Skip to main content

Advertisement

SpringerLink
Log in

MRI-derived radiomics analysis improves the noninvasive pretreatment identification of multimodality therapy candidates with early-stage cervical cancer

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To develop and validate a clinical-radiomics model that incorporates radiomics signatures and pretreatment clinicopathological parameters to identify multimodality therapy candidates among patients with early-stage cervical cancer.

Methods

Between January 2017 and February 2021, 235 patients with IB1-IIA1 cervical cancer who underwent radical hysterectomy were enrolled and divided into training (n = 194, training:validation = 8:2) and testing (n = 41) sets according to surgical time. The radiomics features of each patient were extracted from preoperative sagittal T2-weighted images. Significance testing, Pearson correlation analysis, and Least Absolute Shrinkage and Selection Operator were used to select radiomic features associated with multimodality therapy administration. A clinical-radiomics model incorporating radiomics signature, age, 2018 Federation International of Gynecology and Obstetrics (FIGO) stage, menopausal status, and preoperative biopsy histological type was developed to identify multimodality therapy candidates. A clinical model and a clinical-conventional radiological model were also constructed. A nomogram and decision curve analysis were developed to facilitate clinical application.

Results

The clinical-radiomics model showed good predictive performance, with an area under the curve, sensitivity, and specificity in the testing set of 0.885 (95% confidence interval: 0.781–0.989), 78.9%, and 81.8%, respectively. The AUC, sensitivity, and specificity of the clinical model and clinical-conventional radiological model were 0.751 (0.603–0.900), 63.2%, and 63.6%, 0.801 (0.661–0.942), 73.7%, and 68.2%, respectively. A decision curve analysis demonstrated that when the threshold probability was > 20%, the clinical-radiomics model or nomogram may be more advantageous than the treat all or treat-none strategy.

Conclusions

The clinical-radiomics model and nomogram can potentially identify multimodality therapy candidates in patients with early-stage cervical cancer.

Key Points

• Pretreatment identification of multimodality therapy candidates among patients with early-stage cervical cancer helped to select the optimal primary treatment and reduce severe complication risk and costs.

• The clinical-radiomics model achieved a better prediction performance compared with the clinical model and the clinical-conventional radiological model.

• An easy-to-use nomogram exhibited good performance for individual preoperative prediction.

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

Similar content being viewed by others

Abbreviations

AC:

Adenocarcinoma

ASC:

Adenosquamous carcinoma

AUC:

Area under the curve

CIs:

Confidence intervals

FIGO:

International Federation of Gynecology and Obstetrics

LASSO:

Least Absolute Shrinkage and Selection Operator

LNM:

Lymph node metastasis

LoG:

Laplacians of Gaussians

LVSI:

Lymphovascular space invasion

PMI:

Parametrial invasion

ROC:

Receiver-operating characteristic

SCC:

Squamous cell carcinoma

References

  1. Sung H, Ferlay J, Siegel RL et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. https://doi.org/10.3322/caac.21660

    Article  PubMed  Google Scholar 

  2. Sartori E, Tisi G, Chiudinelli F, La Face B, Franzini R, Pecorelli S (2007) Early stage cervical cancer: adjuvant treatment in negative lymph node cases. Gynecol Oncol 107:S170-174

    Article  Google Scholar 

  3. Doll K, Donnelly E, Helenowksi I et al (2011) Treatment of stage IB1 cervix cancer: comparison of radical hysterectomy and radiation. Gynecol Oncol 123:444

    Article  Google Scholar 

  4. Gemer O, Lavie O, Gdalevich M et al (2016) Evaluation of clinical and pathologic risk factors may reduce the rate of multimodality treatment of early cervical cancer. Am J Clin Oncol 39:37–42

    Article  CAS  Google Scholar 

  5. Shu T, Zhao D, Li B et al (2017) Prognostic evaluation of postoperative adjuvant therapy for operable cervical cancer: 10 years’ experience of National Cancer Center in China. Chin J Cancer Res 29:510–520

    Article  CAS  Google Scholar 

  6. Baalbergen A, Veenstra Y, Stalpers LL, Ansink AC (2010) Primary surgery versus primary radiation therapy with or without chemotherapy for early adenocarcinoma of the uterine cervix. Cochrane Database Syst Rev. https://doi.org/10.1002/14651858.CD006248.pub2:Cd006248

    Article  PubMed  Google Scholar 

  7. Rogers L, Siu SS, Luesley D, Bryant A, Dickinson HO (2012) Radiotherapy and chemoradiation after surgery for early cervical cancer. Cochrane Database Syst Rev 5 Cd007583

  8. Saleh M, Virarkar M, Javadi S, Elsherif SB, de Castro FS, Bhosale P (2020) Cervical cancer: 2018 Revised International Federation of Gynecology and Obstetrics Staging System and the Role of Imaging. AJR Am J Roentgenol 214:1182–1195

    Article  Google Scholar 

  9. Haldorsen IS, Lura N, Blaakær J, Fischerova D, Werner HMJ (2019) What Is the Role of Imaging at Primary Diagnostic Work-Up in Uterine Cervical Cancer? Curr Oncol Rep 21:77

    Article  Google Scholar 

  10. Cibula D, McCluggage WG (2019) Sentinel lymph node (SLN) concept in cervical cancer: current limitations and unanswered questions. Gynecol Oncol 152:202–207

    Article  CAS  Google Scholar 

  11. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  Google Scholar 

  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

    Article  Google Scholar 

  13. Balcacer P, Shergill A, Litkouhi B (2019) MRI of cervical cancer with a surgical perspective: staging, prognostic implications and pitfalls. Abdom Radiol (NY) 44:2557–2571

    Article  Google Scholar 

  14. Wu Q, Wang S, Chen X et al (2019) Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer. Radiother Oncol 138:141–148

    Article  Google Scholar 

  15. Xiao M, Ma F, Li Y et al (2020) Multiparametric MRI-based radiomics nomogram for predicting lymph node metastasis in early-stage cervical cancer. J Magn Reson Imaging 52:885–896

    Article  Google Scholar 

  16. Li Z, Li H, Wang S et al (2019) MR-based radiomics nomogram of cervical cancer in prediction of the lymph-vascular space invasion preoperatively. J Magn Reson Imaging 49:1420–1426

    Article  Google Scholar 

  17. Wu Q, Shi D, Dou S et al (2019) Radiomics analysis of multiparametric MRI evaluates the pathological features of cervical squamous cell carcinoma. J Magn Reson Imaging 49:1141–1148

    Article  Google Scholar 

  18. Wang T, Gao T, Guo H et al (2020) Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram. Eur Radiol 30:3585–3593

    Article  Google Scholar 

  19. National Comprehensive Cancer Network. NCCN clinical practice guidelines in oncology. Cervical Cancer (Version 1.2020). [cited 2020 Jan 14] Available from: https://www.nccn.org/professionals/physician_gls/pdf/cervical.pdf

  20. Wang M, Xia C, Huang L et al (2020) Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation. Lancet Digit Health 2:e506–e515

    Article  Google Scholar 

  21. Landoni F, Colombo A, Milani R, Placa F, Zanagnolo V, Mangioni C (2017) Randomized study between radical surgery and radiotherapy for the treatment of stage IB-IIA cervical cancer: 20-year update. J Gynecol Oncol 28 e34

  22. Landoni F, Maneo A, Colombo A et al (1997) Randomised study of radical surgery versus radiotherapy for stage Ib-IIa cervical cancer. Lancet 350:535–540

    Article  CAS  Google Scholar 

  23. Cibula D, Pötter R, Planchamp F et al (2018) The European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology guidelines for the management of patients with cervical cancer. Radiother Oncol 127:404–416

    Article  Google Scholar 

  24. Marth C, Landoni F, Mahner S, McCormack M, Gonzalez-Martin A, Colombo N (2017) Cervical cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 28:iv72-iv83

  25. Adam JA, van Diepen PR, Mom CH, Stoker J, van Eck-Smit BLF, Bipat S (2020) [(18)F]FDG-PET or PET/CT in the evaluation of pelvic and para-aortic lymph nodes in patients with locally advanced cervical cancer: a systematic review of the literature. Gynecol Oncol 159:588–596

    Article  CAS  Google Scholar 

  26. Xiao M, Yan B, Li Y, Lu J, Qiang J (2020) Diagnostic performance of MR imaging in evaluating prognostic factors in patients with cervical cancer: a meta-analysis. Eur Radiol 30:1405–1418

    Article  Google Scholar 

  27. Bourbonne V, Vallières M, Lucia F et al (2019) MRI-derived radiomics to guide post-operative management for high-risk prostate cancer. Front Oncol 9:807

    Article  Google Scholar 

  28. Bourbonne V, Fournier G, Vallières M et al (2020) External validation of an MRI-derived radiomics model to predict biochemical recurrence after surgery for high-risk prostate cancer. Cancers (Basel) 12:814

    Article  CAS  Google Scholar 

  29. Balleyguier C, Sala E, Da Cunha T et al (2011) Staging of uterine cervical cancer with MRI: guidelines of the European Society of Urogenital Radiology. Eur Radiol 21:1102–1110

    Article  Google Scholar 

  30. Dimopoulos JC, Petrow P, Tanderup K et al (2012) Recommendations from Gynaecological (GYN) GEC-ESTRO Working Group (IV): Basic principles and parameters for MR imaging within the frame of image based adaptive cervix cancer brachytherapy. Radiother Oncol 103:113–122

    Article  Google Scholar 

  31. Avanzo M, Stancanello J, El Naqa I (2017) Beyond imaging: the promise of radiomics. Phys Med 38:122–139

    Article  Google Scholar 

  32. Park SH, Lim H, Bae BK et al (2021) Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer. Cancer Imaging 21:19

    Article  Google Scholar 

  33. Vickers AJ, Cronin AM, Elkin EB, Gonen M (2008) Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak 8:53

    Article  Google Scholar 

  34. Vickers AJ, Van Calster B, Steyerberg EW (2016) Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. BMJ 352 i6

Download references

Acknowledgements

This work was supported by grants from the Natural Science Foundation of China (grant no. 81901829) and the Fundamental Research Funds for the Central Universities (grant no. 3332019032).

Funding

Natural Science Foundation of China, 81901829, Yong-Lan He, the Fundamental Research Funds for the Central Universities, 3332019032, Yong-Lan He.

Author information

Author notes

  1. Yuan Li and Jing Ren contributed equally to this work.

Authors and Affiliations

  1. Department of OB&GYN, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, People’s Republic of China

    Yuan Li, Jun-Jun Yang & Yang Xiang

  2. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, People’s Republic of China

    Jing Ren, Ya-Fei Qi, Xin Gao, Zheng-Yu Jin, Yong-Lan He & Hua-Dan Xue

  3. Beijing Infervision Technology Co., Ltd. 100000, Beijing, People’s Republic of China

    Ying Cao, Chen Xia, Wen Tang & Kuan Chen

  4. Department of Diagnostic Radiology, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong SAR

    Elaine Y. P. Lee

  5. Department of Pathology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, People’s Republic of China

    Bo Chen

  6. Department of Radiotherapy, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, People’s Republic of China

    Hui Guan

Authors
  1. Yuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun-Jun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chen Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Elaine Y. P. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bo Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hui Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ya-Fei Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Xin Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Wen Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Kuan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Zheng-Yu Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Yong-Lan He
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Yang Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Hua-Dan Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yong-Lan He or Yang Xiang.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Professor Yong-Lan He.

Conflict of interest

Co-author Ying Cao, Chen Xia, Wen Tang, and Kuan Chen are employees of Beijing Infervision Technology Co. The other authors have no conflicts of interest to disclose. The authors not employed by Infervision Technology Co. were in control of this study.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Diagnostic or prognostic study

Additional information

Publisher's note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Ren, J., Yang, JJ. et al. MRI-derived radiomics analysis improves the noninvasive pretreatment identification of multimodality therapy candidates with early-stage cervical cancer. Eur Radiol 32, 3985–3995 (2022). https://doi.org/10.1007/s00330-021-08463-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-021-08463-y

Keywords

Search

Navigation