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

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Title: Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound.
Authors: Liu, Dan1,2 (AUTHOR), Liu, Fei2,3 (AUTHOR), Xie, Xiaoyan1 (AUTHOR), Su, Liya1 (AUTHOR), Liu, Ming1 (AUTHOR), Xie, Xiaohua1 (AUTHOR), Kuang, Ming1,4 (AUTHOR), Huang, Guangliang1 (AUTHOR), Wang, Yuqi2,3 (AUTHOR), Zhou, Hui2,3 (AUTHOR), Wang, Kun2,3 (AUTHOR), Lin, Manxia1 (AUTHOR) linmxia@mail.sysu.edu.cn, Tian, Jie2,5 (AUTHOR) tian@ieee.org
Source: European Radiology. Apr2020, Vol. 30 Issue 4, p2365-2376. 12p. 3 Diagrams, 2 Charts, 1 Graph.
Subjects: Contrast-enhanced ultrasound, Chemoembolization, Hepatocellular carcinoma, Artificial intelligence, Receiver operating characteristic curves
Abstract: Objectives: 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.Methods: 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.Results: 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.Conclusions: 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. [ABSTRACT FROM AUTHOR]
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Abstract:<bold>Objectives: </bold>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.<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusions: </bold>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.<bold>Key Points: </bold>• 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. [ABSTRACT FROM AUTHOR]
ISSN:09387994
DOI:10.1007/s00330-019-06553-6