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Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: choice of four-phase and three-phase CT imaging protocol

  • Hepatobiliary
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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To evaluate whether a three-phase dynamic contrast-enhanced CT protocol, when combined with a deep learning model, has similar accuracy in differentiating hepatocellular carcinoma (HCC) from other focal liver lesions (FLLs) compared with a four-phase protocol.

Methods

Three hundred and forty-two patients (mean age 49.1 ± 10.5 years, range 19–86 years, 65.8% male) scanned with a four-phase CT protocol (precontrast, arterial, portal-venous and delayed phases) were retrospectively enrolled. A total of 449 FLLs were categorized into HCC and non-HCC groups based on the best available reference standard. Three convolutional dense networks (CDNs) with the input of four-phase CT images (model A), three-phase images without portal-venous phase (model B) and three-phase images without precontrast phase (model C) were trained on 80% of lesions and evaluated in the other 20% by receiver operating characteristics (ROC) and confusion matrix analysis. The DeLong test was performed to compare the areas under the ROC curves (AUCs) of A with B, B with C, and A with C.

Results

The diagnostic accuracy in differentiating HCC from other FLLs on test sets was 83.3% for model A, 81.1% for model B and 85.6% for model C, and the AUCs were 0.925, 0.862 and 0.920, respectively. The AUCs of models A and C did not differ significantly (p = 0.765), but the AUCs of models A and B (p = 0.038) and of models B and C (p = 0.028) did.

Conclusions

When combined with a CDN, a three-phase CT protocol without precontrast showed similar diagnostic accuracy as a four-phase protocol in differentiating HCC from other FLLs, suggesting that the multiphase CT protocol for HCC diagnosis might be optimized by removing the precontrast phase to reduce radiation dose.

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Funding

The scientific guarantor of this publication is Jin Wang. This study has received funding by (1) National Natural Science Foundation of China (Grant No. 91959118); (2) Science and Technology Program of Guangzhou, China (Grant No. 201704020016); (3) SKY Radiology Department International Medical Research Foundation of China (Grant No. Z-2014-07-1912-15); (4) Clinical Research Foundation of the 3rd Affiliated Hospital of Sun Yat-sen University (Grant No. YHJH201901).

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Author notes

  1. Wenqi Shi and Sichi Kuang have contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, China

    Wenqi Shi, Sichi Kuang, Sue Cao, Bing Hu, Sidong Xie, Simin Chen, Hanxi Zhang & Jin Wang

  2. 12 Sigma Technologies, NO. 420 Fenglin Road, Xuhui District, Shanghai, China

    Yinan Chen, Yajing Zhu, Hui Liu & Meng Ye

  3. 12 Sigma Technologies, 11975 El Camino Real, Suite 102, San Diego, CA, 92130, USA

    Dashan Gao & Yunqiang Chen

  4. Liver Imaging Group, Department of Radiology, University of California at San Diego, Medical Center Drive, La Jolla, CA, 92037, USA

    Claude B. Sirlin

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  1. Wenqi Shi
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Corresponding author

Correspondence to Jin Wang.

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Conflict of interest

Professor Claude B. Sirlin declares financial disclosures: Educational materials royalties: Wolters Kluwer. Research grants: Foundation of NTH, Bayer, GE, Gilead, Philips, Siemens. Consulting: Blade, Boehringer, Epigenomics. Consulting under auspices of UC: AMRA, BMS, Exact Sciences, GE Digital, IBM-Watson. Core lab services: Enanta, Gilead, ICON, Intercept, Nusirt, Shire, Synageva, Takeda. Non-Financial disclosures: Co-Chair, LI-RADS Steering Committee. Co-Chair, LI-RADS Lexicon & Writing Group. Professor Claude B. Sirlin received no grant funding. Professor Jin Wang declares financial disclosures: Research grants: National Natural Science Foundation of China Grant 91959118 (JW), Science and Technology Program of Guangzhou, China 201704020016 (JW), SKY Radiology Department International Medical Research Foundation of China Z-2014-07-1912-15 (JW), Clinical Research Foundation of the 3rd Affiliated Hospital of Sun Yat-sen University YHJH201901 (JW). Non-Financial disclosures: Member, LI-RADS international working group. Member, ISMRM Education committee. Other authors declare that there are no financial or other relationships that might lead to a conflict of interest of the present article. All authors have reviewed the final version of the manuscript and approved it for publication.

Ethics approval

Our institutional review board approved this retrospective study and waived the need for informed consent. This article did not contain any studies with animals.

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Shi, W., Kuang, S., Cao, S. et al. Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: choice of four-phase and three-phase CT imaging protocol. Abdom Radiol 45, 2688–2697 (2020). https://doi.org/10.1007/s00261-020-02485-8

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