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



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).


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.


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).


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


Cervical cancer Human papillomavirus Chemoradiotherapy Diffusion magnetic resonance imaging Prognosis 



Concurrent chemoradiotherapy


Carcinoembryonic antigen


Disease-free survival


International Federation of Gynaecology and Obstetrics


Human papillomavirus


Hazard ratio


Overall survival


Squamous cell carcinoma antigen



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


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.


• prospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

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


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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

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