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Deep convolutional neural network for preoperative prediction of microvascular invasion and clinical outcomes in patients with HCCs

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

We aimed to develop and validate a deep convolutional neural network (DCNN) model for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) and its clinical outcomes using contrast-enhanced computed tomography (CECT) in a large population of candidates for surgery.

Methods

This retrospective study included 1116 patients with HCC who had undergone preoperative CECT and curative hepatectomy. Radiological (R), DCNN, and combined nomograms were constructed in a training cohort (n = 892) respectively based on clinicoradiological factors, DCNN probabilities, and all factors; the performance of each model was confirmed in a validation cohort (n = 244). Accuracy and the AUC to predict MVI were calculated. Disease-free survival (DFS) and overall survival (OS) after surgery were recorded.

Results

The proportion of MVI-positive patients was respectively 38.8% (346/892) and 35.7 % (87/244) in the training and validation cohorts. The AUCs of the R, DCNN, and combined nomograms were respectively 0.809, 0.929, and 0.940 in the training cohorts and 0.837, 0.865, and 0.897 in the validation cohort. The combined nomogram outperformed the R nomogram in the training (p < 0.001) and validation (p = 0.009) cohorts. There was a significant difference in DFS and OS between the R, DCNN, and combined nomogram-predicted groups with and without MVI (p < 0.001).

Conclusions

The combined nomogram based on preoperative CECT performs well for preoperative prediction of MVI and outcome.

Key Points

• A combined nomogram based on clinical information, preoperative CECT, and DCNN can predict MVI and clinical outcomes of patients with HCC.

• DCNN provides added diagnostic ability to predict MVI.

• The AUCs of the combined nomogram are 0.940 and 0.897 in the training and validation cohorts, respectively.

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Abbreviations

DCNN:

Deep convolutional neural network

DFS:

Disease-free survival

EPAP:

Enhancement pattern in the arterial phase

IDI:

Integrated discrimination improvement

MVI:

Microvascular invasion

NRI:

Net reclassification improvement

OS:

Overall survival

R:

Radiological

RVI:

Radiogenomic venous invasion

References

  1. Marrero JA, Kulik LM, Sirlin CB et al (2018) Diagnosis, staging, and management of hepatocellular carcinoma: 2018 practice guidance by the American Association for the Study of Liver Diseases. Hepatology 68:723–750

    Article  Google Scholar 

  2. Zhou J, Sun HC, Wang Z et al (2018) Guidelines for diagnosis and treatment of primary liver cancer in China (2017 edition). Liver Cancer 7:235–260

    Article  Google Scholar 

  3. Cai W, He B, Hu M et al (2019) A radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure in patients with hepatocellular carcinoma. Surg Oncol 28:78–85

    Article  Google Scholar 

  4. Imamura H, Matsuyama Y, Tanaka E et al (2003) Risk factors contributing to early and late phase intrahepatic recurrence of hepatocellular carcinoma after hepatectomy. J Hepatol 38:200–207

    Article  Google Scholar 

  5. Lim KC, Chow PK, Allen JC et al (2011) Microvascular invasion is a better predictor of tumor recurrence and overall survival following surgical resection for hepatocellular carcinoma compared to the Milan criteria. Ann Surg 254:108–113

    Article  Google Scholar 

  6. Sumie S, Kuromatsu R, Okuda K et al (2008) Microvascular invasion in patients with hepatocellular carcinoma and its predictable clinicopathological factors. Ann Surg Oncol 15:1375–1382

    Article  Google Scholar 

  7. Chou CT, Chen RC, Lee CW, Ko CJ, Wu HK, Chen YL (2012) Prediction of microvascular invasion of hepatocellular carcinoma by pre-operative CT imaging. Br J Radiol 85:778–783

    Article  Google Scholar 

  8. Zhao H, Hua Y, Dai T et al (2017) Development and validation of a novel predictive scoring model for microvascular invasion in patients with hepatocellular carcinoma. Eur J Radiol 88:32–40

    Article  Google Scholar 

  9. Lee S, Kim SH, Lee JE, Sinn DH, Park CK (2017) Preoperative gadoxetic acid-enhanced MRI for predicting microvascular invasion in patients with single hepatocellular carcinoma. J Hepatol 67:526–534

    Article  CAS  Google Scholar 

  10. Li H, Zhang J, Zheng Z et al (2018) Preoperative histogram analysis of intravoxel incoherent motion (IVIM) for predicting microvascular invasion in patients with single hepatocellular carcinoma. Eur J Radiol 105:65–71

    Article  Google Scholar 

  11. Wang WT, Yang L, Yang ZX et al (2018) Assessment of microvascular invasion of hepatocellular carcinoma with diffusion kurtosis imaging. Radiology 286:571–580

    Article  Google Scholar 

  12. Cao L, Chen J, Duan T et al (2019) Diffusion kurtosis imaging (DKI) of hepatocellular carcinoma: correlation with microvascular invasion and histologic grade. Quant Imaging Med Surg 9:590–602

    Article  Google Scholar 

  13. Bi WL, Hosny A, Schabath MB et al (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 69:127–157

    PubMed  PubMed Central  Google Scholar 

  14. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H (2018) Artificial intelligence in radiology. Nat Rev Cancer 18:500–510

    Article  CAS  Google Scholar 

  15. Chilamkurthy S, Ghosh R, Tanamala S et al (2018) Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 392:2388–2396

    Article  Google Scholar 

  16. Li X, Zhang S, Zhang Q et al (2019) Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study. Lancet Oncol 20:193–201

    Article  Google Scholar 

  17. Wang K, Lu X, Zhou H et al (2019) Deep learning radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut 68:729–741

    Article  CAS  Google Scholar 

  18. Zhao K, Li Z, Yao S et al (2020) Artificial intelligence quantified tumour-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer. EBioMedicine 61:103054

    Article  Google Scholar 

  19. Zhang B, Yu K, Ning Z et al (2020) Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: a multicenter retrospective cohort study. Bone 140:115561

    Article  CAS  Google Scholar 

  20. Feng ST, Jia Y, Liao B et al (2019) Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI. Eur Radiol 29:4648–4659

    Article  Google Scholar 

  21. Xu X, Zhang HL, Liu QP et al (2019) Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol 70:1133–1144

    Article  Google Scholar 

  22. Zheng J, Chakraborty J, Chapman WC et al (2017) Preoperative prediction of microvascular invasion in hepatocellular carcinoma using quantitative image analysis. J Am Coll Surg 225(778-788):e771

    Google Scholar 

  23. Peng J, Zhang J, Zhang Q, Xu Y, Zhou J, Liu L (2018) A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma. Diagn Interv Radiol 24:121–127

    Article  Google Scholar 

  24. Ma X, Wei J, Gu D et al (2019) Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT. Eur Radiol 29:3595–3605

    Article  Google Scholar 

  25. Yang L, Gu D, Wei J et al (2019) A radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Liver Cancer 8:373–386

    Article  CAS  Google Scholar 

  26. Finn RS, Zhu AX, Farah W et al (2018) Therapies for advanced stage hepatocellular carcinoma with macrovascular invasion or metastatic disease: a systematic review and meta-analysis. Hepatology 67:422–435

    Article  CAS  Google Scholar 

  27. 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 

  28. Segal E, Sirlin CB, Ooi C et al (2007) Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 25:675–680

    Article  CAS  Google Scholar 

  29. Banerjee S, Wang DS, Kim HJ et al (2015) A computed tomography radiogenomic biomarker predicts microvascular invasion and clinical outcomes in hepatocellular carcinoma. Hepatology 62:792–800

    Article  Google Scholar 

  30. Renzulli M, Brocchi S, Cucchetti A et al (2016) Can current preoperative imaging be used to detect microvascular invasion of hepatocellular carcinoma? Radiology 279:432–442

    Article  Google Scholar 

  31. Zhang R, Xu L, Wen X et al (2019) A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Quant Imaging Med Surg 9:1503–1515

    Article  Google Scholar 

  32. He L, Huang Y, Ma Z, Liang C, Liang C, Liu Z (2016) Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule. Sci Rep 6:34921

    Article  CAS  Google Scholar 

  33. Fave X, Cook M, Frederick A et al (2015) Preliminary investigation into sources of uncertainty in quantitative imaging features. Comput Med Imaging Graph 44:54–61

    Article  Google Scholar 

  34. Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I (2019) Validation of a method to compensate multicenter effects affecting CT radiomics. Radiology 291:53–59

    Article  Google Scholar 

  35. Roayaie S, Blume IN, Thung SN et al (2009) A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma. Gastroenterology 137:850–855

    Article  Google Scholar 

  36. Nitta H, Allard MA, Sebagh M et al (2019) Predictive model for microvascular invasion of hepatocellular carcinoma among candidates for either hepatic resection or liver transplantation. Surgery 165:1168–1175

    Article  Google Scholar 

  37. Lei Z, Li J, Wu D et al (2016) Nomogram for preoperative estimation of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma within the Milan criteria. JAMA Surg 151:356–363

    Article  Google Scholar 

  38. Hsu CC, Goyal A, Iuga A et al (2015) Elevated CA19-9 is associated with increased mortality in a prospective cohort of hepatocellular carcinoma patients. Clin Transl Gastroenterol 6:e74

    Article  CAS  Google Scholar 

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Acknowledgements

We thank Dr. Peihua Cao in Zhujiang Hospital, Southern Medical University, for his help in statistics.

Funding

This work was funded by Guangdong Basic and Applied Basic Research Foundation (2019A1515011269, 2021A1515011305), Clinical Research Startup Program of Southern Medical University by High-Level University Construction Funding of Guangdong Provincial Department of Education (LC2016PY034), and Opening Research Fund of Guangzhou Key Laboratory of Molecular and Functional Imaging for Clinical Translation (201905010003).

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Author notes

  1. Xinming Li and Zhendong Qi contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Zhujiang Hospital, Southern Medical University, No. 253, Industrial Road, Guangzhou, People’s Republic of China

    Xinming Li, Zhendong Qi, Shuping Qin, Xuhui Zhang, Wen Liang & Xianyue Quan

  2. School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, People’s Republic of China

    Haiyan Du, Xiao Zhang & Wei Yang

  3. Department of Radiology, Sun Yat-sen University Cancer Center, No. 651 Dongfeng Road East, Guangzhou, People’s Republic of China

    Zhijun Geng, Zhipeng Li, Jianye Liang & Chuanmiao Xie

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  1. Xinming Li
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Corresponding authors

Correspondence to Chuanmiao Xie or Xianyue Quan.

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Guarantor

The scientific guarantor of this publication is Xianyue Quan.

Conflict of interest

The authors have declared that no competing interest exists.

Statistics and biometry

Two of the authors (Xinming Li and Xianyue Quan) have significant statistical expertise. We thank Dr. Peihua Cao of Zhujiang Hospital, Southern Medical University, for his help in statistics.

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Written informed consent was waived by the institutional review board.

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Institutional review board approval was obtained.

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• retrospective

• diagnostic or prognostic study

• multicenter study

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Li, X., Qi, Z., Du, H. et al. Deep convolutional neural network for preoperative prediction of microvascular invasion and clinical outcomes in patients with HCCs. Eur Radiol 32, 771–782 (2022). https://doi.org/10.1007/s00330-021-08198-w

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  • DOI: https://doi.org/10.1007/s00330-021-08198-w

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