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Multisequence magnetic resonance imaging-based radiomics models for the prediction of microsatellite instability in endometrial cancer

  • Magnetic Resonance Imaging
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Purpose

To evaluate the performance of multisequence magnetic resonance imaging (MRI)-based radiomics models in the assessment of microsatellite instability (MSI) status in endometrial cancer (EC).

Materials and methods

This retrospective multicentre study included 338 EC patients with available MSI status and preoperative MRI scans, divided into training (37 MSI, 123 microsatellite stability [MSS]), internal validation (15 MSI, 52 MSS), and external validation cohorts (30 MSI, 81 MSS). Radiomics features were extracted from T2-weighted images, diffusion-weighted images, and contrast-enhanced T1-weighted images. The ComBat harmonisation method was applied to remove intrascanner variability. The Boruta wrapper algorithm was used for key feature selection. Three classification algorithms, logistic regression (LR), random forest (RF), and support vector machine (SVM), were applied to build the radiomics models. The area under the receiver operating characteristic curve (AUC) was calculated to compare the diagnostic performance of the models. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the models.

Results

Among the 1980 features, Boruta finally selected nine radiomics features. A higher MSI prediction performance was achieved after running the ComBat harmonisation method. The SVM algorithm had the best performance, with AUCs of 0.921, 0.903, and 0.937 in the training, internal validation, and external validation cohorts, respectively. The DCA results showed that the SVM algorithm achieved higher net benefits than the other classifiers over a threshold range of 0.581–0.783.

Conclusion

The multisequence MRI-based radiomics models showed promise in preoperatively predicting the MSI status in EC in this multicentre setting.

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Abbreviations

EC:

Endometrial cancer

MSI:

Microsatellite instability

MSS:

Microsatellite stability

MMR:

Mismatch repair

LR:

Logistic regression

RF:

Random forest

SVM:

Support vector machine

DCA:

Decision curve analysis

AUC:

Area under the receiver operating characteristic curve

ROI:

Region of interest

ICC:

Intraclass correlation coefficient

References

  1. Lu KH, Broaddus RR (2020) Endometrial cancer. N Engl J Med 383:2053–2064

    Article  CAS  PubMed  Google Scholar 

  2. Bell DW, Ellenson LH (2019) Molecular genetics of endometrial carcinoma. Annu Rev Pathol 14:339–367

    Article  CAS  PubMed  Google Scholar 

  3. Hamilton CA, Pothuri B, Arend RC et al (2021) Endometrial cancer: a society of gynecologic oncology evidence-based review and recommendations. Gynecol Oncol 160:817–826

    Article  PubMed  Google Scholar 

  4. Latham A, Srinivasan P, Kemel Y et al (2019) Microsatellite instability is associated with the presence of lynch syndrome pan-cancer. J Clin Oncol 37:286–295

    Article  CAS  PubMed  Google Scholar 

  5. Xiao JP, Wang JS, Zhao YY, Du J, Wang YZ (2022) Microsatellite instability as a marker of prognosis: a systematic review and meta-analysis of endometrioid endometrial cancer survival data. Arch Gynecol Obstet. https://doi.org/10.1007/s00404-022-06636-8

    Article  PubMed  Google Scholar 

  6. Evrard C, Alexandre J (2021) Predictive and prognostic value of microsatellite instability in gynecologic cancer (endometrial and ovarian). Cancers Basel 13(10):2434

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lu C, Guan J, Lu S et al (2021) DNA sensing in mismatch repair-deficient tumor cells is essential for anti-tumor immunity. Cancer Cell 39:96-108.e106

    Article  CAS  PubMed  Google Scholar 

  8. O’Malley DM, Bariani GM, Cassier PA et al (2022) Pembrolizumab in patients with microsatellite instability-high advanced endometrial cancer: results from the KEYNOTE-158 study. J Clin Oncol 40:752–761

    Article  CAS  PubMed  Google Scholar 

  9. Makker V, Colombo N, Casado Herráez A et al (2022) Lenvatinib plus Pembrolizumab for advanced endometrial cancer. N Engl J Med 386:437–448

    Article  CAS  PubMed  Google Scholar 

  10. Abu-Rustum NR, Yashar CM, Bradley K et al (2021) NCCN Guidelines® insights: uterine neoplasms, version 3.2021. J Natl Compr Canc Netw 19:888–895

    Article  PubMed  Google Scholar 

  11. Concin N, Matias-Guiu X, Vergote I et al (2021) ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma. Int J Gynecol Cancer 31:12–39

    Article  PubMed  Google Scholar 

  12. Visser NCM, Reijnen C, Massuger L, Nagtegaal ID, Bulten J, Pijnenborg JMA (2017) Accuracy of endometrial sampling in endometrial carcinoma: A systematic review and meta-analysis. Obstet Gynecol 130:803–813

    Article  PubMed  Google Scholar 

  13. Veeraraghavan H, Friedman CF, DeLair DF et al (2020) Machine learning-based prediction of microsatellite instability and high tumor mutation burden from contrast-enhanced computed tomography in endometrial cancers. Sci Rep 10:17769

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Mayerhoefer ME, Materka A, Langs G et al (2020) Introduction to Radiomics. J Nucl Med 61:488–495

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  PubMed  Google Scholar 

  16. Ueno Y, Forghani B, Forghani R et al (2017) Endometrial carcinoma: MR imaging-based texture model for preoperative risk stratification—a preliminary analysis. Radiology 284:748–757

    Article  PubMed  Google Scholar 

  17. Luo Y, Mei D, Gong J, Zuo M, Guo X (2020) Multiparametric MRI-based radiomics nomogram for predicting lymphovascular space invasion in endometrial carcinoma. J Magn Reson Imaging 52:1257–1262

    Article  PubMed  Google Scholar 

  18. Yan BC, Li Y, Ma FH et al (2021) Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study. Eur Radiol 31:411–422

    Article  CAS  PubMed  Google Scholar 

  19. De Smedt L, Lemahieu J, Palmans S et al (2015) Microsatellite instable vs stable colon carcinomas: analysis of tumour heterogeneity, inflammation and angiogenesis. Br J Cancer 113:500–509

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zhang W, Huang Z, Zhao J et al (2021) Development and validation of magnetic resonance imaging-based radiomics models for preoperative prediction of microsatellite instability in rectal cancer. Ann Transl Med 9:134

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Li Z, Dai H, Liu Y, Pan F, Yang Y, Zhang M (2021) Radiomics analysis of multi-sequence MR images for predicting microsatellite instability status preoperatively in rectal cancer. Front Oncol 11:697497

    Article  PubMed  PubMed Central  Google Scholar 

  22. Stelloo E, Jansen AML, Osse EM et al (2017) Practical guidance for mismatch repair-deficiency testing in endometrial cancer. Ann Oncol 28:96–102

    Article  CAS  PubMed  Google Scholar 

  23. Luchini C, Bibeau F, Ligtenberg MJL et al (2019) ESMO recommendations on microsatellite instability testing for immunotherapy in cancer, and its relationship with PD-1/PD-L1 expression and tumour mutational burden: a systematic review-based approach. Ann Oncol 30:1232–1243

    Article  CAS  PubMed  Google Scholar 

  24. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107

    Article  PubMed  PubMed Central  Google Scholar 

  25. Mali SA, Ibrahim A, Woodruff HC et al (2021) Making radiomics more reproducible across scanner and imaging protocol variations: A review of harmonization methods. J Pers Med 11:842

    Article  PubMed  PubMed Central  Google Scholar 

  26. Orlhac F, Lecler A, Savatovski J et al (2021) How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. Eur Radiol 31:2272–2280

    Article  PubMed  Google Scholar 

  27. Da-Ano R, Masson I, Lucia F et al (2020) Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies. Sci Rep 10:10248

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wadee R, Grayson W (2019) A potpourri of pathogenetic pathways in endometrial carcinoma with a focus on Lynch syndrome. Ann Diagn Pathol 39:92–104

    Article  PubMed  Google Scholar 

  29. Ren J, Yuan Y, Qi M, Tao X (2020) Machine learning-based CT texture analysis to predict HPV status in oropharyngeal squamous cell carcinoma: comparison of 2D and 3D segmentation. Eur Radiol 30:6858–6866

    Article  PubMed  Google Scholar 

  30. Shen C, Liu Z, Guan M et al (2017) 2D and 3D CT radiomics features prognostic performance comparison in non-small cell lung cancer. Transl Oncol 10:886–894

    Article  PubMed  PubMed Central  Google Scholar 

  31. Li ZC, Bai H, Sun Q et al (2018) Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: a multicentre study. Eur Radiol 28:3640–3650

    Article  PubMed  Google Scholar 

  32. Abbasian Ardakani A, Bureau NJ, Ciaccio EJ, Acharya UR (2021) Interpretation of radiomics features—a pictorial review. Comput Methods Programs Biomed 215:106609

    Article  PubMed  Google Scholar 

  33. Tian Q, Yan LF, Zhang X et al (2018) Radiomics strategy for glioma grading using texture features from multiparametric MRI. J Magn Reson Imaging 48:1518–1528

    Article  PubMed  Google Scholar 

  34. Daye D, Tabari A, Kim H et al (2021) Quantitative tumor heterogeneity MRI profiling improves machine learning-based prognostication in patients with metastatic colon cancer. Eur Radiol 31:5759–5767

    Article  PubMed  Google Scholar 

  35. Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W (2018) Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteomics 15:41–51

    CAS  PubMed  Google Scholar 

  36. Noble WS (2006) What is a support vector machine? Nat Biotechnol 24:1565–1567

    Article  CAS  PubMed  Google Scholar 

  37. Wang X, Wan Q, Chen H, Li Y, Li X (2020) Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods. Eur Radiol 30:4595–4605

    Article  PubMed  Google Scholar 

  38. Qian Z, Li Y, Wang Y et al (2019) Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers. Cancer Lett 451:128–135

    Article  CAS  PubMed  Google Scholar 

  39. Yin P, Mao N, Zhao C et al (2019) Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features. Eur Radiol 29:1841–1847

    Article  PubMed  Google Scholar 

  40. Mao B, Ma J, Duan S, Xia Y, Tao Y, Zhang L (2021) Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics. Eur Radiol 31:4576–4586

    Article  PubMed  Google Scholar 

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Author notes

  1. Xiao-Li Song and Hong-Jian Luo have contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China

    Xiao-Li Song & Jinliang Niu

  2. Department of Radiology, Peking University People’s Hospital, Beijing, China

    Xiao-Li Song, Hong-Jian Luo, Ping Yin, Ying Liu & Nan Hong

  3. Department of Radiology, The Third Affiliated Hospital of Zunyi Medical University, The First People’s Hospital of Zunyi, Zunyi, Guizhou Province, China

    Hong-Jian Luo

  4. Department of Pharmaceuticals Diagnosics, GE Healthcare, Beijing, China

    Jia-Liang Ren

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by XLS, HJL, JLR, PY, YL, JLN and NH. The first draft of the manuscript was written by XLS, HJL and JLR. The manuscript was revised by NH, JLN, XLS, HJL and JLR. And all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Nan Hong.

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This study was approved by the local ethic committee and written informed consent was waived due to the retrospective nature of the study.

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Song, XL., Luo, HJ., Ren, JL. et al. Multisequence magnetic resonance imaging-based radiomics models for the prediction of microsatellite instability in endometrial cancer. Radiol med 128, 242–251 (2023). https://doi.org/10.1007/s11547-023-01590-0

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  • DOI: https://doi.org/10.1007/s11547-023-01590-0

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