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Whole-tumor texture model based on diffusion kurtosis imaging for assessing cervical cancer: a preliminary study

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To evaluate the diagnostic potential of diffusion kurtosis imaging (DKI) functional maps with whole-tumor texture analysis in differentiating cervical cancer (CC) subtype and grade.

Methods

Seventy-six patients with CC were enrolled. First-order texture features of the whole tumor were extracted from DKI and DWI functional maps, including apparent kurtosis coefficient averaged over all directions (MK), kurtosis along the axial direction (Ka), kurtosis along the radial direction (Kr), mean diffusivity (MD), fractional anisotropy (FA), and ADC maps, respectively. The Mann-Whitney U test and ROC curve were used to select the most representative texture features. Models based on each individual and combined functional maps were established using multivariate logistic regression analysis. Conventional parameters—the average values of ADC and DKI parameters derived from the conventional ROI method—were also evaluated.

Results

The combined model based on Ka, Kr, MD, and FA maps yielded the best diagnostic performance in discrimination of cervical squamous cell cancer (SCC) and cervical adenocarcinoma (CAC) with the highest AUC (0.932). Among individual functional map derived models, Kr map–derived model showed the best performance when differentiating tumor subtypes (AUC = 0.828). MK_90th percentile was useful for distinguishing high-grade and low-grade in SCC tumors with an AUC of 0.701. The average values of MD, FA, and ADC were significantly different between SCC and CAC, but no conventional parameters were useful for tumor grading.

Conclusions

The whole-tumor texture analysis applied to DKI functional maps can be used for differential diagnosis of cervical cancer subtypes and grading SCC.

Key Points

• The whole-tumor texture analysis applied to DKI functional maps allows accurate differential diagnosis of CC subtype and grade.

• The combined model derived from multiple functional maps performs significantly better than the single models when differentiating tumor subtypes.

• MK_90th percentile was useful for distinguishing poorly and well-/moderately differentiated SCC tumors with an AUC of 0.701.

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Abbreviations

ADC:

Apparent diffusion coefficient

CAC:

Cervical adenocarcinoma

CC:

Cervical cancer

CI:

Confidence interval

DKI:

Diffusion kurtosis imaging

FA:

Fractional anisotropy

FIGO:

International Federation of Gynecology and Obstetrics

ICC:

Inter-class correlation coefficients

IQR:

Interquartile range

Ka:

Kurtosis along the axial direction

Kr:

Kurtosis along the radial direction

MAD:

Mean absolute deviation

MD:

Mean diffusivity

MK:

Apparent kurtosis coefficient averaged over all directions

PCC:

Pearson correlation coefficients

rMAD:

Robust mean absolute deviation

RMS:

Root mean squared

ROI:

Region of interest

SCC:

Cervical squamous cell cancer

VOI:

Volume of interest

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Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China

    Qi Zhang, Xiaoduo Yu, Han Ouyang, Jieying Zhang, Shuang Chen & Xinming Zhao

  2. GE Healthcare, MR Research, Beijing, China

    Lizhi Xie

Authors
  1. Qi Zhang
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  2. Xiaoduo Yu
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  3. Han Ouyang
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  4. Jieying Zhang
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  7. Xinming Zhao
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Corresponding author

Correspondence to Xinming Zhao.

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Guarantor

The scientific guarantor of this publication is Xinming Zhao, PhD, Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.

Conflict of interest

One of the authors of this manuscript (Lizhi Xie) is an employee of GE Healthcare. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Lizhi Xie 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.

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• Prospective

• Diagnostic or prognostic study

• Performed at one institution

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Zhang, Q., Yu, X., Ouyang, H. et al. Whole-tumor texture model based on diffusion kurtosis imaging for assessing cervical cancer: a preliminary study. Eur Radiol 31, 5576–5585 (2021). https://doi.org/10.1007/s00330-020-07612-z

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  • DOI: https://doi.org/10.1007/s00330-020-07612-z

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