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Nomogram development and validation to predict hepatocellular carcinoma tumor behavior by preoperative gadoxetic acid-enhanced MRI

  • Hepatobiliary-Pancreas
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Abstract

Objectives

Pretreatment evaluation of tumor biology and microenvironment is important to predict prognosis and plan treatment. We aimed to develop nomograms based on gadoxetic acid-enhanced MRI to predict microvascular invasion (MVI), tumor differentiation, and immunoscore.

Methods

This retrospective study included 273 patients with HCC who underwent preoperative gadoxetic acid-enhanced MRI. Patients were assigned to two groups: training (N = 191) and validation (N = 82). Univariable and multivariable logistic regression analyses were performed to investigate clinical variables and MRI features’ associations with MVI, tumor differentiation, and immunoscore. Nomograms were developed based on features associated with these three histopathological features in the training cohort, then validated, and evaluated.

Results

Predictors of MVI included tumor size, rim enhancement, capsule, percent decrease in T1 images (T1D%), standard deviation of apparent diffusion coefficient, and alanine aminotransferase levels, while capsule, peritumoral enhancement, mean relaxation time on the hepatobiliary phase (T1E), and alpha-fetoprotein levels predicted tumor differentiation. Predictors of immunoscore included the radiologic score constructed by tumor number, intratumoral vessel, margin, capsule, rim enhancement, T1D%, relaxation time on plain scan (T1P), and alpha-fetoprotein and alanine aminotransferase levels. Three nomograms achieved good concordance indexes in predicting MVI (0.754, 0.746), tumor differentiation (0.758, 0.699), and immunoscore (0.737, 0.726) in the training and validation cohorts, respectively.

Conclusion

MRI-based nomograms effectively predict tumor behaviors in HCC and may assist clinicians in prognosis prediction and pretreatment decisions.

Key Points

• This study developed and validated three nomograms based on gadoxetic acid-enhanced MRI to predict MVI, tumor differentiation, and immunoscore in patients with HCC.

• The pretreatment prediction of tumor microenvironment may be useful to guide accurate prognosis and planning of surgical and immunological therapies for individual patients with HCC.

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Abbreviations

ADC:

Apparent diffusion coefficient

CI:

Confidence interval

CT:

Center of the tumor

HBP:

Hepatobiliary phase

HCC:

Hepatocellular carcinoma

IM:

Invasive margin

MVI:

Microvascular invasion

T1D%:

Percent decrease in T1 images

T1E :

Relaxation time on the hepatobiliary phase

T1P :

Relaxation time on plain scan

TIL:

Tumor-infiltrating lymphocyte

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Funding

This study has received funding from the National Natural Science Foundation of China (No. 81971684, 81771908, 81801703) and the Guangdong Natural Science Foundation of Guangdong Province (No. 2018A030310282).

Author information

Author notes

  1. Mimi Tang and Qian Zhou contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China

    Mimi Tang, Mengqi Huang & Shi-Ting Feng

  2. Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China

    Qian Zhou & Sui Peng

  3. Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China

    Kaiyu Sun

  4. GE Healthcare, Shanghai, China

    Tingfan Wu & Xin Li

  5. Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China

    Bing Liao & Lili Chen

  6. Department of Liver Surgery, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China

    Junbin Liao

  7. Department of Gastroenterology and Hepatology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China

    Sui Peng

  8. Precision Medicine Institute, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China

    Sui Peng

  9. Division of Interventional Ultrasound, Department of Medical Ultrasonics, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China

    Shuling Chen

Authors
  1. Mimi Tang
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  2. Qian Zhou
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  3. Mengqi Huang
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  4. Kaiyu Sun
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  7. Bing Liao
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  8. Lili Chen
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  12. Shi-Ting Feng
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Corresponding authors

Correspondence to Shuling Chen or Shi-Ting Feng.

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Guarantor

The scientific guarantor of this publication is Shi-Ting Feng.

Conflict of interest

Two of the authors (Tingfan Wu, Xin Li) are employees of GE Healthcare. The remaining 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

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Two hundred and seven study subjects have been previously reported in “Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging” which has been published in European Radiology. This study is different from the previous one either in the objective or in the method.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Tang, M., Zhou, Q., Huang, M. et al. Nomogram development and validation to predict hepatocellular carcinoma tumor behavior by preoperative gadoxetic acid-enhanced MRI. Eur Radiol 31, 8615–8627 (2021). https://doi.org/10.1007/s00330-021-07941-7

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  • DOI: https://doi.org/10.1007/s00330-021-07941-7

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