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A predictive model integrating deep and radiomics features based on gadobenate dimeglumine-enhanced MRI for postoperative early recurrence of hepatocellular carcinoma

  • Abdominal Radiology
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Purpose

Hepatocellular carcinoma (HCC) is the most common liver cancer worldwide, and early recurrence of HCC after curative hepatic resection is indicative of poor prognoses. We aim to develop a predictive model for postoperative early recurrence of HCC based on deep and radiomics features from multi-phasic magnetic resonance imaging (MRI).

Materials and methods

A total of 472 HCC patients were included and divided into the training (n = 378) and validation (n = 94) cohorts in the retrospective study. We separately extracted radiomics features and deep features from eight phases of gadoxetic acid-enhanced MRI and utilized the least absolute shrinkage and selection operator logistic regression algorithm for feature selection and model construction. We integrated the selected two types of features into a combined model and established a radiomics model as well as a deep learning (DL) model for comparison.

Results

In the training and validation cohorts, the combined model demonstrated better performance for stratifying patients at high risk of early recurrence (AUC of 0.911 and 0.840, accuracy of 0.779 and 0.777, sensitivity of 0.927 and 0.769, specificity 0.720 and 0.779) than the radiomics model (AUC of 0.740 and 0.780) and the DL model (AUC of 0.887 and 0.813).

Conclusion

The combined model integrating deep and radiomics features from multi-phasic MRI is efficient for noninvasively stratifying patients at high risk of early HCC recurrence after resection.

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Abbreviations

AUC:

Area under the receiver operating characteristic curve

BCLC:

Barcelona Clinic Liver Cancer

CNN:

Convolutional neural network

DCA:

Decision curve analysis

DCNN:

Deep convolutional neural network

DL:

Deep learning

FC:

Fully connected

HCC:

Hepatocellular carcinoma

LASSO:

Least absolute shrinkage and selection operator

MRI:

Magnetic resonance imaging

NPV:

Negative predictive value

PPV:

Positive predictive value

RFS:

Recurrence-free survival

ROC:

Receiver operating characteristic

T2WI:

T2-weighted imaging

VOI:

Volume of interest

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Acknowledgements

Funding was provided by National Natural Science Foundation of China (Grant Nos. 81227901, 81527805, 81571661), Chinese Academy of Sciences (Grant No. GJJSTD20170004), National Key Research and Development Program of China (Grant No.2017YFC0108804), The Shanghai Sailing Program (Grant No. 19YF1408100), Fudan University Medical-Industrial Integration Project (Grant No. XM03211196) and The China International Medical Foundation (Grant No. Z-2014-07-2003-04).

Funding

This study was funded by National Natural Science Foundation of China (No. 81227901, 81527805, 81571661), Chinese Academy of Sciences (No. GJJSTD20170004), The National Key Research and Development Program of China (No. 2017YFC0108804), The Shanghai Sailing Program (No. 19YF1408100), The China International Medical Foundation (No. Z-2014-07-2003-04), and Fudan University Medical-Industrial Integration Project (No. XM03211196).

Author information

Author notes

  1. Wenyu Gao, Wentao Wang, and Danjun Song have contributed equally to this work.

Authors and Affiliations

  1. Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China

    Wenyu Gao & Manning Wang

  2. Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China

    Wenyu Gao & Manning Wang

  3. Department of Radiology, Cancer Center, Shanghai Medical Imaging Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Rd., Shanghai, 200032, China

    Wentao Wang, Chun Yang, Mengsu Zeng & Sheng-xiang Rao

  4. Shanghai Institute of Medical Imaging, Shanghai, China

    Wentao Wang, Mengsu Zeng & Sheng-xiang Rao

  5. Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China

    Danjun Song & Kai Zhu

  6. Department of Interventional Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China

    Danjun Song

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  1. Wenyu Gao
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Correspondence to Sheng-xiang Rao or Manning Wang.

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Gao, W., Wang, W., Song, D. et al. A predictive model integrating deep and radiomics features based on gadobenate dimeglumine-enhanced MRI for postoperative early recurrence of hepatocellular carcinoma. Radiol med 127, 259–271 (2022). https://doi.org/10.1007/s11547-021-01445-6

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