Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound



We aimed to establish and validate an artificial intelligence–based radiomics strategy for predicting personalized responses of hepatocellular carcinoma (HCC) to first transarterial chemoembolization (TACE) session by quantitatively analyzing contrast-enhanced ultrasound (CEUS) cines.


One hundred and thirty HCC patients (89 for training, 41 for validation), who received ultrasound examination (CEUS and B-mode) within 1 week before the first TACE session, were retrospectively enrolled. Ultrasonographic data was used for building and validating deep learning radiomics-based CEUS model (R-DLCEUS), machine learning radiomics-based time-intensity curve of CEUS model (R-TIC), and machine learning radiomics-based B-Mode images model (R-BMode), respectively, to predict responses (objective-response and non-response) to TACE with reference to modified response evaluation criteria in solid tumor. The performance of models was compared by areas under the receiver operating characteristic curve (AUC) and the DeLong test was used to compare different AUCs. The prediction robustness was assessed for each model.


AUCs of R-DLCEUS, R-TIC, and R-BMode were 0.93 (95% CI, 0.80–0.98), 0.80 (95% CI, 0.64–0.90), and 0.81 (95% CI, 0.67–0.95) in the validation cohort, respectively. AUC of R-DLCEUS shows significant difference compared with that of R-TIC (p = 0.034) and R-BMode (p = 0.039), whereas R-TIC was not significantly different from R-BMode. The performance was highly reproducible with different training and validation cohorts.


DL-based radiomics method can effectively utilize CEUS cines to achieve accurate and personalized prediction. It is easy to operate and holds good potential for benefiting TACE candidates in clinical practice.

Key Points

• Deep learning (DL) radiomics-based CEUS model can accurately predict responses of HCC patients to their first TACE session by quantitatively analyzing their pre-operative CEUS cines.

• The visualization of the 3D CNN analysis adopted in CEUS model provided direct insight into what computers “see” on CEUS cines, which can help people understand the interpretation of CEUS data.

• The proposed prediction method is easy to operate and labor-saving for clinical practice, facilitating the clinical treatment decision of HCCs with very few time costs.

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Artificial intelligence


Area under the receiver operating characteristic curve


Barcelona Clinical Liver Cancer


Convolutional 3D


Contrast-enhanced CT


Contrast-enhanced MRI


Contrast-enhanced ultrasound


Convolutional neural network


Complete response


Computed tomography


Deep learning


Fully connected layer


Gradient Boosted Regression Trees


Hepatitis B virus


Hepatocellular carcinoma


Hepatitis C virus


Machine learning


Modified Response Evaluation Criteria in Solid Tumors


Magnetic resonance imaging


Negative predictive value


Progression disease


Positive predictive value


Partial response


Radial based function


Region of interest


Stable disease


Support vector machine


Training cohort


Transarterial chemoembolization


Time-intensity curve


Validation cohort


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This work was supported by grants from the Ministry of Science and Technology of China under Grant (No. 2017YFA0205200), National Natural Science Foundation of China under Grant (Nos. K0109003, 61671449, 81227901, and 81527805), and Chinese Academy of Sciences under Grant (Nos. GJJSTD20170004, XDBS01030200, and QYZDJ-SSW-JSC005).

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Correspondence to Manxia Lin or Jie Tian.

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The scientific guarantor of this publication is Jie Tian.

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Liu, D., Liu, F., Xie, X. et al. Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound. Eur Radiol 30, 2365–2376 (2020).

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  • Therapeutic chemoembolization
  • Hepatocellular carcinoma
  • Ultrasonography
  • Deep learning