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Prediction of TACE Treatment Response in a Preoperative MRI via Analysis of Integrating Deep Learning and Radiomics Features

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Abstract

Purpose

To evaluate the efficiency of an integrated model on MRI scans of hepatocellular carcinoma (HCC) patients for preoperative prediction of transcatheter arterial chemoembolization (TACE) treatment response.

Methods

Radiomics and deep learning features were integrated to build a prediction model for preoperative procedures so as to obtain a fast and accurate prediction of TACE treatment response. This is a retrospective study and the data consists of 71 HCC patients who underwent TACE treatment in a single center. These patients were divided into two groups: progressive disease (PD) response (20 patients) and non-progressive disease (N-PD) response (51 patients). fivefold cross-validation was applied to the data set to validate model performance. A receiver operating characteristic (ROC) curve was used to assess the predictive ability of the model. Quantification of its results was performed by calculating the area under the receiver operating characteristic curve (AUC). The accuracy, recall, specificity, precision and f1_score were also calculated for the cutoff value that maximized the AUC value.

Results

As assessed by the fivefold cross-validation, the integrated model had the best prediction ability, with a value of AUC 0.947 ± 0.069, an accuracy of 0.893 ± 0.088, f1-score of 0.700 ± 0.245, specificity of 0.700 ± 0.245, precision of 0.700 ± 0.245 and a recall of 0.600 ± 0.279. This was followed by the deep learning-based model with an AUC of 0.867 ± 0.121 and the radiomics-based model with an AUC of 0.848 ± 0.128.

Conclusion

The experiment results demonstrate that a feature set that combines radiomics and deep learning features tends to be effective in predicting TACE treatment response as opposed to using only one feature. However, due to the limited amount of data, more data will be needed to verify the effectiveness of this method in the future.

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Funding

This work was supported by the Integrated Medicine and Engineering Research Project of Fudan University [Grant Numbers yg2021–020].

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Authors and Affiliations

  1. Academy of Engineering and Technology, Fudan University, Shanghai, 200433, China

    Yuchi Tian & Xiaodong Yang

  2. School of Biomedical Engineering, ShanghaiTech University, Shanghai, 201210, China

    Temitope Emmanuel Komolafe

  3. Academy of Information Science and Technology, Fudan University, Shanghai, 200433, China

    Tao Chen

  4. Department of Interventional Radiology, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200032, China

    Bo Zhou

  5. Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China

    Xiaodong Yang

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  1. Yuchi Tian
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  2. Temitope Emmanuel Komolafe
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  3. Tao Chen
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  4. Bo Zhou
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Correspondence to Bo Zhou or Xiaodong Yang.

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Tian, Y., Komolafe, T.E., Chen, T. et al. Prediction of TACE Treatment Response in a Preoperative MRI via Analysis of Integrating Deep Learning and Radiomics Features. J. Med. Biol. Eng. 42, 169–178 (2022). https://doi.org/10.1007/s40846-022-00692-w

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