Skip to main content
SpringerLink
Log in

Magnetic resonance imaging-based texture analysis for the prediction of postoperative clinical outcome in uterine cervical cancer

  • Pelvis
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Objectives

Magnetic resonance imaging (MRI)-based texture analysis (MRTA) is a novel image analysis tool that offers objective information about the spatial arrangement of MRI signal intensity. We aimed to investigate the value of MRTA in predicting the postoperative clinical outcome of patients with uterine cervical cancer.

Methods

This retrospective study included 115 patients with surgically proven cervical cancer who underwent preoperative pelvic 3T-MRI, and MRTA was performed on T2-weighted images (T2), apparent diffusion coefficient (ADC) maps, and contrast-enhanced T1-weighted images (CE-T1). Filtration histogram-based texture analysis was used to generate six first-order statistical parameters [mean intensity, standard deviation (SD), mean of positive pixels (MPP), entropy, skewness, and kurtosis] at five spatial scaling factors (SSFs, 2–6 mm) as well as from unfiltered images. Cox proportional hazard models and time-dependent receiver operating characteristic analyses were used to evaluate the associations between parameters and recurrence-free survival (RFS).

Results

During a median follow-up of 36 months, tumor recurrence was found in 26 patients (22.6%). Multivariate analysis demonstrated that CE-T1 MPP and T2 kurtosis at SSF3–5, CE-T1 MPP at SSF6, and CE-T1 SD at unfiltered images were independent predictors of RFS (p < 0.05). Regarding the 2-year RFS for CE-T1 MPP and T2 kurtosis at SSF5, and CE-T1 MPP at SSF6, patients with > optimal cutoff values demonstrated significantly worse survival than those with ≤ optimal cutoff values (p < 0.05).

Conclusion

Preoperative MRTA may be useful for predicting postoperative outcome in patients with cervical cancer.

Graphic 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

Similar content being viewed by others

Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

CE-T1:

Contrast-enhanced fat-saturated T1-weighted

CI:

Confidence interval

DWI:

Diffusion-weighted imaging

FIGO:

International Federation of Gynecology and Obstetrics

FOV:

Field of view

HR:

Hazard ratio

MPP:

Mean of positive pixels

MRI:

Magnetic resonance imaging

MRTA:

MRI-based texture analysis

NSA:

Number of signals acquired

RFS:

Recurrence-free survival

ROC:

Receiver operating characteristics

ROI:

Region-of-interest

SCC:

Squamous cell carcinoma

SD:

Standard deviation

SENSE:

Sensitivity encoding

SSF:

Spatial scaling factor

TA:

Texture analysis

TE:

Echo time

TR:

Repetition time

References

  1. Torre LA, Siegel RL, Ward EM, Jemal A (2016) Global Cancer Incidence and Mortality Rates and Trends—An Update. Cancer Epidemiol Biomarkers Prev 25:16–27. https://doi.org/10.1158/1055-9965.EPI-15-0578

    Article  PubMed  Google Scholar 

  2. Sala E, Rockall AG, Freeman SJ, Mitchell DG, Reinhold C (2013) The added role of MR imaging in treatment stratification of patients with gynecologic malignancies: what the radiologist needs to know. Radiology 266:717–740. https://doi.org/10.1148/radiol.12120315

    Article  PubMed  Google Scholar 

  3. Wakefield JC, Downey K, Kyriazi S, deSouza NM (2013) New MR techniques in gynecologic cancer. Am J Roentgenol 200:249–260. https://doi.org/10.2214/AJR.12.8932

    Article  Google Scholar 

  4. Dagogo-Jack I, Shaw AT (2018) Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol 15:81–94. https://doi.org/10.1038/nrclinonc.2017.166

    Article  CAS  PubMed  Google Scholar 

  5. Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B, Ganeshan B, Miles KA, Cook GJ, Goh V (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3:573–589. https://doi.org/10.1007/s13244-012-0196-6

    Article  PubMed  PubMed Central  Google Scholar 

  6. Chen SW, Shen WC, Hsieh TC, Liang JA, Hung YC, Yeh LS, Chang WC, Lin WC, Yen KY, Kao CH (2018) Textural features of cervical cancers on FDG-PET/CT associate with survival and local relapse in patients treated with definitive chemoradiotherapy. Sci Rep 8:11859. https://doi.org/10.1038/s41598-018-30336-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Meng J, Liu S, Zhu L, Zhu L, Wang H, Xie L, Guan Y, He J, Yang X, Zhou Z (2018) Texture Analysis as Imaging Biomarker for recurrence in advanced cervical cancer treated with CCRT. Sci Rep 8:11399. https://doi.org/10.1038/s41598-018-29838-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Yoon A, Park JJ, Park BK, Lee YY, Paik ES, Choi CH, Kim TJ, Kim CK, Lee JW, Bae DS, Kim BG (2016) Long-term Outcomes of MRI Stage IIB Cervical Cancer. Int J Gynecol Cancer 26:1252–1257. https://doi.org/10.1097/IGC.0000000000000762

    Article  PubMed  Google Scholar 

  9. Park JY, Lee JW, Park BK, Lee YY, Choi CH, Kim TJ, Bae DS, Kim BG, Park JJ, Park SY, Kim CK (2014) Postoperative outcomes of MR-invisible stage IB1 cervical cancer. Am J Obstet Gynecol 211:168 e161–167. https://doi.org/10.1016/j.ajog.2014.02.032

    Article  Google Scholar 

  10. Heo SH, Shin SS, Kim JW, Lim HS, Jeong YY, Kang WD, Kim SM, Kang HK (2013) Pre-treatment diffusion-weighted MR imaging for predicting tumor recurrence in uterine cervical cancer treated with concurrent chemoradiation: value of histogram analysis of apparent diffusion coefficients. Korean J Radiol 14:616–625. https://doi.org/10.3348/kjr.2013.14.4.616

    Article  PubMed  PubMed Central  Google Scholar 

  11. Lund KV, Simonsen TG, Kristensen GB, Rofstad EK (2017) Pretreatment late-phase DCE-MRI predicts outcome in locally advanced cervix cancer. Acta Oncol 56:675–681. https://doi.org/10.1080/0284186X.2017.1294762

    Article  PubMed  Google Scholar 

  12. Fang J, Zhang B, Wang S, Jin Y, Wang F, Ding Y, Chen Q, Chen L, Li Y, Li M, Chen Z, Liu L, Liu Z, Tian J, Zhang S (2020) Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer. Theranostics 10:2284–2292. https://doi.org/10.7150/thno.37429

    Article  PubMed  PubMed Central  Google Scholar 

  13. Pecorelli S (2009) Revised FIGO staging for carcinoma of the vulva, cervix, and endometrium. Int J Gynaecol Obstet 105:103–104

    Article  PubMed  Google Scholar 

  14. Ganeshan B, Miles KA (2013) Quantifying tumour heterogeneity with CT. Cancer Imaging 13:140–149. https://doi.org/10.1102/1470-7330.2013.0015

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wang M, Perucho JAU, Tse KY, Chu MMY, Ip P, Lee EYP (2020) MRI texture features differentiate clinicopathological characteristics of cervical carcinoma. Eur Radiol 30:5384–5391. https://doi.org/10.1007/s00330-020-06913-7

    Article  PubMed  Google Scholar 

  16. Ho KC, Fang YH, Chung HW, Yen TC, Ho TY, Chou HH, Hong JH, Huang YT, Wang CC, Lai CH (2016) A preliminary investigation into textural features of intratumoral metabolic heterogeneity in (18)F-FDG PET for overall survival prognosis in patients with bulky cervical cancer treated with definitive concurrent chemoradiotherapy. Am J Nucl Med Mol Imaging 6:166–175

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Prescott JW, Zhang D, Wang JZ, Mayr NA, Yuh WT, Saltz J, Gurcan M (2010) Temporal analysis of tumor heterogeneity and volume for cervical cancer treatment outcome prediction: preliminary evaluation. J Digit Imaging 23:342–357. https://doi.org/10.1007/s10278-009-9179-7

    Article  PubMed  Google Scholar 

  18. Lee J, Kim CK, Park SY (2020) Histogram analysis of apparent diffusion coefficients for predicting pelvic lymph node metastasis in patients with uterine cervical cancer. MAGMA 33:283–292. https://doi.org/10.1007/s10334-019-00777-9

    Article  CAS  PubMed  Google Scholar 

  19. Wormald BW, Doran SJ, Ind TE, D'Arcy J, Petts J, deSouza NM (2020) Radiomic features of cervical cancer on T2-and diffusion-weighted MRI: Prognostic value in low-volume tumors suitable for trachelectomy. Gynecol Oncol 156:107–114. https://doi.org/10.1016/j.ygyno.2019.10.010

    Article  PubMed  PubMed Central  Google Scholar 

  20. Miles KA, Ganeshan B, Hayball MP (2013) CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 13:400–406. https://doi.org/10.1102/1470-7330.2013.9045

    Article  PubMed  PubMed Central  Google Scholar 

  21. Ciolina M, Vinci V, Villani L, Gigli S, Saldari M, Panici PB, Perniola G, Laghi A, Catalano C, Manganaro L (2019) Texture analysis versus conventional MRI prognostic factors in predicting tumor response to neoadjuvant chemotherapy in patients with locally advanced cancer of the uterine cervix. Radiol Med 124:955–964. https://doi.org/10.1007/s11547-019-01055-3

    Article  PubMed  Google Scholar 

  22. Ytre-Hauge S, Dybvik JA, Lundervold A, Salvesen OO, Krakstad C, Fasmer KE, Werner HM, Ganeshan B, Hoivik E, Bjorge L, Trovik J, Haldorsen IS (2018) Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer. J Magn Reson Imaging 48:1637–1647. https://doi.org/10.1002/jmri.26184

    Article  PubMed  Google Scholar 

  23. Park JJ, Kim CK, Park SY, Park BK, Kim B (2014) Value of diffusion-weighted imaging in predicting parametrial invasion in stage IA2–IIA cervical cancer. Eur Radiol 24:1081–1088. https://doi.org/10.1007/s00330-014-3109-x

    Article  PubMed  Google Scholar 

  24. Kim HS, Kim CK, Park BK, Huh SJ, Kim B (2013) Evaluation of therapeutic response to concurrent chemoradiotherapy in patients with cervical cancer using diffusion-weighted MR imaging. J Magn Reson Imaging 37:187–193. https://doi.org/10.1002/jmri.23804

    Article  PubMed  Google Scholar 

  25. Xue H, Ren C, Yang J, Sun Z, Li S, Jin Z, Shen K, Zhou W (2014) Histogram analysis of apparent diffusion coefficient for the assessment of local aggressiveness of cervical cancer. Arch Gynecol Obstet 290:341–348. https://doi.org/10.1007/s00404-014-3221-9

    Article  PubMed  Google Scholar 

  26. Song M, Lin J, Song F, Wu D, Qian Z (2020) The value of MR-based radiomics in identifying residual disease in patients with carcinoma in situ after cervical conization. Sci Rep 10:19890. https://doi.org/10.1038/s41598-020-76853-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Gourtsoyianni S, Doumou G, Prezzi D, Taylor B, Stirling JJ, Taylor NJ, Siddique M, Cook GJR, Glynne-Jones R, Goh V (2017) Primary Rectal Cancer: Repeatability of Global and Local-Regional MR Imaging Texture Features. Radiology 284:552–561. https://doi.org/10.1148/radiol.2017161375

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank Sook Young Woo, PhD of Statistics and Data Center, Samsung Medical Center, for help with statistical assistance and thank Editage (www.editage.co.kr) for English language editing.

Author information

Authors and Affiliations

  1. Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea

    Ka Eun Kim & Chan Kyo Kim

  2. Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea

    Chan Kyo Kim

  3. Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea

    Chan Kyo Kim

Authors
  1. Ka Eun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chan Kyo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chan Kyo Kim.

Ethics declarations

Conflict of interest

The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 36 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, K.E., Kim, C.K. Magnetic resonance imaging-based texture analysis for the prediction of postoperative clinical outcome in uterine cervical cancer. Abdom Radiol 47, 352–361 (2022). https://doi.org/10.1007/s00261-021-03288-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-021-03288-1

Keywords

Search

Navigation