Skip to main content

Advertisement

SpringerLink
Log in

Diagnostic value of whole-tumor apparent diffusion coefficient map radiomics analysis in predicting early recurrence of solitary hepatocellular carcinoma ≤ 5 cm

  • Hepatobiliary
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To evaluate the role of whole-tumor radiomics analysis of apparent diffusion coefficient (ADC) maps in predicting early recurrence (ER) of solitary hepatocellular carcinoma (HCC) ≤ 5 cm and compare the diagnostic efficiency of whole-tumor and single-slice ADC measurements.

Methods

One hundred and seventy patients with primary HCC were randomly divided into the training set (n = 119) and the test set (n = 51). The diagnostic efficiency was compared between the whole-tumor and single-slice ADC measurements. The clinical–radiological model was established by selected significant clinical characteristics and qualitative imaging features. The radiomics model was constructed using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. The significant clinical–radiological risk factors and radiomics features were integrated to develop the combined model. Receiver operating characteristic (ROC) curves were used for evaluating the predictive performance.

Results

Cirrhosis, age, and albumin were significantly associated with ER in the clinical–radiological model selected by the random forest classifier. The diagnostic efficiency of the whole-tumor ADC measurements was slight higher than that of the single-slice (AUC = 0.602 and 0.586, respectively). The clinical–radiological model (AUC = 0.84 and 0.82 in the training and test sets, respectively) showed better diagnostic performance than the radiomics model (AUC = 0.70 and 0.69 in the training and test sets, respectively) in predicting ER. The combined model showed optimal predictive performance with the highest AUC values of 0.88 and 0.85 in the training and test sets, respectively.

Conclusions

The whole-tumor ADC measurements performed better than the single-slice ADC measurements. The clinical–radiological model performed better than the radiomics model for predicting ER in patients with solitary HCC ≤ 5 cm.

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
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

AdaBoost:

Adaptive boosting

ADC:

Apparent diffusion coefficient

AFP:

Alpha-fetoprotein

ALB:

Albumin

ANOVA:

Analysis of variance

AUC:

Area under the curve

CI:

Confidence interval

DWI:

Diffusion-weighted imaging

ER:

Early recurrence

HCC:

Hepatocellular carcinoma

ICC:

Intraclass correlation coefficient

LASSO:

The least absolute shrinkage and selection operator

LR:

Logistic regression

MRI:

Magnetic resonance imaging

RF:

Random forest

ROC:

Receiver operating characteristic

ROI:

Region of interest

VOI:

Volume of interest

References

  1. [1] Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71:209-249

    PubMed  Google Scholar 

  2. [2] Chen W, Zheng R, Baade PD et al (2016) Cancer statistics in China, 2015. CA Cancer J Clin 66:115-132

    Article  Google Scholar 

  3. [3] Wang JH, Wang CC, Hung CH, Chen CL, Lu SN (2012) Survival comparison between surgical resection and radiofrequency ablation for patients in BCLC very early/early stage hepatocellular carcinoma. Journal of Hepatology 56:412-418

    Article  Google Scholar 

  4. [4] Portolani N, Coniglio A, Ghidoni S et al (2006) Early and late recurrence after liver resection for hepatocellular carcinoma: prognostic and therapeutic implications. Annals of Surgery 243:229-235

    Article  Google Scholar 

  5. [5] Xu XF, Xing H, Han J et al (2019) Risk Factors, Patterns, and Outcomes of Late Recurrence After Liver Resection for Hepatocellular Carcinoma: A Multicenter Study From China. JAMA Surgery 154:209-217

    Article  Google Scholar 

  6. [6] Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology 278:563-577

    Article  Google Scholar 

  7. [7] Ariizumi S, Kitagawa K, Kotera Y et al (2011) A non-smooth tumor margin in the hepatobiliary phase of gadoxetic acid disodium (Gd-EOB-DTPA)-enhanced magnetic resonance imaging predicts microscopic portal vein invasion, intrahepatic metastasis, and early recurrence after hepatectomy in patients with hepatocellular carcinoma. J Hepatobiliary Pancreat Sci 18:575-585

    Article  Google Scholar 

  8. Zhang L, Kuang S, Chen J et al (2019) The Role of Preoperative Dynamic Contrast-enhanced 3.0-T MR Imaging in Predicting Early Recurrence in Patients With Early-Stage Hepatocellular Carcinomas After Curative Resection. Frontiers in Oncology 9:1336

  9. [9] An C, Kim DW, Park YN, Chung YE, Rhee H, Kim MJ (2015) Single Hepatocellular Carcinoma: Preoperative MR Imaging to Predict Early Recurrence after Curative Resection. Radiology 276:433-443

    Article  Google Scholar 

  10. [10] Ahn SJ, Kim JH, Park SJ, Kim ST, Han JK (2019) Hepatocellular carcinoma: preoperative gadoxetic acid-enhanced MR imaging can predict early recurrence after curative resection using image features and texture analysis. Abdom Radiol (NY) 44:539-548

    Article  Google Scholar 

  11. [11] Lewis S, Dyvorne H, Cui Y, Taouli B (2014) Diffusion-weighted imaging of the liver: techniques and applications. Magn Reson Imaging Clin N Am 22:373-395

    Article  Google Scholar 

  12. [12] Lee S, Kim SH, Hwang JA, Lee JE, Ha SY (2019) Pre-operative ADC predicts early recurrence of HCC after curative resection. European Radiology 29:1003-1012

    Article  Google Scholar 

  13. [13] Ameli S, Shaghaghi M, Aliyari GM et al (2020) Role of baseline volumetric functional MRI in predicting histopathologic grade and patients' survival in hepatocellular carcinoma. European Radiology 30:3748-3758

    Article  Google Scholar 

  14. [14] Kong C, Zhao Z, Chen W et al (2021) Prediction of tumor response via a pretreatment MRI radiomics-based nomogram in HCC treated with TACE. European Radiology 31:7500-7511

    Article  Google Scholar 

  15. [15] Song W, Yu X, Guo D et al (2020) MRI-Based Radiomics: Associations With the Recurrence-Free Survival of Patients With Hepatocellular Carcinoma Treated With Conventional Transcatheter Arterial Chemoembolization. Journal of Magnetic Resonance Imaging 52:461-473

    Article  Google Scholar 

  16. [16] Zhang Z, Jiang H, Chen J et al (2019) Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction. Cancer Imaging 19:22

    Article  CAS  Google Scholar 

  17. [17] Yuan C, Wang Z, Gu D et al (2019) Prediction early recurrence of hepatocellular carcinoma eligible for curative ablation using a Radiomics nomogram. Cancer Imaging 19:21

    Article  Google Scholar 

  18. [18] Lurie Y, Webb M, Cytter-Kuint R, Shteingart S, Lederkremer GZ (2015) Non-invasive diagnosis of liver fibrosis and cirrhosis. World J Gastroenterol 21:11567-11583

    Article  CAS  Google Scholar 

  19. [19] Muhi A, Ichikawa T, Motosugi U et al (2013) Diffusion-weighted imaging of hepatocellular carcinoma for predicting early recurrence and survival after hepatectomy. Hepatology International 7:662-668

    Article  Google Scholar 

  20. [20] Nakanishi M, Chuma M, Hige S et al (2012) Relationship between diffusion-weighted magnetic resonance imaging and histological tumor grading of hepatocellular carcinoma. Annals of Surgical Oncology 19:1302-1309

    Article  Google Scholar 

  21. [21] Zhao Y, Wu J, Zhang Q et al (2021) Radiomics Analysis Based on Multiparametric MRI for Predicting Early Recurrence in Hepatocellular Carcinoma After Partial Hepatectomy. Journal of Magnetic Resonance Imaging 53:1066-1079

    Article  Google Scholar 

  22. [22] Arita Y, Yoshida S, Kwee TC et al (2021) Diagnostic value of texture analysis of apparent diffusion coefficient maps for differentiating fat-poor angiomyolipoma from non-clear-cell renal cell carcinoma. European Journal of Radiology 143:109895

    Article  Google Scholar 

  23. [23] Yin JD, Song LR, Lu HC, Zheng X (2020) Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps. World J Gastroenterol 26:2082-2096

    Article  Google Scholar 

  24. [24] Omata M, Cheng AL, Kokudo N et al (2017) Asia-Pacific clinical practice guidelines on the management of hepatocellular carcinoma: a 2017 update. Hepatology International 11:317-370

    Article  Google Scholar 

  25. [25] Nahm JH, Lee HS, Kim H et al (2021) Pathological predictive factors for late recurrence of hepatocellular carcinoma in chronic liver disease. Liver International 41:1662-1674

    Article  CAS  Google Scholar 

  26. [26] Wang H, Wu MC, Cong WM (2019) Microvascular invasion predicts a poor prognosis of solitary hepatocellular carcinoma up to 2 cm based on propensity score matching analysis. Hepatology Research 49:344-354

    Article  CAS  Google Scholar 

  27. [27] Li T, Fan J, Qin LX et al (2011) Risk factors, prognosis, and management of early and late intrahepatic recurrence after resection of primary clear cell carcinoma of the liver. Annals of Surgical Oncology 18:1955-1963

    Article  CAS  Google Scholar 

  28. [28] Pompili M, Saviano A, de Matthaeis N et al (2013) Long-term effectiveness of resection and radiofrequency ablation for single hepatocellular carcinoma </=3 cm. Results of a multicenter Italian survey. Journal of Hepatology 59:89-97

    Article  Google Scholar 

  29. [29] Chong HH, Yang L, Sheng RF et al (2021) Multi-scale and multi-parametric radiomics of gadoxetate disodium-enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma </= 5 cm. European Radiology 31:4824-4838

    Article  CAS  Google Scholar 

  30. [30] Wu N, Chen G, Hu H, Pang L, Chen Z (2015) Low pretherapeutic serum albumin as a risk factor for poor outcome in esophageal squamous cell carcinomas. Nutrition and Cancer-An International Journal 67:481-485

    Article  CAS  Google Scholar 

  31. [31] Gupta D, Lis CG (2010) Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature. Nutrition Journal 9:69

    Article  Google Scholar 

  32. [32] Li B, Liu A, Wen Y et al (2021) The prognostic values of serum markers in hepatocellular carcinoma after invasive therapies based on real-world data. Journal of Clinical Laboratory Analysis 35:e23932

    CAS  PubMed  PubMed Central  Google Scholar 

  33. [33] Zeng J, Lin K, Liu H et al (2020) Prognosis Factors of Young Patients Undergoing Curative Resection for Hepatitis B Virus-Related Hepatocellular Carcinoma: A Multicenter Study. Cancer Management and Research 12:6597-6606

    Article  CAS  Google Scholar 

  34. [34] Lai Y, Lee JC, Hung HC et al (2020) Models to predict disease-free survival for hepatocellular carcinoma patients with surgical resections. Journal of Surgical Oncology 122:1444-1452

    Article  CAS  Google Scholar 

Download references

Funding

This study was supported by the PUMC Youth Fund (Grant No. 2017320010).

Author information

Author notes

  1. Xiaohong Ma and Xinming Zhao contributed equally to this work.

Authors and Affiliations

  1. Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China

    Leyao Wang, Bing Feng, Meng Liang, Dengfeng Li, Shuang Wang, Xiaohong Ma & Xinming Zhao

  2. Magnetic Resonance Imaging Research, General Electric Healthcare (China), Beijing, 100176, China

    Sicong Wang

  3. Institute of Electronical and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, 518055, China

    Jiesi Hu

Authors
  1. Leyao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bing Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sicong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiesi Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Meng Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dengfeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shuang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xiaohong Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xinming Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: LW and XM; methodology: LW and BF; formal analysis and investigation: LW, SW, JH, DL, ML, and SW; writing—original draft preparation: LW; writing—review and editing: XM and XZ; supervision: XZ.

Corresponding authors

Correspondence to Xiaohong Ma or Xinming Zhao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This study was approved by our institutional review board.

Informed consent

The requirement for patient informed consent was waived due to the retrospective nature of this study.

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 12 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, L., Feng, B., Wang, S. et al. Diagnostic value of whole-tumor apparent diffusion coefficient map radiomics analysis in predicting early recurrence of solitary hepatocellular carcinoma ≤ 5 cm. Abdom Radiol 47, 3290–3300 (2022). https://doi.org/10.1007/s00261-022-03582-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-022-03582-6

Keywords

Search

Navigation