Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Deep Learning: A Multi-Center and Prospective Validation Study

Simple Summary Microvascular invasion (MVI) is an independent risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). Preoperative knowledge of MVI would assist with tailored surgical strategy making to prolong patient survival. Previous radiological studies proved the role of noninvasive medical imaging in MVI prediction. However, hitherto, deep learning methods remained unexplored for this clinical task. As an end-to-end self-learning strategy, deep learning may not only achieve improved prediction accuracy, but may also visualize high-risk areas of invasion by generating attention maps. In this multicenter study, we developed deep learning models to perform MVI preoperative assessments using two imaging modalities—computed tomography (CT) and gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI). A head-to-head prospective validation was conducted to verify the validity of deep learning models and achieve a comparison between CT and EOB-MRI for MVI assessment. The findings put forward a better understanding of MVI preoperative prediction in HCC management. Abstract Microvascular invasion (MVI) is a critical risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). Preknowledge of MVI would assist tailored surgery planning in HCC management. In this multicenter study, we aimed to explore the validity of deep learning (DL) in MVI prediction using two imaging modalities—contrast-enhanced computed tomography (CE-CT) and gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI). A total of 750 HCCs were enrolled from five Chinese tertiary hospitals. Retrospective CE-CT (n = 306, collected between March, 2013 and July, 2019) and EOB-MRI (n = 329, collected between March, 2012 and March, 2019) data were used to train two DL models, respectively. Prospective external validation (n = 115, collected between July, 2015 and February, 2018) was performed to assess the developed models. Furthermore, DL-based attention maps were utilized to visualize high-risk MVI regions. Our findings revealed that the EOB-MRI-based DL model achieved superior prediction outcome to the CE-CT-based DL model (area under receiver operating characteristics curve (AUC): 0.812 vs. 0.736, p = 0.038; sensitivity: 70.4% vs. 57.4%, p = 0.015; specificity: 80.3% vs. 86.9%, p = 0.052). DL attention maps could visualize peritumoral high-risk areas with genuine histopathologic confirmation. Both DL models could stratify high and low-risk groups regarding progression free survival and overall survival (p < 0.05). Thus, DL can be an efficient tool for MVI prediction, and EOB-MRI was proven to be the modality with advantage for MVI assessment than CE-CT.

[1]  Jing Zhang,et al.  Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. , 2019, Journal of hepatology.

[2]  K. Tanabe,et al.  Prognostic and Therapeutic Implications of Microvascular Invasion in Hepatocellular Carcinoma , 2019, Annals of Surgical Oncology.

[3]  D. Gu,et al.  Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT , 2019, European Radiology.

[4]  M. Abecassis,et al.  Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases , 2019, Clinical Liver Disease.

[5]  D. Gu,et al.  A Radiomics Nomogram for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma , 2018, Liver Cancer.

[6]  Kathryn J Fowler,et al.  Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. , 2018, Radiology.

[7]  Jie Tian,et al.  Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study , 2018, Gut.

[8]  R. Golfieri,et al.  Imaging features of microvascular invasion in hepatocellular carcinoma developed after direct-acting antiviral therapy in HCV-related cirrhosis , 2018, European Radiology.

[9]  O. Abe,et al.  Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study. , 2017, Radiology.

[10]  R. Grimm,et al.  Assessment of Microvascular Invasion of Hepatocellular Carcinoma with Diffusion Kurtosis Imaging. , 2017, Radiology.

[11]  D. Sinn,et al.  Preoperative gadoxetic acid-enhanced MRI for predicting microvascular invasion in patients with single hepatocellular carcinoma. , 2017, Journal of hepatology.

[12]  Ramprasaath R. Selvaraju,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, International Journal of Computer Vision.

[13]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Stefano Brocchi,et al.  Can Current Preoperative Imaging Be Used to Detect Microvascular Invasion of Hepatocellular Carcinoma? , 2016, Radiology.

[15]  Kui Wang,et al.  Nomogram for Preoperative Estimation of Microvascular Invasion Risk in Hepatitis B Virus-Related Hepatocellular Carcinoma Within the Milan Criteria. , 2016, JAMA surgery.

[16]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  A. Rutman,et al.  A Computed Tomography Radiogenomic Biomarker Predicts Microvascular Invasion and Clinical Outcomes in Hepatocellular Carcinoma , 2015, Hepatology.

[18]  Y. Maehara,et al.  New Pathologic Stratification of Microvascular Invasion in Hepatocellular Carcinoma: Predicting Prognosis After Living-donor Liver Transplantation , 2015, Transplantation.

[19]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[20]  M. Schwartz,et al.  Recurrence of hepatocellular cancer after resection: patterns, treatments, and prognosis. , 2015, Annals of surgery.

[21]  M. Sata,et al.  The Significance of Classifying Microvascular Invasion in Patients with Hepatocellular Carcinoma , 2014, Annals of Surgical Oncology.

[22]  F. Gheza,et al.  Number and Tumor Size Are Not Sufficient Criteria to Select Patients for Liver Transplantation for Hepatocellular Carcinoma , 2012, Annals of Surgical Oncology.

[23]  Myeong-Jin Kim,et al.  Prediction of microvascular invasion of hepatocellular carcinoma: Usefulness of peritumoral hypointensity seen on gadoxetate disodium‐enhanced hepatobiliary phase images , 2012, Journal of magnetic resonance imaging : JMRI.

[24]  P. Chow,et al.  Microvascular Invasion Is a Better Predictor of Tumor Recurrence and Overall Survival Following Surgical Resection for Hepatocellular Carcinoma Compared to the Milan Criteria , 2011, Annals of surgery.

[25]  F. Piscaglia,et al.  Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study. , 2010, Journal of hepatology.

[26]  S. Kawasaki,et al.  Risk factors contributing to early and late phase intrahepatic recurrence of hepatocellular carcinoma after hepatectomy. , 2003, Journal of hepatology.

[27]  A. Burroughs,et al.  A Systematic Review of Microvascular Invasion in Hepatocellular Carcinoma: Diagnostic and Prognostic Variability , 2012, Annals of Surgical Oncology.

[28]  L. Mariani,et al.  Predicting survival after liver transplantation in patients with hepatocellular carcinoma beyond the Milan criteria: a retrospective, exploratory analysis. , 2009, The Lancet. Oncology.

[29]  D. Woodfield Hepatocellular carcinoma. , 1986, The New Zealand medical journal.