Advertisement

European Radiology

, Volume 29, Issue 2, pp 556–565 | Cite as

Prognostic model based on magnetic resonance imaging, whole-tumour apparent diffusion coefficient values and HPV genotyping for stage IB-IV cervical cancer patients following chemoradiotherapy

  • Gigin Lin
  • Lan-Yan Yang
  • Yu-Chun Lin
  • Yu-Ting Huang
  • Feng-Yuan Liu
  • Chun-Chieh Wang
  • Hsin-Ying Lu
  • Hsin-Ju Chiang
  • Yu-Ruei Chen
  • Ren-Chin Wu
  • Koon-Kwan Ng
  • Ji-Hong Hong
  • Tzu-Chen Yen
  • Chyong-Huey LaiEmail author
Oncology

Abstract

Objectives

To develop and validate a prognostic model of integrating whole-tumour apparent diffusion coefficient (ADC) from pretreatment diffusion-weighted (DW) magnetic resonance (MR) imaging with human papillomavirus (HPV) genotyping in predicting the overall survival (OS) and disease-free survival (DFS) for women with stage IB–IV cervical cancer following concurrent chemoradiotherapy (CCRT).

Methods

We retrospectively analysed three prospectively collected cohorts comprising 300 patients with stage IB–IV cervical cancer treated with CCRT in 2007–2014 and filtered 134 female patients who underwent MR imaging at 3.0 T for final analysis (age, 24–92 years; median, 54 years). Univariate and multivariate Cox regression analyses were used to evaluate the whole-tumour ADC histogram parameters, HPV genotyping and relevant clinical variables in predicting OS and DFS. The dataset was randomly split into training (n = 88) and testing (n = 46) datasets for construction and independent bootstrap validation of the models.

Results

The median follow-up time for surviving patients was 69 months (range, 9–126 months). Non-squamous cell type, ADC10 <0.77 × 10-3 mm2/s, T3-4, M1 stage and high-risk HPV status were selected to generate a model, in which the OS and DFS for the low, intermediate and high-risk groups were significantly stratified (p < 0.0001). The prognostic model improved the prediction significantly compared with the International Federation of Gynaecology and Obstetrics (FIGO) stage for both the training and independent testing datasets (p < 0.0001).

Conclusions

The prognostic model based on integrated clinical and imaging data could be a useful clinical biomarker to predict OS and DFS in patients with stage IB–IV cervical cancer treated with CCRT.

Key points

• ADC 10 is the best prognostic factor among ADC parameters in cervical cancer treated with CCRT

• A novel prognostic model was built based on histology, ADC 10 , T and M stage and HPV status

• The prognostic model outperforms FIGO stage in the survival prediction

Keywords

Cervical cancer Human papillomavirus Chemoradiotherapy Diffusion magnetic resonance imaging Prognosis 

Abbreviations

CCRT

Concurrent chemoradiotherapy

CEA

Carcinoembryonic antigen

DFS

Disease-free survival

FIGO

International Federation of Gynaecology and Obstetrics

HPV

Human papillomavirus

HR

Hazard ratio

OS

Overall survival

SCC-Ag

Squamous cell carcinoma antigen

Notes

Funding

Supported by Chang Gung Medical Foundation grant CIRPG3E0022, CMRPG3F2241; National Science Council (Taiwan) MOST 104-2314-B-182A-095-MY3, NMRPD1E1051-3; Chang Gung IRB 95-1243B, 97-2366B, 102-0620A3 and 104-8300B. The authors acknowledge the assistance provided by the Cancer Center and the Clinical Trial Center, Chang Gung Memorial Hospital, Linkou, Taiwan, which was founded by the Ministry of Health and Welfare of Taiwan MOHW106-TDU-B-212-113005.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Chyong-Huey Lai.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Lan-Yan Yang, PhD. kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• prospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2018_5651_MOESM1_ESM.doc (234 kb)
ESM 1 (DOC 234 kb)

References

  1. 1.
    American Cancer Society: cancer facts and figures (2018) American Cancer Society. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2018/cancer-facts-and-figures-2018.pdf. Accessed 27 June 2018
  2. 2.
    NCCN Clinical Practice Guidelines in Oncology: Cervical Cancer. National Comprehensive Cancer Network Web site. https://www.nccn.org/professionals/physician_gls/pdf/cervical.pdf. Accessed 27 June 2018
  3. 3.
    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 266:717–740CrossRefGoogle Scholar
  4. 4.
    Wang CC, Lai CH, Huang HJ et al (2010) Clinical effect of human papillomavirus genotypes in patients with cervical cancer undergoing primary radiotherapy. Int J Radiat Oncol Biol Phys 78:1111-1120Google Scholar
  5. 5.
    Kuang F, Yan Z, Wang J, Rao Z (2014) The value of diffusion-weighted MRI to evaluate the response to radiochemotherapy for cervical cancer. Magn Reson Imaging 32:342–349CrossRefGoogle Scholar
  6. 6.
    Nakamura K, Joja I, Kodama J, Hongo A, Hiramatsu Y (2012) Measurement of SUVmax plus ADCmin of the primary tumour is a predictor of prognosis in patients with cervical cancer. Eur J Nucl Med Mol Imaging 39:283–290CrossRefGoogle Scholar
  7. 7.
    Heo SH, Shin SS, Kim JW et al (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 14:616-625Google Scholar
  8. 8.
    McVeigh PZ, Syed AM, Milosevic M, Fyles A, Haider MA (2008) Diffusion-weighted MRI in cervical cancer. Eur Radiol 18:1058–1064CrossRefGoogle Scholar
  9. 9.
    Micco M, Vargas HA, Burger IA et al (2014) Combined pre-treatment MRI and 18F-FDG PET/CT parameters as prognostic biomarkers in patients with cervical cancer. Eur J Radiol 83:1169–1176CrossRefGoogle Scholar
  10. 10.
    Himoto Y, Fujimoto K, Kido A et al (2015) Pretreatment mean apparent diffusion coefficient is significantly correlated with event-free survival in patients with International Federation of Gynecology and Obstetrics stage Ib to IIIb cervical cancer. Int J Gynecol Cancer 25:1079–1085CrossRefGoogle Scholar
  11. 11.
    Liu Y, Bai R, Sun H, Liu H, Zhao X, Li Y (2009) Diffusion-weighted imaging in predicting and monitoring the response of uterine cervical cancer to combined chemoradiation. Clin Radiol 64:1067–1074CrossRefGoogle Scholar
  12. 12.
    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 37:187–193CrossRefGoogle Scholar
  13. 13.
    Gladwish A, Milosevic M, Fyles A et al (2016) Association of apparent diffusion coefficient with disease recurrence in patients with locally advanced cervical cancer treated with radical chemotherapy and radiation therapy. Radiology 279:158–166CrossRefGoogle Scholar
  14. 14.
    Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures. they are data. Radiology 278:563–577Google Scholar
  15. 15.
    Lai CH, Chao A, Wang CC, Huang HJ (2014) Human papillomavirus and management of cervical cancer: does genotype matter. Curr Obstet Gynecol Rep 3:136–142CrossRefGoogle Scholar
  16. 16.
    Kim BG (2013) Squamous cell carcinoma antigen in cervical cancer and beyond. J Gynecol Oncol 24:291–292CrossRefGoogle Scholar
  17. 17.
    Lin G, Ng KK, Chang CJ et al (2009) Myometrial invasion in endometrial cancer: diagnostic accuracy of diffusion-weighted 3.0-T MR imaging—initial experience. Radiology 250:784–792CrossRefGoogle Scholar
  18. 18.
    Cuschieri K, Brewster DH, Graham C et al (2014) Influence of HPV type on prognosis in patients diagnosed with invasive cervical cancer. Int J Cancer 135:2721–2726CrossRefGoogle Scholar
  19. 19.
    Lin G, Lai CH, Tsai SY et al (2017) (1)H MR spectroscopy in cervical carcinoma using external phase array body coil at 3.0 tesla: prediction of poor prognostic human papillomavirus genotypes. J Magn Reson Imaging 45:899–907CrossRefGoogle Scholar
  20. 20.
    Molinaro AM, Simon R, Pfeiffer RM (2005) Prediction error estimation: a comparison of resampling methods. Bioinformatics 21:3301–3307CrossRefGoogle Scholar
  21. 21.
    Nakamura K, Joja I, Nagasaka T et al (2012) The mean apparent diffusion coefficient value (ADCmean) on primary cervical cancer is a predictive marker for disease recurrence. Gynecol Oncol 127:478–483CrossRefGoogle Scholar
  22. 22.
    Jalaguier-Coudray A, Villard-Mahjoub R, Delouche A et al (2017) Value of dynamic contrast-enhanced and diffusion-weighted MR imaging in the detection of pathologic complete response in cervical cancer after neoadjuvant therapy: a retrospective observational study. Radiology.  https://doi.org/10.1148/radiol.2017161299:161299
  23. 23.
    Marur S, Li S, Cmelak AJ et al (2017) E1308: Phase II trial of induction chemotherapy followed by reduced-dose radiation and weekly cetuximab in patients with HPV-associated resectable squamous cell carcinoma of the oropharynx—ECOG-ACRIN Cancer Research Group. J Clin Oncol 35:490–497CrossRefGoogle Scholar
  24. 24.
    Liu Y, Ye Z, Sun H, Bai R (2015) Clinical application of diffusion-weighted magnetic resonance imaging in uterine cervical cancer. Int J Gynecol Cancer 25:1073–1078CrossRefGoogle Scholar
  25. 25.
    Kuang F, Ren J, Zhong Q, Liyuan F, Huan Y, Chen Z (2013) The value of apparent diffusion coefficient in the assessment of cervical cancer. Eur Radiol 23:1050–1058CrossRefGoogle Scholar
  26. 26.
    Lin Y, Li H, Chen Z et al (2015) Correlation of histogram analysis of apparent diffusion coefficient with uterine cervical pathologic finding. AJR Am J Roentgenol 204:1125–1131CrossRefGoogle Scholar
  27. 27.
    Payne GS, Schmidt M, Morgan VA et al (2010) Evaluation of magnetic resonance diffusion and spectroscopy measurements as predictive biomarkers in stage 1 cervical cancer. Gynecol Oncol 116:246–252CrossRefGoogle Scholar
  28. 28.
    Guan Y, Shi H, Chen Y et al (2016) Whole-lesion histogram analysis of apparent diffusion coefficient for the assessment of cervical cancer. J Comput Assist Tomogr 40:212–217CrossRefGoogle Scholar
  29. 29.
    Xue H, Ren C, Yang J et al (2014) Histogram analysis of apparent diffusion coefficient for the assessment of local aggressiveness of cervical cancer. Arch Gynecol Obstet 290:341–348CrossRefGoogle Scholar
  30. 30.
    Downey K, Riches SF, Morgan VA et al (2013) Relationship between imaging biomarkers of stage I cervical cancer and poor-prognosis histologic features: quantitative histogram analysis of diffusion-weighted MR images. AJR Am J Roentgenol 200:314–320CrossRefGoogle Scholar
  31. 31.
    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 24:1081–1088CrossRefGoogle Scholar
  32. 32.
    Olsen JR, Esthappan J, DeWees T et al (2013) Tumor volume and subvolume concordance between FDG-PET/CT and diffusion-weighted MRI for squamous cell carcinoma of the cervix. J Magn Reson Imaging 37:431–434CrossRefGoogle Scholar
  33. 33.
    Ho KC, Lin G, Wang JJ, Lai CH, Chang CJ, Yen TC (2009) Correlation of apparent diffusion coefficients measured by 3T diffusion-weighted MRI and SUV from FDG PET/CT in primary cervical cancer. Eur J Nucl Med Mol Imaging 36:200–208CrossRefGoogle Scholar
  34. 34.
    Park JJ, Kim CK, Park BK (2016) Prognostic value of diffusion-weighted magnetic resonance imaging and 18F-fluorodeoxyglucose-positron emission tomography/computed tomography after concurrent chemoradiotherapy in uterine cervical cancer. Radiother Oncol 120:507–511CrossRefGoogle Scholar
  35. 35.
    Ueno Y, Lisbona R, Tamada T, Alaref A, Sugimura K, Reinhold C (2017) Comparison of FDG PET metabolic tumour volume versus ADC histogram: prognostic value of tumour treatment response and survival in patients with locally advanced uterine cervical cancer. Br J Radiol 90:20170035CrossRefGoogle Scholar
  36. 36.
    Grech-Sollars M, Hales PW, Miyazaki K et al (2015) Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain. NMR Biomed 28:468–485CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Gigin Lin
    • 1
    • 2
    • 3
    • 4
  • Lan-Yan Yang
    • 3
    • 5
  • Yu-Chun Lin
    • 1
    • 2
  • Yu-Ting Huang
    • 1
    • 3
  • Feng-Yuan Liu
    • 3
    • 6
  • Chun-Chieh Wang
    • 2
    • 3
    • 7
  • Hsin-Ying Lu
    • 1
    • 2
    • 4
  • Hsin-Ju Chiang
    • 1
    • 2
    • 4
  • Yu-Ruei Chen
    • 1
  • Ren-Chin Wu
    • 3
    • 8
  • Koon-Kwan Ng
    • 1
    • 2
    • 3
  • Ji-Hong Hong
    • 2
    • 3
    • 7
  • Tzu-Chen Yen
    • 3
    • 6
  • Chyong-Huey Lai
    • 3
    • 5
    Email author
  1. 1.Department of Medical Imaging and InterventionChang Gung Memorial Hospital at LinkouTaoyuanTaiwan
  2. 2.Imaging Core Laboratory, Institute for Radiological ResearchChang Gung Memorial Hospital at Linkou and Chang Gung UniversityTaoyuanTaiwan
  3. 3.Department of Obstetrics and Gynecology and Gynecologic Cancer Research CenterChang Gung Memorial Hospital at Linkou and Chang Gung UniversityTaoyuanTaiwan
  4. 4.Clinical Metabolomics Core LaboratoryChang Gung Memorial Hospital at LinkouTaoyuanTaiwan
  5. 5.Clinical Trial CenterChang Gung Memorial Hospital at Linkou and Chang Gung UniversityTaoyuanTaiwan
  6. 6.Department of Nuclear Medicine and Center for Advanced Molecular Imaging and TranslationChang Gung Memorial Hospital and Chang Gung University, Linkou Medical CenterTaoyuanTaiwan
  7. 7.Department of Radiation OncologyChang Gung Memorial Hospital at Linkou and Chang Gung UniversityTaoyuanTaiwan
  8. 8.Department of PathologyChang Gung Memorial Hospital at Linkou and Chang Gung UniversityTaoyuanTaiwan

Personalised recommendations