Skip to main content
SpringerLink
Log in

Multiparametric MRI radiomics improves preoperative diagnostic performance for local staging in patients with endometrial cancer

  • Kidneys, Ureters, Bladder, Retroperitoneum
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To determine whether multiparametric magnetic resonance imaging (MRI) radiomics-based machine learning methods can improve preoperative local staging in patients with endometrial cancer (EC).

Methods

Data of patients with histologically confirmed EC who underwent preoperative MRI were retrospectively analyzed and divided into a training or test set. Radiomic features extracted from multiparametric MR images were used to train and test the prediction of deep myometrial invasion (DMI) and cervical stromal invasion (CSI). Two radiologists assessed the presence of DMI and CSI on conventional MR images. A combined model incorporating a radiomic signature and conventional MR images was constructed and presented as a nomogram. Performance of the predictive models was assessed using the area under curve (AUC) in the receiver operating curve analysis and pairwise comparison using DeLong’s test with Bonferroni correction.

Results

This study included 198 women (training set = 138, test set = 60). Conventional MRI achieved AUCs of 0.837 and 0.799 for detecting DMI and 0.825 and 0.858 for detecting CSI in the training and test sets, respectively. The nomogram achieved AUCs of 0.928 and 0.869 for detecting DMI and 0.913 and 0.937 for detecting CSI in the training and test sets, respectively. The ability of the nomogram to detect DMI and CSI in the two sets was superior to that of conventional MRI (adjusted p < 0.05), except for the ability to detect CSI in the test set (adjusted p > 0.05).

Conclusion

A nomogram incorporating radiomics signature into conventional MRI improved the efficacy of preoperative local staging of EC.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. 1 Siegel RL, Miller KD, Jemal A (2020) Cancer statistics, 2020. CA Cancer J Clin 70:7-30. https://doi.org/10.3322/caac.21590

    Article  Google Scholar 

  2. 2 Gu B, Shang X, Yan M et al (2021) Variations in incidence and mortality rates of endometrial cancer at the global, regional, and national levels, 1990-2019. Gynecol Oncol 161:573-580. https://doi.org/10.1016/j.ygyno.2021.01.036

    Article  PubMed  Google Scholar 

  3. 3 Concin N, Creutzberg CL, Vergote I et al (2021) ESGO/ESTRO/ESP Guidelines for the management of patients with endometrial carcinoma. Virchows Arch 478:153-190. https://doi.org/10.1007/s00428-020-03007-z

    Article  PubMed  Google Scholar 

  4. 4 Luomaranta A, Leminen A, Loukovaara M (2015) Magnetic resonance imaging in the assessment of high-risk features of endometrial carcinoma: a meta-analysis. Int J Gynecol Cancer 25:837-842. https://doi.org/10.1097/IGC.0000000000000194

    Article  PubMed  Google Scholar 

  5. 5 Solmaz U, Mat E, Dereli M et al (2015) Lymphovascular space invasion and cervical stromal invasion are independent risk factors for nodal metastasis in endometrioid endometrial cancer. Aust N Z J Obstet Gynaecol 55:81-86. https://doi.org/10.1111/ajo.12321

    Article  PubMed  Google Scholar 

  6. 6 Sorosky JI (2012) Endometrial cancer. Obstet Gynecol 120:383-397. https://doi.org/10.1097/AOG.0b013e3182605bf1

    Article  PubMed  Google Scholar 

  7. 7 Carneiro MM, Lamaita RM, Ferreira MC, Silva-Filho AL (2016) Fertility-preservation in endometrial cancer: is it safe? Review of the literature. JBRA Assist Reprod 20:232-239. https://doi.org/10.5935/1518-0557.20160045

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8 Woo S, Kim SY, Cho JY, Kim SH (2017) Assessment of deep myometrial invasion of endometrial cancer on MRI: added value of second-opinion interpretations by radiologists subspecialized in gynaecologic oncology. Eur Radiol 27:1877-1882. https://doi.org/10.1007/s00330-016-4582-1

    Article  PubMed  Google Scholar 

  9. 9 Bi Q, Chen Y, Wu K et al (2020) The diagnostic value of MRI for preoperative staging in patients with endometrial cancer: a meta-analysis. Acad Radiol 27:960-968. https://doi.org/10.1016/j.acra.2019.09.018

    Article  PubMed  Google Scholar 

  10. 10 Utsunomiya D, Notsute S, Hayashida Y et al (2004) Endometrial carcinoma in adenomyosis: assessment of myometrial invasion on T2-weighted spin-echo and gadolinium-enhanced T1-weighted images. AJR Am J Roentgenol 182:399-404. https://doi.org/10.2214/ajr.182.2.1820399

    Article  PubMed  Google Scholar 

  11. 11 Lin G, Huang YT, Chao A et al (2017) Endometrial cancer with cervical stromal invasion: diagnostic accuracy of diffusion-weighted and dynamic contrast enhanced MR imaging at 3T. Eur Radiol 27:1867-1876. https://doi.org/10.1007/s00330-016-4583-0

    Article  PubMed  Google Scholar 

  12. 12 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. https://doi.org/10.1038/nrclinonc.2017.141

    Article  PubMed  Google Scholar 

  13. 13 Wang Y, Bi Q, Deng Y et al (2023) Development and validation of an MRI-based radiomics nomogram for assessing deep myometrial invasion in early stage endometrial adenocarcinoma. Acad Radiol 30:668-679. https://doi.org/10.1016/j.acra.2022.05.017

    Article  PubMed  Google Scholar 

  14. 14 Rodriguez-Ortega A, Alegre A, Lago V et al (2021) Machine learning-based integration of prognostic magnetic resonance imaging biomarkers for myometrial invasion stratification in endometrial cancer. J Magn Reson Imaging 54:987-995. https://doi.org/10.1002/jmri.27625

    Article  PubMed  Google Scholar 

  15. 15 Yan BC, Ma XL, Li Y, Duan SF, Zhang GF, Qiang JW (2021) MRI-based radiomics nomogram for selecting ovarian preservation treatment in patients with early-stage endometrial cancer. Front Oncol 11:730281. https://doi.org/10.3389/fonc.2021.730281

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16 Zhao M, Wen F, Shi J et al (2022) MRI-based radiomics nomogram for the preoperative prediction of deep myometrial invasion of FIGO stage I endometrial carcinoma. Med Phys 49:6505-6516. https://doi.org/10.1002/mp.15835

    Article  PubMed  CAS  Google Scholar 

  17. 17 Stanzione A, Cuocolo R, Del Grosso R et al (2021) Deep myometrial infiltration of endometrial cancer on MRI: A radiomics-powered machine learning pilot study. Acad Radiol 28:737-744. https://doi.org/10.1016/j.acra.2020.02.028

    Article  PubMed  Google Scholar 

  18. 18 Chen J, Gu H, Fan W et al (2021) MRI-based radiomic model for preoperative risk stratification in stage I endometrial cancer. J Cancer 12:726-734. https://doi.org/10.7150/jca.50872

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19 Yan BC, Li Y, Ma FH et al (2020) Preoperative assessment for high-risk endometrial cancer by developing an MRI- and clinical-based radiomics nomogram: a multicenter study. J Magn Reson Imaging 52:1872-1882. https://doi.org/10.1002/jmri.27289

    Article  PubMed  Google Scholar 

  20. 20 Song Y, Zhang J, Zhang YD et al (2020) FeAture Explorer (FAE): A tool for developing and comparing radiomics models. PLoS One 15:e0237587. https://doi.org/10.1371/journal.pone.0237587

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. 21 Zwanenburg A, Vallières M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328-338. https://doi.org/10.1148/radiol.2020191145

    Article  PubMed  Google Scholar 

  22. 22 Li X, Dessi M, Marcus D et al (2023) Prediction of deep myometrial infiltration, clinical risk category, histological type, and lymphovascular space invasion in women with endometrial cancer based on clinical and T2-weighted MRI radiomic features. Cancers 15. https://doi.org/10.3390/cancers15082209

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23 Ma X, Shen M, He Y et al (2021) The role of volumetric ADC histogram analysis in preoperatively evaluating the tumour subtype and grade of endometrial cancer. Eur J Radiol 140:109745. https://doi.org/10.1016/j.ejrad.2021.109745

    Article  PubMed  Google Scholar 

  24. 24 Otani S, Himoto Y, Nishio M et al (2022) Radiomic machine learning for pretreatment assessment of prognostic risk factors for endometrial cancer and its effects on radiologists' decisions of deep myometrial invasion. Magn Reson Imaging 85:161-167. https://doi.org/10.1016/j.mri.2021.10.024

    Article  PubMed  CAS  Google Scholar 

  25. 25 Yamada I, Miyasaka N, Kobayashi D et al (2019) Endometrial carcinoma: texture analysis of apparent diffusion coefficient maps and its correlation with histopathologic findings and prognosis. Radiology: Imaging Cancer 1. https://doi.org/10.1148/rycan.2019190054

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26 Yang J, Cao Y, Zhou F, Li C, Lv J, Li P (2023) Combined deep-learning MRI-based radiomic models for preoperative risk classification of endometrial endometrioid adenocarcinoma. Frontiers in Oncology 13. https://doi.org/10.3389/fonc.2023.1231497

    Article  PubMed  PubMed Central  Google Scholar 

  27. 27 Chen X, Wang Y, Shen M et al (2020) Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution. Eur Radiol 30:4985-4994. https://doi.org/10.1007/s00330-020-06870-1

    Article  PubMed  Google Scholar 

  28. 28 Lefebvre TL, Ueno Y, Dohan A et al (2022) Development and validation of multiparametric MRI-based radiomics models for preoperative risk stratification of endometrial cancer. Radiology 305:375-386. https://doi.org/10.1148/radiol.212873

    Article  PubMed  Google Scholar 

  29. 29 Fiset S, Welch ML, Weiss J et al (2019) Repeatability and reproducibility of MRI-based radiomic features in cervical cancer. Radiother Oncol 135:107-114. https://doi.org/10.1016/j.radonc.2019.03.001

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank Elsevier Language Editing Services for English language editing.

Funding

This study has received funding by Joint Funds for the innovation of science and technology, Fujian province (Grant Number: 2020Y9146) and Fujian provincial natural science foundation (Grant Number: 2023J011215).

Author information

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, People’s Republic of China

    Ruqi Fang, Na Lin & Dairong Cao

  2. Department of Radiology, Fujian Provincial Maternity and Children’s Hospital, Fuzhou, 350001, Fujian, People’s Republic of China

    Ruqi Fang, Shuping Weng, Kaili Liu & Xiaping Chen

  3. Department of Radiology, Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, Fujian, People’s Republic of China

    Dairong Cao

  4. Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, Fujian, People’s Republic of China

    Dairong Cao

  5. Department of Radiology, Fujian Provincial Obstetrics and Gynecology Hospital, Fuzhou, 350011, Fujian, People’s Republic of China

    Ruqi Fang, Shuping Weng, Kaili Liu & Xiaping Chen

Authors
  1. Ruqi Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Na Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuping Weng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kaili Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaping Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dairong Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors (1) made substantial contributions to the study concept or the data analysis or interpretation; (2) drafted the manuscript or revised it critically for important intellectual content; (3) approved the final version of the manuscript to be published; and (4) agreed to be accountable for all aspects of the work.

Corresponding author

Correspondence to Dairong Cao.

Ethics declarations

Competing interest

The authors have no relevant financial or nonfinancial interests to disclose.

Ethical approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Biomedical Research Ethics Committee of Fujian Provincial Maternity and Children’s Hospital (Ethics Approval Number: 2021KLR614).

Consent to participate and publish

The requirement of informed patient consent was waived in the present case report in accordance with the opt-out method used at our institution.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fang, R., Lin, N., Weng, S. et al. Multiparametric MRI radiomics improves preoperative diagnostic performance for local staging in patients with endometrial cancer. Abdom Radiol 49, 875–887 (2024). https://doi.org/10.1007/s00261-023-04149-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-023-04149-9

Keywords

Search

Navigation