Skip to main content
SpringerLink
Log in

Preoperative CECT-based Radiomic Signature for Predicting the Response of Transarterial Chemoembolization (TACE) Therapy in Hepatocellular Carcinoma

  • Scientific Paper (other)
  • Imaging
  • Published:
CardioVascular and Interventional Radiology Aims and scope Submit manuscript

Abstract

Purpose

To evaluate the efficiency of radiomics signatures in predicting the response of transarterial chemoembolization (TACE) therapy based on preoperative contrast-enhanced computed tomography (CECT).

Materials

This study consisted of 111 patients with intermediate-stage hepatocellular carcinoma who underwent CECT at both the arterial phase (AP) and venous phase (VP) before and after TACE. According to mRECIST 1.1, patients were divided into an objective-response group (n = 38) and a non-response group (n = 73). Among them, 79 patients were assigned as the training dataset, and the remaining 32 cases were assigned as the test dataset.

Methods

Radiomics features were extracted from CECT images. Two feature ranking methods and three classifiers were used to find the best single-phase radiomics signatures for both AP and VP on the training set. Meanwhile, multi-phase radiomics signatures were built upon integration of images from two CECT phases by decision-level fusion and feature-level fusion. Finally, multivariable logistic regression was used to develop a nomogram by combining radiomics signatures and clinic-radiologic characteristics. The prediction performance was evaluated by AUC on the test dataset.

Results

The multi-phase radiomics signature (AUC = 0.883) performed better in predicting TACE therapy response compared to the best single-phase radiomics signature (AUC = 0.861). The nomogram (AUC = 0.913) showed better performance than any radiomics signatures.

Conclusion

The radiomics signatures and nomogram were developed and validated for predicting responses to TACE therapy, and the radiomics model may play a positive role in identifying patients who may benefit from TACE therapy in clinical practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.

    Article  Google Scholar 

  2. Villanueva A. Hepatocellular carcinoma. N Engl J Med. 2019;380(15):1450–62.

    Article  CAS  Google Scholar 

  3. Chang Y, Jeong SW, Young JJ, Jae KY. Recent updates of transarterial chemoembolilzation in hepatocellular carcinoma. Int J Mol Sci. 2020;21(21):8165.

    Article  CAS  Google Scholar 

  4. Qiu G, Jin Z, Chen X, Huang J. Interpretation of guidelines for the diagnosis and treatment of primary liver cancer (2019 edition) in China. Glob Health Med. 2020;2(5):306–11.

    Article  Google Scholar 

  5. Haywood N, Gennaro K, Obert J, Sauer PF Jr, Redden DT, Zarzour J, et al. Does the degree of hepatocellular carcinoma tumor necrosis following transarterial chemoembolization impact patient survival? J Oncol. 2016;2016:4692139.

    Article  Google Scholar 

  6. Lencioni R, Petruzzi P, Crocetti L. Chemoembolization of hepatocellular carcinoma. Semin Intervent Radiol. 2013;30(1):3–11.

    Article  Google Scholar 

  7. Kudo M, Matsui O, Izumi N, Kadoya M, Okusaka T, Miyayama S, et al. Transarterial chemoembolization failure/refractoriness: JSH-LCSGJ criteria 2014 update. Oncology. 2014;87(Suppl 1):22–31.

    Article  CAS  Google Scholar 

  8. Bruix J, Reig M, Sherman M. Evidence-based diagnosis, staging, and treatment of patients with hepatocellular carcinoma. Gastroenterology. 2016;150(4):835–53.

    Article  Google Scholar 

  9. Kudo M. Proposal of primary endpoints for TACE combination trials with systemic therapy: lessons learned from 5 negative trials and the positive TACTICS trial. Liver Cancer. 2018;7(3):225–34.

    Article  Google Scholar 

  10. Kloeckner R, Pitton MB, Dueber C, Schmidtmann I, Galle PR, Koch S, et al. Validation of clinical scoring systems ART and ABCR after transarterial chemoembolization of hepatocellular carcinoma. J Vasc Interv Radiol. 2017;28(1):94–102.

    Article  Google Scholar 

  11. Chen L, Ni CF, Chen SX, Cao JM, Zhu HD, Guo JH, et al. A modified model for assessment for retreatment with transarterial chemoembolization in Chinese hepatocellular carcinoma patients. J Vasc Interv Radiol. 2016;27(9):1288–97.

    Article  Google Scholar 

  12. Xia W, Hu B, Li H, Geng C, Wu Q, Yang L, et al. Multiparametric-MRI-based radiomics model for differentiating primary central nervous system lymphoma from glioblastoma: development and cross-vendor validation. J Magn Reson Imaging. 2021;53(1):242–50.

    Article  Google Scholar 

  13. Meng X, Xia W, Xie P, Zhang R, Li W, Wang M, et al. Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer. Eur Radiol. 2019;29(6):3200–9.

    Article  Google Scholar 

  14. Xu X, Zhang HL, Liu QP, Sun SW, Zhang J, Zhu FP, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol. 2019;70(6):1133–44.

    Article  Google Scholar 

  15. Jian J, Li Y, Pickhardt PJ, Xia W, He Z, Zhang R, et al. MR image-based radiomics to differentiate type Iota and type IotaIota epithelial ovarian cancers. Eur Radiol. 2021;31(1):403–10.

    Article  Google Scholar 

  16. Wang X, Zhao X, Li Q, Xia W, Peng Z, Zhang R, et al. Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT? Eur Radiol. 2019;29(11):6049–58.

    Article  Google Scholar 

  17. Sun Y, Bai H, Xia W, Wang D, Zhou B, Zhao X, et al. Predicting the outcome of transcatheter arterial embolization therapy for unresectable hepatocellular carcinoma based on radiomics of preoperative multiparameter MRI. J Magn Reson Imaging. 2020;52(4):1083–90.

    Article  Google Scholar 

  18. Sieghart W, Hucke F, Peck-Radosavljevic M. Transarterial chemoembolization: modalities, indication, and patient selection. J Hepatol. 2015;62(5):1187–95.

    Article  Google Scholar 

  19. Peng J, Kang S, Ning Z, Deng H, Shen J, Xu Y, et al. Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging. Eur Radiol. 2020;30(1):413–24.

    Article  Google Scholar 

  20. Chapiro J, Duran R, Lin M, Schernthaner RE, Wang Z, Gorodetski B, et al. Identifying staging markers for hepatocellular carcinoma before transarterial chemoembolization: comparison of three-dimensional quantitative versus non–three-dimensional imaging markers. Radiology. 2015;275(2):438–47.

    Article  Google Scholar 

  21. Zhao W, Yang J, Sun Y, Li C, Wu W, Jin L, et al. 3D deep learning from CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas. Cancer Res. 2018;78(24):6881–9.

    Article  CAS  Google Scholar 

  22. Zhao X, Wang X, Xia W, Li Q, Zhou L, Li Q, et al. A cross-modal 3D deep learning for accurate lymph node metastasis prediction in clinical stage T1 lung adenocarcinoma. Lung Cancer. 2020;145:10–7.

    Article  Google Scholar 

  23. Liu D, Liu F, Xie X, Su L, Liu M, 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. 2020;30(4):2365–76.

    Article  Google Scholar 

  24. Van Griethuysen JJ, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104–7.

    Article  Google Scholar 

  25. Chang Y-W, Lin C-J, editors. Feature ranking using linear SVM. Causation and prediction challenge; 2008: PMLR.

  26. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJJSr. Machine learning methods for quantitative radiomic biomarkers. Sci Rep. 2015;5:13087.

    Article  CAS  Google Scholar 

  27. Nie K, Shi L, Chen Q, Hu X, Jabbour SK, Yue N, et al. Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI Prediction of pathological response for LARC Using MRI. Clin Cancer Res. 2016;22(21):5256–64.

    Article  Google Scholar 

  28. Qi Y. Random forest for bioinformatics. Ensemble machine learning. Springer; 2012. p. 307–23.

    Book  Google Scholar 

  29. Fernández-Delgado M, Cernadas E, Barro S, Amorim DJT. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res. 2014;15(1):3133–81.

    Google Scholar 

  30. Huang Y-q, Liang C-h, He L, Tian J, Liang C-s, Chen X, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol. 2016;34(18):2157–64.

    Article  Google Scholar 

  31. Gunatilaka AH, Baertlein BAJ. Feature-level and decision-level fusion of noncoincidently sampled sensors for land mine detection. IEEE Transact Pattern Anal Mach Intell. 2001;23(6):577–89.

    Article  Google Scholar 

  32. Planet S, Iriondo I, editors. Comparison between decision-level and feature-level fusion of acoustic and linguistic features for spontaneous emotion recognition. In: 7th Iberian conference on information systems and technologies (CISTI 2012) ; 2012: IEEE.

  33. Hanley JA, McNeil BJJR. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29–36.

    Article  CAS  Google Scholar 

  34. Huang Y, Liu Z, He L, Chen X, Pan D, Ma Z, et al. Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non—small cell lung cancer. 2016;281(3) :947–57.

  35. Mundra PA. Rajapakse JCJIton. SVM-RFE with MRMR filter for gene selection. 2009;9(1):31–7.

    Google Scholar 

  36. Ramírez-Gallego S, Lastra I, Martínez-Rego D, Bolón-Canedo V, Benítez JM, Herrera F, et al. Fast-mRMR: fast minimum redundancy maximum relevance algorithm for high-dimensional big data. Int J Intell Syst. 2017;32(2):134–52.

    Article  Google Scholar 

  37. Belgiu M, Drăguţ LJI. Random forest in remote sensing: a review of applications and future directions. J Photogramm Remote Sens. 2016;114:24–31.

    Article  Google Scholar 

  38. Cherkassky V, Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression. J Neural Netw. 2004;17(1):113–26.

    Article  Google Scholar 

  39. Zou HJ. The adaptive lasso and its oracle properties. J Am Stat Assoc. 2006;101(476):1418–29.

    Article  CAS  Google Scholar 

  40. Yasui Y, Tsuchiya K, Kurosaki M, Takeguchi T, Takeguchi Y, Okada M, et al. Up-to-seven criteria as a useful predictor for tumor downstaging to within Milan criteria and Child-Pugh grade deterioration after initial conventional transarterial chemoembolization. Hepatol Res. 2018;48(6):442–50.

    Article  CAS  Google Scholar 

  41. Wang Q, Xia D, Bai W, Wang E, Sun J, Huang M, et al. Development of a prognostic score for recommended TACE candidates with hepatocellular carcinoma: a multicentre observational study. J Hepatol. 2019;70(5):893–903.

    Article  Google Scholar 

  42. Tacher V, Lin M, Duran R, Yarmohammadi H, Lee H, Chapiro J, et al. Comparison of existing response criteria in patients with hepatocellular carcinoma treated with transarterial chemoembolization using a 3D quantitative approach. Radiology. 2016;278(1):275–84.

    Article  Google Scholar 

Download references

Funding

The Key Research and Development Program of Shandong Province (Grant Number 2021SFGC0104). The Key Research and Development Program of Jiangsu [Grant Number: BE2021663]. The National Natural Science Foundation of China [Grant Number: 81871439]. The Science and Technology Planning Project of Suzhou [Grant Number: SJC2021014]. Jiangsu Province Engineering Research Center of Diagnosis and Treatment of Children’s Malignant Tumor.

Author information

Author notes

  1. Honglin Bai, Siyu Meng and Chuanfeng Xiong are primary authors and contributed equally to this work.

  2. Caifang Ni, Xin Gao and Zhi Li are the joint corresponding authors.

Authors and Affiliations

  1. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88 Keling Road, Suzhou, 215163, Jiangsu, China

    Honglin Bai, Siyu Meng, Wei Shi, Wei Xia, XingYu Zhao, Junming Jian, Yizhi Song & Xin Gao

  2. School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, 215163, China

    Honglin Bai & XingYu Zhao

  3. Tandon School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY, USA

    Chuanfeng Xiong

  4. Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, No. 188 Shizi Street, Suzhou, 215006, Jiangsu, China

    Zhao Liu, Qimeng Ren, Caifang Ni & Zhi Li

  5. People′s Hospital of Xinjiang Kizilsu Kirgiz Autonomous Prefecture, West Pamir Road 5, Atush, Xinjiang, 845350, China

    Zhi Li

Authors
  1. Honglin Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siyu Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chuanfeng Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qimeng Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wei Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. XingYu Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Junming Jian
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yizhi Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Caifang Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Xin Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Zhi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Caifang Ni, Xin Gao or Zhi Li.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

Institutional Review Board approval was obtained in concordance with the standards of the First Affiliated Hospital of Soochow University Ethics Committee (2020; approval no. 064). All procedures performed in this study, which involved human participants, were in accordance with the ethical standards of the institutional and/or nation research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Consent for Publication

The manuscript, or part of it, have not been and will not be submitted elsewhere for publication.

Informed Consent

Approved by the ethics committee of the First Affiliated Hospital of Soochow University Ethics Committee, this retrospective analysis was involving CECT of patients in and informed consent was waived for all patients.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 2444 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bai, H., Meng, S., Xiong, C. et al. Preoperative CECT-based Radiomic Signature for Predicting the Response of Transarterial Chemoembolization (TACE) Therapy in Hepatocellular Carcinoma. Cardiovasc Intervent Radiol 45, 1524–1533 (2022). https://doi.org/10.1007/s00270-022-03221-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00270-022-03221-z

Keywords

Search

Navigation