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Prediction of the Ki-67 expression level and prognosis of gastrointestinal stromal tumors based on CT radiomics nomogram

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

To build and validate a radiomics nomogram integrated with the radiomics signature and subjective CT characteristics to predict the Ki-67 expression level of gastrointestinal stromal tumors (GISTs). Moreover, the purpose was to compare the performance of pathological Ki-67 expression level with predicted Ki-67 expression level in estimating the prognosis of GISTs patients.

Methods

According to pathological results, patients were classified into high-Ki-67 labeling index group (Ki-67 LI ≥ 5%) and low-Ki-67 LI group (Ki-67 LI < 5%). Radiomics features extracted from contrast-enhanced CT(CECT) images were selected and classified to build a radiomics signature. A combined model was built by incorporating radiomics signature and determinant subjective CT characteristics using multivariate logistic regression analysis. The diagnostic performance of the radiomics signature, subjective CT model and combined model were explored by receiver operating characteristic (ROC) curve analysis and Delong test. The model with best diagnostic performance was then set up for the prediction nomogram. Recurrence-free survival (RFS) rates were compared utilizing Kaplan–Meier curve.

Results

The generated combined model yielded the best diagnostic performance with area under the curve (AUC) values of 0.738 [95% confidence interval (CI): 0.669–0.807] and 0.772 (95% CI 0.683–0.860) in the training set and testing set respectively. The nomogram based on the combined model demonstrated good calibration in the training set and testing set (both P > 0.05). Patients of high-Ki-67 LI group predicted by our nomogram had a poorer RFS than patients of low–Ki-67 LI group (P < 0.0001).

Conclusion

This radiomics nomogram based on CECT had a satisfactory performance in predicting both the Ki-67 expression level and prognosis noninvasively in patients with GISTs, which may serve as an effective imaging tool that can assist in guiding personalized clinical treatment.

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References

  1. Nishida T, Blay JY, Hirota S, Kitagawa Y, Kang YK (2016) The standard diagnosis, treatment, and follow-up of gastrointestinal stromal tumors based on guidelines. Gastric Cancer 19:3–14. https://doi.org/10.1007/s10120-015-0526-8

    Article  CAS  PubMed  Google Scholar 

  2. von Mehren M, Joensuu H (2018) Gastrointestinal stromal tumors. J Clin Oncol 36:136–143. https://doi.org/10.1200/JCO.2017.74.9705

    Article  Google Scholar 

  3. Sicklick JK, Lopez NE (2013) Optimizing surgical and imatinib therapy for the treatment of gastrointestinal stromal tumors. J Gastrointest Surg 17:1997–2006. https://doi.org/10.1007/s11605-013-2243-0

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  5. Hedenström P, Nilsson B, Demir A, Andersson C, Enlund F, Nilsson O, Sadik R (2017) Characterizing gastrointestinal stromal tumors and evaluating neoadjuvant imatinib by sequencing of endoscopic ultrasound-biopsies. World J Gastroenterol 23:5925–5935. https://doi.org/10.3748/wjg.v23.i32.5925

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Scholzen T, Gerdes J (2000) The Ki-67 protein: from the known and the unknown. J Cell Physiol 182:311–322

    Article  CAS  Google Scholar 

  7. Petrelli F, Viale G, Cabiddu M, Barni S (2015) Prognostic value of different cut-off levels of Ki-67 in breast cancer: a systematic review and meta-analysis of 64196 patients. Breast Cancer Res Treat 153:477–479. https://doi.org/10.1007/s10549-015-3559-0

    Article  PubMed  Google Scholar 

  8. Yang C, Su H, Liao X, Han C, Yu T, Zhu G, Wang X, Winkler CA, O’Brien SJ, Peng T (2018) Marker of proliferation Ki-67 expression is associated with transforming growth factor beta 1 and can predict the prognosis of patients with hepatic B virus-related hepatocellular carcinoma. Cancer Manag Res 10:679–696. https://doi.org/10.2147/CMAR.S162595

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Lei B, Liu S, Qi W, Zhao Y, Li Y, Lin N, Xu X, Zhi C, Mei J, Yan Z, Wan L, Shen H (2013) PBK/TOPK expression in non-small-cell lung cancer: its correlation and prognostic significance with Ki67 and p53 expression. Histopathology 63:696–703. https://doi.org/10.1111/his.12215

    Article  PubMed  Google Scholar 

  10. Liu X, Qiu H, Zhang P, Feng X, Chen T, Li Y, Tao K, Li G, Sun X, Zhou Z (2018) Ki-67 labeling index may be a promising indicator to identify “very high-risk” gastrointestinal stromal tumor: a multicenter retrospective study of 1022 patients. Hum Pathol 74:17–24. https://doi.org/10.1016/j.humpath.2017.09.003

    Article  CAS  PubMed  Google Scholar 

  11. Pyo JS, Kang G, Sohn JH (2016) Ki-67 labeling index can be used as a prognostic marker in gastrointestinal stromal tumor: a systematic review and meta-analysis. Int J Biol Markers 31:204–210. https://doi.org/10.5301/jbm.5000183

    Article  Google Scholar 

  12. Belev B, Brčić I, Prejac J, Golubić ZA, Vrbanec D, Božikov J, Alerić I, Boban M, Razumović JJ (2013) Role of Ki-67 as a prognostic factor in gastrointestinal stromal tumors. World J Gastroenterol 19:523–527. https://doi.org/10.3748/wjg.v19.i4.523

    Article  PubMed  PubMed Central  Google Scholar 

  13. Li H, Ren G, Cai R, Chen J, Wu X, Zhao J (2018) A correlation research of Ki67 index, CT features, and risk stratification in gastrointestinal stromal tumor. Cancer Med 7:4467–4474. https://doi.org/10.1002/cam4.1737

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577. https://doi.org/10.1148/radiol.2015151169

    Article  PubMed  Google Scholar 

  15. Mao B, Zhang L, Ning P, Ding F, Wu F, Lu G, Geng Y, Ma J (2020) Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics. Eur Radiol 30:6924–6932. https://doi.org/10.1007/s00330-020-07056-5

    Article  PubMed  Google Scholar 

  16. Chen X, Yang Z, Yang J, Liao Y, Pang P, Fan W, Chen X (2020) Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study. Cancer Imaging 20:24. https://doi.org/10.1186/s40644-020-00302-5

    Article  PubMed  PubMed Central  Google Scholar 

  17. Dercle L, Fronheiser M, Lu L, Du S, Hayes W, Leung DK, Roy A, Wilkerson J, Guo P, Fojo AT, Schwartz LH, Zhao B (2020) Identification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics. Clin Cancer Res 26:2151–2162. https://doi.org/10.1158/1078-0432.CCR-19-2942

    Article  CAS  PubMed  Google Scholar 

  18. 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. https://doi.org/10.1007/s00330-018-5629-2

    Article  PubMed  Google Scholar 

  19. Wang C, Li H, Jiaerken Y, Huang P, Sun L, Dong F, Huang Y, Dong D, Tian J, Zhang M (2019) Building CT radiomics-based models for preoperatively predicting malignant potential and mitotic count of gastrointestinal stromal tumors. Transl Oncol 12:1229–1236. https://doi.org/10.1016/j.tranon.2019.06.005

    Article  PubMed  PubMed Central  Google Scholar 

  20. Fu J, Fang MJ, Dong D, Li J, Sun YS, Tian J, Tang L (2020) Heterogeneity of metastatic gastrointestinal stromal tumor on texture analysis: DWI texture as potential biomarker of overall survival. Eur J Radiol 125:108825. https://doi.org/10.1016/j.ejrad.2020.108825

    Article  PubMed  Google Scholar 

  21. 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. https://doi.org/10.1016/j.ejrad.2018.07.025

    Article  PubMed  Google Scholar 

  22. Wu H, Han X, Wang Z, Mo L, Liu W, Guo Y, Wei X, Jiang X (2020) Prediction of the Ki-67 marker index in hepatocellular carcinoma based on CT radiomics features. Phys Med Biol 65:235048. https://doi.org/10.1088/1361-6560/abac9c

    Article  CAS  PubMed  Google Scholar 

  23. Ma W, Ji Y, Qi L, Guo X, Jian X, Liu P (2018) Breast cancer Ki67 expression prediction by DCE-MRI radiomics features. Clin Radiol 73:909.e1-909.e5. https://doi.org/10.1016/j.crad.2018.05.027

    Article  CAS  Google Scholar 

  24. Gu Q, Feng Z, Liang Q, Li M, Deng J, Ma M, Wang W, Liu J, Liu P, Rong P (2019) Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer. Eur J Radiol 118:32–37. https://doi.org/10.1016/j.ejrad.2019.06.025

    Article  PubMed  Google Scholar 

  25. Joensuu H (2008) Risk stratification of patients diagnosed with gastrointestinal stromal tumor. Hum Pathol 39:1411–1419. https://doi.org/10.1016/j.humpath.2008.06.025

    Article  PubMed  Google Scholar 

  26. Brancatelli G, Federle MP, Grazioli L, Blachar A, Peterson MS, Thaete L (2001) Focal nodular hyperplasia: CT findings with emphasis on multiphasic helical CT in 78 patients. Radiology 219:61–68. https://doi.org/10.1148/radiology.219.1.r01ap0361

    Article  CAS  PubMed  Google Scholar 

  27. Miettinen M, Lasota J (2006) Gastrointestinal stromal tumors: pathology and prognosis at different sites. Semin Diagn Pathol 23:70–83. https://doi.org/10.1053/j.semdp.2006.09.001

    Article  PubMed  Google Scholar 

  28. Ye Z, Jiang H, Chen J, Liu X, Wei Y, Xia C, Duan T, Cao L, Zhang Z, Song B (2019) Texture analysis on gadoxetic acid enhanced-MRI for predicting Ki-67 status in hepatocellular carcinoma: a prospective study. Chin J Cancer Res 31:806–817. https://doi.org/10.21147/j.issn.1000-9604.2019.05.10

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Peng J, Zhang J, Zhang Q, Xu Y, Zhou J, Liu L (2018) A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma. Diagn Interv Radiol 24:121–127. https://doi.org/10.5152/dir.2018.17467

    Article  PubMed  PubMed Central  Google Scholar 

  30. Joensuu H, Wardelmann E, Sihto H, Eriksson M, Sundby Hall K, Reichardt A, Hartmann JT, Pink D, Cameron S, Hohenberger P, Al-Batran SE, Schlemmer M, Bauer S, Nilsson B, Kallio R, Junnila J, Vehtari A, Reichardt P (2017) Effect of KIT and PDGFRA mutations on survival in patients with gastrointestinal stromal tumors treated with adjuvant imatinib: an exploratory analysis of a randomized clinical trial. JAMA Oncol 3:602–609. https://doi.org/10.1001/jamaoncol.2016.5751

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

No funding was received to assist with the preparation of this manuscript.

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Author notes

  1. Qiuxia Feng and Bo Tang contribute equally to the article.

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210009, Jiangsu Province, China

    Qiuxia Feng, Bo Tang, Yudong Zhang & Xisheng Liu

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  1. Qiuxia Feng
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  2. Bo Tang
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Contributions

We confirm that all the four listed authors have participated actively in the study and the authors' contributions are as follows: conceptualization: XL; material preparation: BT; methodology: YZ; data collection and analysis: BT; QF; writing—original draft preparation: QF and BT; writing—review and editing: QF; study supervision and guarantors: XL. QF and BT contributed equally to the article. All authors read and approved the final manuscript.

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Correspondence to Xisheng Liu.

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The authors have no relevant financial or non-financial interests to disclose.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee of the First Affiliated Hospital with Nanjing Medical University and with the 1964 Helsinki Declaration and its later amendments.

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Feng, Q., Tang, B., Zhang, Y. et al. Prediction of the Ki-67 expression level and prognosis of gastrointestinal stromal tumors based on CT radiomics nomogram. Int J CARS 17, 1167–1175 (2022). https://doi.org/10.1007/s11548-022-02575-6

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  • DOI: https://doi.org/10.1007/s11548-022-02575-6

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