, Maggialetti N, Lanzetta MM, Frezzetti G, Masserelli A, Pradella S, Giovagnoni A, Miele V. Relationship between diagnostic imaging features and prognostic outcomes in gastrointestinal stromal tumors (GIST). Acta Biomed. 2019;90(5):9–19.

    PubMed  Google Scholar 

  18. 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. NCCN Task Force report: update on the management of patients with gastrointestinal stromal tumors. J Natl Compr Canc Netw. 2010 Suppl 2(0 2):S1–41; quiz S42–4.

  19. Hong X, Choi H, Loyer EM, Benjamin RS, Trent JC, Charnsangavej C. Gastrointestinal stromal tumor: role of CT in diagnosis and in response evaluation and surveillance after treatment with imatinib. Radiographics. 2006;26(2):481–95.

    Article  PubMed  Google Scholar 

  20. Yu MH, Lee JM, Baek JH, Han JK, Choi BI. MRI features of gastrointestinal stromal tumors. AJR Am J Roentgenol. 2014;203(5):980–91.

    Article  PubMed  Google Scholar 

  21. Petralia G, Zugni F, Summers PE, et al. Whole-body magnetic resonance imaging (WB-MRI) for cancer screening: recommendations for use. Radiol med. 2021;126:1434–50.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Jasperson KW, Kohlmann W, Gammon A, Slack H, Buchmann L, Hunt J, Kirchhoff AC, Baskin H, Shaaban A, Schiffman JD. Role of rapid sequence whole-body MRI screening in SDH-associated hereditary paraganglioma families. Fam Cancer. 2014;13(2):257–65.

    Article  CAS  PubMed  Google Scholar 

  23. Scapicchio C, Gabelloni M, Barucci A, Cioni D, Saba L, Neri E. A deep look into radiomics. Radiol Med. 2021;126(10):1296–311.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Vicini S, Bortolotto C, Rengo M, Ballerini D, Bellini D, Carbone I, Preda L, Laghi A, Coppola F, Faggioni L. A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers. Radiol Med. 2022;127(8):819–36.

    Article  PubMed  Google Scholar 

  25. Gurgitano M, Angileri SA, Rodà GM, Liguori A, Pandolfi M, Ierardi AM, Wood BJ, Carrafiello G. Interventional radiology ex-machina: impact of artificial Intelligence on practice. Radiol Med. 2021;126(7):998–1006.

    Article  PubMed  PubMed Central  Google Scholar 

  26. D’Angelo A, Orlandi A, Bufi E, Mercogliano S, Belli P, Manfredi R. Automated breast volume scanner (ABVS) compared to handheld ultrasound (HHUS) and contrast-enhanced magnetic resonance imaging (CE-MRI) in the early assessment of breast cancer during neoadjuvant chemotherapy: an emerging role to monitoring tumor response? Radiol Med. 2021;126(4):517–26.

    Article  PubMed  Google Scholar 

  27. Satake H, Ishigaki S, Ito R, Naganawa S. Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence. Radiol Med. 2022;127(1):39–56.

    Article  PubMed  Google Scholar 

  28. Palmisano A, Vignale D, Boccia E, Nonis A, Gnasso C, Leone R, Montagna M, Nicoletti V, Bianchi AG, Brusamolino S, Dorizza A, Moraschini M, Veettil R, Cereda A, Toselli M, Giannini F, Loffi M, Patelli G, Monello A, Iannopollo G, Ippolito D, Mancini EM, Pontone G, Vignali L, Scarnecchia E, Iannacone M, Baffoni L, Sperandio M, de Carlini CC, Sironi S, Rapezzi C, Antiga L, Jagher V, Di Serio C, Furlanello C, Tacchetti C, Esposito A. AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients. Radiol Med. 2022;127(9):960–72.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Matsoukas S, Scaggiante J, Schuldt BR, Smith CJ, Chennareddy S, Kalagara R, Majidi S, Bederson JB, Fifi JT, Mocco J, Kellner CP. Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: a systematic review and pooled analysis. Radiol Med. 2022;127(10):1106–23.

    Article  PubMed  Google Scholar 

  30. Zerunian M, Pucciarelli F, Caruso D, Polici M, Masci B, Guido G, De Santis D, Polverari D, Principessa D, Benvenga A, Iannicelli E, Laghi A. Artificial intelligence based image quality enhancement in liver MRI: a quantitative and qualitative evaluation. Radiol Med. 2022;127(10):1098–105.

    PubMed  PubMed Central  Google Scholar 

  31. Coppola F, Faggioni L, Regge D, Giovagnoni A, Golfieri R, Bibbolino C, Miele V, Neri E, Grassi R. Artificial intelligence: radiologists’ expectations and opinions gleaned from a nationwide online survey. Radiol Med. 2021;126(1):63–71.

    Article  PubMed  Google Scholar 

  32. Yang Z, Tang LH, Klimstra DS. Effect of tumor heterogeneity on the assessment of Ki67 labeling index in well-differentiated neuroendocrine tumors metastatic to the liver: implications for prognostic stratification. Am J Surg Pathol. 2011;35(6):853–60.

    Article  PubMed  Google Scholar 

  33. Hockel M, Knoop C, Schlenger K, et al. Intratumoral pO2 predicts survival in advanced cancer of the uterine cervix. Radiother Oncol. 1993;26(1):45–50.

    Article  CAS  PubMed  Google Scholar 

  34. Cozzi D, Bicci E, Cavigli E, Danti G, Bettarini S, Tortoli P, Mazzoni LN, Busoni S, Pradella S, Miele V. Radiomics in pulmonary neuroendocrine tumours (NETs). Radiol Med. 2022;127(6):609–15.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Gregucci F, Fiorentino A, Mazzola R, Ricchetti F, Bonaparte I, Surgo A, Figlia V, Carbonara R, Caliandro M, Ciliberti MP, Ruggieri R, Alongi F. Radiomic analysis to predict local response in locally advanced pancreatic cancer treated with stereotactic body radiation therapy. Radiol Med. 2022;127(1):100–7.

    Article  PubMed  Google Scholar 

  36. Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Grassi R, Grassi F, Ottaiano A, Nasti G, Tatangelo F, Pilone V, Miele V, Brunese MC, Izzo F, Petrillo A. Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases. Radiol Med. 2022;127(5):461–70.

    Article  PubMed  Google Scholar 

  37. Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B, Ganeshan B, Miles KA, Cook GJ, Goh V. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging. 2012;3(6):573–89.

    Article  PubMed  PubMed Central  Google Scholar 

  38. van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-“how-to” guide and critical reflection. Insights Imaging. 2020;11(1):91.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P, Cook G. Introduction to radiomics. J Nucl Med. 2020;61(4):488–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol. 2022;19(2):132–46.

    Article  CAS  PubMed  Google Scholar 

  41. Parmar C, Rios Velazquez E, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, Mitra S, Shankar BU, Kikinis R, Haibe-Kains B, Lambin P, Aerts HJ. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS ONE. 2014;9(7): e102107.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Bashir U, Siddique MM, Mclean E, Goh V, Cook GJ. Imaging Heterogeneity in lung cancer: techniques, applications, and challenges. AJR Am J Roentgenol. 2016;207(3):534–43.

    Article  PubMed  Google Scholar 

  43. Rogers W, Thulasi Seetha S, Refaee TAG, Lieverse RIY, Granzier RWY, Ibrahim A, Keek SA, Sanduleanu S, Primakov SP, Beuque MPL, Marcus D, van der Wiel AMA, Zerka F, Oberije CJG, van Timmeren JE, Woodruff HC, Lambin P. Radiomics: from qualitative to quantitative imaging. Br J Radiol. 2020;93(1108):20190948.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol. 2004;59:1061–9.

    Article  CAS  PubMed  Google Scholar 

  45. Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG, Bellomi M. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018;2(1):36.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Galloway MM. Texture analysis using gray level run lengths. Comput Graphics Image Process. 1975;4(2):172–9.

    Article  Google Scholar 

  47. Nioche C, Orlhac F, Buvat I, Texture-User Guide Local Image Features Extraction, 2022.

  48. Choudhary R, Gianey HK, Comprehensive Review On Supervised Machine Learning Algorithms, 2017, International Conference on Machine Learning and Data Science (MLDS), Noida, 2017 37–43.

  49. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJWL. Machine Learning methods for Quantitative Radiomic Biomarkers. Sci Rep. 2015;17(5):13087.

    Article  Google Scholar 

  50. Parmar C, Barry JD, Hosny A, Quackenbush J, Aerts HJWL. Data Analysis Strategies in Medical Imaging. Clin Cancer Res. 2018;24(15):3492–9.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Wong T-T. Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recogn. 2015;48(9):2839–46.

    Article  Google Scholar 

  52. Agazzi GM, Ravanelli M, Roca E, Medicina D, Balzarini P, Pessina C, Vermi W, Berruti A, Maroldi R, Farina D. CT texture analysis for prediction of EGFR mutational status and ALK rearrangement in patients with non-small cell lung cancer. Radiol Med. 2021;126(6):786–94.

    Article  PubMed  Google Scholar 

  53. Bracci S, Dolciami M, Trobiani C, Izzo A, Pernazza A, D’Amati G, Manganaro L, Ricci P. Quantitative CT texture analysis in predicting PD-L1 expression in locally advanced or metastatic NSCLC patients. Radiol Med. 2021;126(11):1425–33.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Danti G, Flammia F, Matteuzzi B, Cozzi D, Berti V, Grazzini G, Pradella S, Recchia L, Brunese L, Miele V. Gastrointestinal neuroendocrine neoplasms (GI-NENs): hot topics in morphological, functional, and prognostic imaging. Radiol Med. 2021;126(12):1497–507.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77.

    Article  PubMed  Google Scholar 

  56. Chiti G, Grazzini G, Flammia F, Matteuzzi B, Tortoli P, Bettarini S, Pasqualini E, Granata V, Busoni S, Messerini L, Pradella S, Massi D, Miele V. Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): a radiomic model to predict tumor grade. Radiol Med. 2022;127(9):928–38.

    Article  PubMed  Google Scholar 

  57. Benedetti G, Mori M, Panzeri MM, Barbera M, Palumbo D, Sini C, Muffatti F, Andreasi V, Steidler S, Doglioni C, Partelli S, Manzoni M, Falconi M, Fiorino C, De Cobelli F. CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors. Radiol Med. 2021;126(6):745–60.

    Article  PubMed  Google Scholar 

  58. Cusumano D, Meijer G, Lenkowicz J, Chiloiro G, Boldrini L, Masciocchi C, Dinapoli N, Gatta R, Casà C, Damiani A, Barbaro B, Gambacorta MA, Azario L, De Spirito M, Intven M, Valentini V. A field strength independent MR radiomics model to predict pathological complete response in locally advanced rectal cancer. Radiol Med. 2021;126(3):421–9.

    Article  PubMed  Google Scholar 

  59. Wang FH, Zheng HL, Li JT, Li P, Zheng CH, Chen QY, Huang CM, Xie JW. Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features. Radiol Med. 2022;127(10):1085–97.

    Article  PubMed  Google Scholar 

  60. Han D, Yu N, Yu Y, He T, Duan X. Performance of CT radiomics in predicting the overall survival of patients with stage III clear cell renal carcinoma after radical nephrectomy. Radiol Med. 2022;127(8):837–47.

    Article  PubMed  Google Scholar 

  61. Chiloiro G, Cusumano D, de Franco P, Lenkowicz J, Boldrini L, Carano D, Barbaro B, Corvari B, Dinapoli N, Giraffa M, Meldolesi E, Manfredi R, Valentini V, Gambacorta MA. Does restaging MRI radiomics analysis improve pathological complete response prediction in rectal cancer patients? A prognostic model development Radiol Med. 2022;127(1):11–20.

    PubMed  Google Scholar 

  62. Fan Y, Zhao Z. Wang, X et al Radiomics for prediction of response to EGFR-TKI based on metastasis/brain parenchyma (M/BP)-interface. Radiol med. 2022;127:1342–54.

    Article  PubMed  Google Scholar 

  63. De Robertis R, Geraci L, Tomaiuolo L, Bortoli L, Beleù A, Malleo G, D’Onofrio M. Liver metastases in pancreatic ductal adenocarcinoma: a predictive model based on CT texture analysis. Radiol Med. 2022;127(10):1079–84.

    Article  PubMed  Google Scholar 

  64. Masci GM, Ciccarelli F, Mattei FI, Grasso D, Accarpio F, Catalano C, Laghi A, Sammartino P, Iafrate F. Role of CT texture analysis for predicting peritoneal metastases in patients with gastric cancer. Radiol Med. 2022;127(3):251–8.

    Article  PubMed  Google Scholar 

  65. Yao F, Bian S, Zhu D, Yuan Y, Pan K, Pan Z, Feng X, Tang K, Yang Y. Machine learning-based radiomics for multiple primary prostate cancer biological characteristics prediction with 18F-PSMA-1007 PET: comparison among different volume segmentation thresholds. Radiol Med. 2022;127(10):1170–8.

    Article  PubMed  Google Scholar 

  66. Brunese L, Brunese MC, Carbone M, Ciccone V, Mercaldo F, Santone A. Automatic PI-RADS assignment by means of formal methods. Radiol Med. 2022;127(1):83–9.

    Article  PubMed  Google Scholar 

  67. Nardone V, Reginelli A, Grassi R, Boldrini L, Vacca G, D’Ippolito E, Annunziata S, Farchione A, Belfiore MP, Desideri I, Cappabianca S. Delta radiomics: a systematic review. Radiol Med. 2021;126(12):1571–83.

    Article  PubMed  Google Scholar 

  68. Xu F, Ma X, Wang Y, Tian Y, Tang W, Wang M, Wei R, Zhao X. CT texture analysis can be a potential tool to differentiate gastrointestinal stromal tumors without KIT exon 11 mutation. Eur J Radiol. 2018;107:90–7.

    Article  PubMed  Google Scholar 

  69. Zhang QW, Gao YJ, Zhang RY, Zhou XX, Chen SL, Zhang Y, Liu Q, Xu JR, Ge ZZ. Personalized CT-based radiomics nomogram preoperative predicting Ki-67 expression in gastrointestinal stromal tumors: a multicenter development and validation cohort. Clin Transl Med. 2020;9(1):12.

    Article  PubMed  PubMed Central  Google Scholar 

  70. 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. Int J Comput Assist Radiol Surg. 2022;17(6):1167–75.

    Article  PubMed  Google Scholar 

  71. Starmans MPA, Timbergen MJM, Vos M, Renckens M, Grünhagen DJ, van Leenders GJLH, Dwarkasing RS, Willemssen FEJA, Niessen WJ, Verhoef C, Sleijfer S, Visser JJ, Klein S. Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach. J Digit Imaging. 2022;35(2):127–36.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Zhuo M, Guo J, Tang Y, Tang X, Qian Q, Chen Z. Ultrasound radiomics model-based nomogram for predicting the risk Stratification of gastrointestinal stromal tumors. Front Oncol. 2022;26(12): 905036.

    Article  Google Scholar 

  73. 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. Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively. Eur Radiol. 2019;29(3):1074–82.

    Article  PubMed  Google Scholar 

  74. Song Y, Li J, Wang H, Liu B, Yuan C, Liu H, Zheng Z, Min F, Li Y. Radiomics nomogram based on contrast-enhanced CT to predict the malignant potential of gastrointestinal stromal tumor: a two-center study. Acad Radiol. 2022;29(6):806–16.

    Article  PubMed  Google Scholar 

  75. Giganti F, Antunes S, Salerno A, Ambrosi A, Marra P, Nicoletti R, Orsenigo E, Chiari D, Albarello L, Staudacher C, Esposito A, Del Maschio A, De Cobelli F. Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol. 2017;27(5):1831–9.

    Article  PubMed  Google Scholar 

  76. 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. Texture analysis of CT images in predicting malignancy risk of gastrointestinal stromal tumours. Clin Radiol. 2018;73(3):266–74.

    Article  CAS  PubMed  Google Scholar 

  77. Chu H, Pang P, He J, Zhang D, Zhang M, Qiu Y, Li X, Lei P, Fan B, Xu R. Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors. Sci Rep. 2021;11(1):12009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. 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. Transl Oncol. 2019;12(9):1229–36.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Zhang L, Kang L, Li G, Zhang X, Ren J, Shi Z, Li J, Yu S. Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors. Radiol Med. 2020;125(5):465–73.

    Article  PubMed  Google Scholar 

  80. Jia X, Wan L, Chen X, Ji W, Huang S, Qi Y, Cui J, Wei S, Cheng J, Chai F, Feng C, Liu Y, Zhang H, Sun Y, Hong N, Rao S, Zhang X, Xiao Y, Ye Y, Tang L, Wang Y. Risk stratification for 1- to 2-cm gastric gastrointestinal stromal tumors: visual assessment of CT and EUS high-risk features versus CT radiomics analysis. Eur Radiol. 2022 30.

  81. Zhang QW, Zhou XX, Zhang RY, Chen SL, Liu Q, Wang J, Zhang Y, Lin J, Xu JR, Gao YJ, Ge ZZ. Comparison of malignancy-prediction efficiency between contrast and non-contract CT-based radiomics features in gastrointestinal stromal tumors: a multicenter study. Clin Transl Med. 2020;10(3): e291.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Palatresi D, Fedeli F, Danti G, Pasqualini E, Castiglione F, Messerini L, Massi D, Bettarini S, Tortoli P, Busoni S, Pradella S, Miele V. Correlation of CT radiomic features for GISTs with pathological classification and molecular subtypes: preliminary and monocentric experience. Radiol Med. 2022;127(2):117–28.

    Article  PubMed  Google Scholar 

  83. Yang L, Zheng T, Dong Y, Wang Z, Liu D, Du J, Wu S, Shi Q, Liu L. MRI texture-based models for predicting mitotic index and risk classification of gastrointestinal stromal tumors. J Magn Reson Imaging. 2021;53(4):1054–65.

    Article  PubMed  Google Scholar 

  84. Yang L, Du D, Zheng T, Liu L, Wang Z, Du J, Yi H, Cui Y, Liu D, Fang Y. Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI. Front Oncol. 2022;23(12): 948557.

    Article  Google Scholar 

  85. Ning Z, Luo J, Li Y, Han S, Feng Q, Xu Y, Chen W, Chen T, Zhang Y. Pattern classification for gastrointestinal stromal tumors by integration of radiomics and deep convolutional features. IEEE J Biomed Health Inform. 2019;23(3):1181–91.

    Article  PubMed  Google Scholar 

  86. Ba-Ssalamah A, Muin D, Schernthaner R, Kulinna-Cosentini C, Bastati N, Stift J, Gore R, Mayerhoefer ME. Texture-based classification of different gastric tumors at contrast-enhanced CT. Eur J Radiol. 2013;82(10):e537–43.

    Article  PubMed  Google Scholar 

  87. Zheng J, Xia Y, Xu A, Weng X, Wang X, Jiang H, Li Q, Li F. Combined model based on enhanced CT texture features in liver metastasis prediction of high-risk gastrointestinal stromal tumors. Abdom Radiol (NY). 2022;47(1):85–93.

    Article  PubMed  Google Scholar 

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Galluzzo, A., Boccioli, S., Danti, G. et al. Radiomics in gastrointestinal stromal tumours: an up-to-date review. Jpn J Radiol 41, 1051–1061 (2023). https://doi.org/10.1007/s11604-023-01441-y

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