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Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features

  • Abdominal Radiology
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Objective

Development and validation of a radiomics nomogram for predicting recurrence and adjuvant therapy benefit populations in high/intermediate-risk gastrointestinal stromal tumors (GISTs) based on computed tomography (CT) radiomic features.

Methods

Retrospectively collected from 2009.07 to 2015.09, 220 patients with pathological diagnosis of intermediate- and high-risk stratified gastrointestinal stromal tumors and received imatinib treatment were randomly divided into (6:4) training cohort and validation cohort. The 2D-tumor region of interest (ROI) was delineated from the portal-phase images on contrast-enhanced (CE) CT, and radiological features were extracted. The most valuable radiological features were obtained using a Lasso-Cox regression model. Integrated construction was conducted of nomograms of radiomics characteristics to predict recurrence-free survival (RFS) in patients receiving adjuvant therapy.

Results

Eight radiomic signatures were finally selected. The area under the curve (AUC) of the radiomics signature model for predicting 3-, 5-, and 7-year RFS in the training and validation cohorts (training cohort AUC = 0.80, 0.84, 0.76; validation cohort AUC = 0.78, 0.80, 0.76). The constructed radiomics nomogram was more accurate than the clinicopathological nomogram for predicting RFS in GIST (C-index: 0.864 95%CI, 0.817–0.911 vs. 0.733 95%CI, 0.675–0.791). Kaplan–Meier survival curve analysis showed a greater benefit from adjuvant therapy in patients with high radiomics scores (training cohort: p < 0.0001; validation cohort: p = 0.017), while there was no significant difference in the low-score group (p > 0.05).

Conclusion

In this study, a nomogram constructed based on preoperative CT radiomics features could be used for RFS prediction in high/intermediate-risk GISTs and assist the clinical decision-making for GIST patients.

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Data availability

The authors declare that they had full access to all of the data in this study and the authors take complete responsibility for the integrity of the data and the accuracy of the data analysis. Data are available for bona fide researchers who request it from the authors.

Abbreviations

GIST(s):

Gastrointestinal stromal tumor(s)

CT:

Computed tomography

ROI:

Region of interest

CE-CT:

Contrast-enhanced computed tomography

LASSO-COX:

Least absolute shrinkage and selection operator-Cox regression model

RFS:

Recurrence-free survival

AUC:

Area under the curve

ICC:

Intraclass correlation coefficient

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run length matrix

GLSZM:

Gray-level size zone matrix

GLDM:

Gray-level dependence matrix

NGTDM:

Neighboring gray tone difference matrix

Rad-score:

Radiomics score

BMI:

Body mass index

HR:

Hazard ratio

CI:

Confidence interval

References

  1. Joensuu H, Hohenberger P, Corless CL (2013) Gastrointestinal stromal tumour. Lancet 382(9896):973–983. https://doi.org/10.1016/S0140-6736(13)60106-3

    Article  CAS  PubMed  Google Scholar 

  2. Anderson WJ, Doyle LA (2021) Updates from the 2020 world health organization classification of soft tissue and bone tumours. Histopathology 78(5):644–657. https://doi.org/10.1111/his.14265

    Article  PubMed  Google Scholar 

  3. Gronchi A, Miah AB, Dei Tos AP, Abecassis N et al (2021) Soft tissue and visceral sarcomas: ESMO-EURACAN-GENTURIS clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 32(11):1348–1365. https://doi.org/10.1016/j.annonc.2021.07.006

    Article  CAS  PubMed  Google Scholar 

  4. Joensuu H, Eriksson M, Hall KS, Reichardt A, Hermes B, Schütte J, Reichardt P (2020) Survival outcomes associated with 3 years vs 1 year of adjuvant imatinib for patients with high-risk gastrointestinal stromal tumors: an analysis of a randomized clinical trial after 10-year follow-up. JAMA Oncol 6(8):1241–1246. https://doi.org/10.1001/jamaoncol.2020.2091

    Article  PubMed  Google Scholar 

  5. van der Graaf WTA, Tielen R, Bonenkamp JJ et al (2018) Nationwide trends in the incidence and outcome of patients with gastrointestinal stromal tumour in the imatinib era. Br J Surg 105(8):1020–1027. https://doi.org/10.1002/bjs.10809

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Randall RL, Benjamin RS, Boles S et al (2018) Soft tissue sarcoma, version 1.2018, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 16(5):536–563

    Article  Google Scholar 

  7. Li J, Ye Y, Wang J et al (2017) Chinese consensus guidelines for diagnosis and management of gastrointestinal stromal tumor. Chin J Cancer Res. 29(4):281–293

    Article  Google Scholar 

  8. Hølmebakk T, Wiedswang AM, Meza-Zepeda LA et al (2021) Integrating anatomical, molecular and clinical risk factors in gastrointestinal stromal tumor of the stomach. Ann Surg Oncol 28(11):6837–6845. https://doi.org/10.1245/s10434-021-09605-8

    Article  PubMed  PubMed Central  Google Scholar 

  9. Nishida T, Hølmebakk T, Raut CP, Rutkowski P (2019) Defining tumor rupture in gastrointestinal stromal tumor. Ann Surg Oncol. 26(6):1669–1675. https://doi.org/10.1245/s10434-019-07297-9

    Article  PubMed  PubMed Central  Google Scholar 

  10. Hølmebakk T, Hompland I, Bjerkehagen B et al (2018) Recurrence-free survival after resection of gastric gastrointestinal stromal tumors classified according to a strict definition of tumor rupture: a population-based study. Ann Surg Oncol 25(5):1133–1139. https://doi.org/10.1245/s10434-018-6353-5

    Article  PubMed  Google Scholar 

  11. Rutkowski P, Ziętek M, Cybulska-Stopa B et al (2021) The analysis of 3-year adjuvant therapy with imatinib in patients with high-risk molecular profiled gastrointestinal stromal tumors (GIST) treated in routine practice. Eur J Surg Oncol 47(5):1191–1195. https://doi.org/10.1016/j.ejso.2020.08.004

    Article  PubMed  Google Scholar 

  12. Li GZ, Raut CP (2019) Targeted therapy and personalized medicine in gastrointestinal stromal tumors: drug resistance, mechanisms, and treatment strategies. Onco Targets Ther 12:5123–5133. https://doi.org/10.2147/OTT.S180763

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. von Mehren M, Kane JM, Bui MM et al (2020) NCCN guidelines insights: soft tissue sarcoma, version 1.2021. J Natl Compr Canc Netw 18(12):1604–1612. https://doi.org/10.6004/jnccn.2020.0058

    Article  Google Scholar 

  14. Patel DJ, Lutfi W, Sweigert P, Eguia E, Abood G, Knab L, Baker MS (2020) Adjuvant systemic therapy for intermediate and large gastric gastrointestinal stromal tumors (GISTs): Is there a survival benefit following margin negative surgical resection? Am J Surg 219(3):436–439

    Article  Google Scholar 

  15. Joensuu H, Eriksson M, Hall KS, Hartmann JT, Pink D, Schütte J, Reichardt P (2012) One vs three years of adjuvant imatinib for operable gastrointestinal stromal tumor: a randomized trial. JAMA 307(12):1265–1272. https://doi.org/10.1001/jama.2012.347

    Article  CAS  PubMed  Google Scholar 

  16. Raut CP, Espat NJ, Maki RG, Araujo DM, Trent J, Williams TF, DeMatteo RP (2018) Efficacy and tolerability of 5-year adjuvant imatinib treatment for patients with resected intermediate-or high-risk primary gastrointestinal stromal tumor: the PERSIST-5 clinical trial. JAMA Oncol 4(12):e184060–e184060. https://doi.org/10.1001/jamaoncol.2018.4060

    Article  PubMed  PubMed Central  Google Scholar 

  17. Autorino R, Gui B, Panza G et al (2022) Radiomics-based prediction of two-year clinical outcome in locally advanced cervical cancer patients undergoing neoadjuvant chemoradiotherapy. Radiol Med 127(5):498–506. https://doi.org/10.1007/s11547-022-01482-9

    Article  PubMed  PubMed Central  Google Scholar 

  18. Thawani R, McLane M, Beig N et al (2018) Radiomics and radiogenomics in lung cancer: a review for the clinician. Lung Cancer 115:34–41. https://doi.org/10.1016/j.lungcan.2017.10.015

    Article  PubMed  Google Scholar 

  19. Chetan MR, Gleeson FV (2021) Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Radiol 31(2):1049–1058. https://doi.org/10.1007/s00330-020-07141-9

    Article  PubMed  Google Scholar 

  20. Park H, Lim Y, Ko ES et al (2018) Radiomics signature on magnetic resonance imaging: association with disease-free survival in patients with invasive breast cancer. Clin Cancer Res 24(19):4705–4714. https://doi.org/10.1158/1078-0432.CCR-17-3783

    Article  PubMed  Google Scholar 

  21. Jiang Y, Wang W, Chen C et al (2019) Radiomics signature on computed tomography imaging: association with lymph node metastasis in patients with gastric cancer. Front Oncol 9:340. https://doi.org/10.3389/fonc.2019.00340

    Article  PubMed  PubMed Central  Google Scholar 

  22. Sun R, Limkin EJ, Vakalopoulou M et al (2018) A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol 19(9):1180–1191. https://doi.org/10.1016/S1470-2045(18)30413-3

    Article  CAS  PubMed  Google Scholar 

  23. Dongsheng Gu, Yabin Hu, Ding H et al (2019) CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur Radiol 29(12):6880–6890. https://doi.org/10.1007/s00330-019-06176-x

    Article  Google Scholar 

  24. Wang C, Li H, Jiaerken Y et al (2019) Building CT radiomics-based models for preoperatively predicting malignant potential and mitotic count of gastrointestinal stromal tumors. Transl Oncol 12(9):1229–1236. https://doi.org/10.1016/j.tranon.2019.06.005

    Article  PubMed  PubMed Central  Google Scholar 

  25. Zhao Y, Feng M, Wang M et al (2021) CT Radiomics for the Preoperative Prediction of Ki67 Index in gastrointestinal stromal tumors: a multi-center study. Front Oncol 11:689136. https://doi.org/10.3389/fonc.2021.689136

    Article  PubMed  PubMed Central  Google Scholar 

  26. Starmans MPA, Timbergen MJM, Vos M et al (2022) Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach. J Digit Imaging 35(2):127–136. https://doi.org/10.1007/s10278-022-00590-2

    Article  PubMed  PubMed Central  Google Scholar 

  27. Zhang L, Kang L, Li G et al (2020) Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors. Radiol Med 125(5):465–473. https://doi.org/10.1007/s11547-020-01138-6

    Article  PubMed  Google Scholar 

  28. Palatresi D, Fedeli F, Danti G et al (2022) Correlation of CT radiomic features for GISTs with pathological classification and molecular subtypes: preliminary and monocentric experience. Radiol Med 127(2):117–128. https://doi.org/10.1007/s11547-021-01446-5

    Article  PubMed  Google Scholar 

  29. Zhang QW, Gao YJ, Zhang RY, Zhou XX, Chen SL, Zhang Y, Ge ZZ (2020) Personalized CT-based radiomics nomogram preoperative predicting Ki-67 expression in gastrointestinal stromal tumors: a multicenter development and validation cohort. Clin Trans Med 9(1):1–11. https://doi.org/10.1186/s40169-020-0263-4

    Article  Google Scholar 

  30. Xue C, Yuan J, Lo GG, Chang AT, Poon DM, Wong OL, Chu WC (2021) Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review. Quant Imaging Med Surg 11(10):4431

    Article  Google Scholar 

  31. Zwanenburg A, Vallières M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295(2):328–338. https://doi.org/10.1148/radiol.2020191145

    Article  PubMed  Google Scholar 

  32. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77(21):e104–e107. https://doi.org/10.1158/0008-5472

    Article  PubMed  PubMed Central  Google Scholar 

  33. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 37(5):1483–1503. https://doi.org/10.1148/rg.2017170056

    Article  PubMed  Google Scholar 

  34. Mullah MAS, Hanley JA, Benedetti A (2021) LASSO type penalized spline regression for binary data. BMC Med Res Methodol 21(1):1–14. https://doi.org/10.1186/s12874-021-01234-9

    Article  Google Scholar 

  35. Cheung LC, Pan Q, Hyun N, Katki HA (2019) Prioritized concordance index for hierarchical survival outcomes. Statist Med 38(15):2868–2882. https://doi.org/10.1002/sim.8157

    Article  Google Scholar 

  36. Deniffel D, Abraham N, Namdar K et al (2020) Using decision curve analysis to benchmark performance of a magnetic resonance imaging-based deep learning model for prostate cancer risk assessment. Eur Radiol 30(12):6867–6876. https://doi.org/10.1007/s00330-020-07030-1

    Article  PubMed  Google Scholar 

  37. Nishida T, Sato S, Ozaka M et al (2022) Long-term adjuvant therapy for high-risk gastrointestinal stromal tumors in the real world. Gastric Cancer. https://doi.org/10.1007/s10120-022-01310-z

    Article  PubMed  Google Scholar 

  38. Kang S, Ryu M-H, Bang YH et al (2021) Adjuvant imatinib treatment for 5-years vs 3-years in patients with ruptured localized gastrointestinal stromal tumor: a retrospective analysis. Cancer Res Treat. https://doi.org/10.4143/crt.2021.1040

    Article  PubMed  PubMed Central  Google Scholar 

  39. Li J, Ye Y, Wang J et al (2017) Chinese consensus guidelines for diagnosis and management of gastrointestinal stromal tumor. Chin J Cancer Res 29:281–293

    Article  Google Scholar 

  40. Xin Wu, Li J, Wentong Xu et al (2018) Postoperative imatinib in patients with intermediate risk gastrointestinal stromal tumor. Future Oncol 14(17):1721–1729. https://doi.org/10.2217/fon-2017-0691

    Article  CAS  Google Scholar 

  41. Lin Y, Wang M, Jia J et al (2020) Development and validation of a prognostic nomogram to predict recurrence in high-risk gastrointestinal stromal tumour: A retrospective analysis of two independent cohorts. EbioMedicine 60:103016

    Article  Google Scholar 

  42. Jiang Y, Chen C, Xie J, Wang W, Zha X, Lv W, Li G (2018) Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer. EbioMedicine 36:171–182. https://doi.org/10.1016/j.ebiom.2018.09.007

    Article  PubMed  PubMed Central  Google Scholar 

  43. Choe J, Lee SM, Do KH et al (2020) Outcome prediction in resectable lung adenocarcinoma patients: value of CT radiomics. Eur Radiol 30(9):4952–4963. https://doi.org/10.1007/s00330-020-06872-z

    Article  PubMed  Google Scholar 

  44. Fan S, Cui X, Liu C et al (2021) CT-based radiomics signature: a potential biomarker for predicting postoperative recurrence risk in stage ii colorectal cancer. Front Oncol 19(11):644933. https://doi.org/10.3389/fonc.2021.644933

    Article  Google Scholar 

  45. Park S, Sham JG, Kawamoto S et al (2021) CT radiomics-based preoperative survival prediction in patients with pancreatic ductal adenocarcinoma. AJR Am J Roentgenol 217(5):1104–1112. https://doi.org/10.2214/AJR.20.23490

    Article  PubMed  Google Scholar 

  46. Tang S, Jing Ou, Liu J et al (2021) Application of contrast-enhanced CT radiomics in prediction of early recurrence of locally advanced oesophageal squamous cell carcinoma after trimodal therapy. Cancer Imaging 21(1):38. https://doi.org/10.1186/s40644-021-00407-5

    Article  PubMed  PubMed Central  Google Scholar 

  47. Chen T, Lili Xu, Ye L et al (2019) A new nomogram for recurrence-free survival prediction of gastrointestinal stromal tumors: comparison with current risk classification methods. Eur J Surg Oncol 45(6):1109–1114. https://doi.org/10.1016/j.ejso.2018.12.014

    Article  PubMed  Google Scholar 

  48. Lin Y, Wang M, Jia J et al (2020) Development and validation of a prognostic nomogram to predict recurrence in high-risk gastrointestinal stromal tumour: a retrospective analysis of two independent cohorts. EbioMedicine 60:103016. https://doi.org/10.1016/j.ebiom.2020.103016

    Article  PubMed  PubMed Central  Google Scholar 

  49. Cao X, Cui J, Yu T, Li Z, Zhao G (2020) Fibrinogen/albumin ratio index is an independent prognosis predictor of recurrence-free survival in patients after surgical resection of gastrointestinal stromal tumors. Front Oncol 10:1459. https://doi.org/10.3389/fonc.2020.01459

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank all patients, their families, and all investigators involved in the present study. At the same time, I would like to thank my lover Ling Dan.

Funding

This research was funded by Construction Project of Fujian Province Minimally Invasive Medical Center (No. [2021] 662).

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Author notes

  1. Fu-Hai Wang and Hua-Long Zheng authors have contributed equally to this work.

Authors and Affiliations

  1. Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China

    Fu-Hai Wang, Hua-Long Zheng, Jin-Tao Li, Ping Li, Chao-Hui Zheng, Qi-Yue Chen, Chang-Ming Huang & Jian-Wei Xie

  2. Fujian Provincial Minimally Invasive Medical Center, Fujian, China

    Fu-Hai Wang, Hua-Long Zheng, Jin-Tao Li, Ping Li, Chao-Hui Zheng, Qi-Yue Chen, Chang-Ming Huang & Jian-Wei Xie

  3. Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China

    Fu-Hai Wang, Hua-Long Zheng, Jin-Tao Li, Ping Li, Chao-Hui Zheng, Qi-Yue Chen, Chang-Ming Huang & Jian-Wei Xie

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  1. Fu-Hai Wang
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Contributions

J.-W.X. contributed to conceptualization; H.-L.Z. was involved in data curation; F.-H.W. contributed to formal analysis; C.-M.H. was involved in funding acquisition; J.-T.L. contributed to investigation; F.-H.W. and J.-W.X. were involved in methodology; P.L. contributed to project administration; P.L. and C.-M.H. were involved in resources; F.-H.W. and J.-T.L. provided software; H.-L.Z. and Q.-Y.C. were involved in validation; C.-H.Z. contributed to visualization; F.-H.W. and H.-L.Z. were involved in writing—original draft; and Q.-Y.C., C.-M.H., and J.-W.X contributed to writing—review and editing.

Corresponding authors

Correspondence to Qi-Yue Chen, Chang-Ming Huang or Jian-Wei Xie.

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This study is a retrospective study, and patients’ informed consent was waived with the approval of the Institutional Review Board.

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The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board.

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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. Radiol med 127, 1085–1097 (2022). https://doi.org/10.1007/s11547-022-01549-7

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