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Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules

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

Abstract

Purpose

To enhance clinician’s decision-making by diagnosing hepatocellular carcinoma (HCC) in cirrhotic patients with indeterminate liver nodules using quantitative imaging features extracted from triphasic CT scans.

Material and methods

We retrospectively analyzed 178 cirrhotic patients from 27 institutions, with biopsy-proven liver nodules classified as indeterminate using the European Association for the Study of the Liver (EASL) guidelines. Patients were randomly assigned to a discovery cohort (142 patients (pts.)) and a validation cohort (36 pts.). Each liver nodule was segmented on each phase of triphasic CT scans, and 13,920 quantitative imaging features (12 sets of 1160 features each reflecting the phenotype at one single phase or its change between two phases) were extracted. Using machine-learning techniques, the signature was trained and calibrated (discovery cohort), and validated (validation cohort) to classify liver nodules as HCC vs. non-HCC. Effects of segmentation and contrast enhancement quality were also evaluated.

Results

Patients were predominantly male (88%) and CHILD A (65%). Biopsy was positive for HCC in 77% of patients. LI-RADS scores were not different between HCC and non-HCC patients. The signature included a single radiomics feature quantifying changes between arterial and portal venous phases: DeltaV-A_DWT1_LL_Variance-2D and reached area under the receiver operating characteristic curve (AUC) of 0.70 (95%CI 0.61–0.80) and 0.66 (95%CI 0.64–0.84) in discovery and validation cohorts, respectively. The signature was influenced neither by segmentation nor by contrast enhancement.

Conclusion

A signature using a single feature was validated in a multicenter retrospective cohort to diagnose HCC in cirrhotic patients with indeterminate liver nodules. Artificial intelligence could enhance clinicians’ decision by identifying a subgroup of patients with high HCC risk.

Key Points

• In cirrhotic patients with visually indeterminate liver nodules, expert visual assessment using current guidelines cannot accurately differentiate HCC from differential diagnoses. Current clinical protocols do not entail biopsy due to procedural risks. Radiomics can be used to non-invasively diagnose HCC in cirrhotic patients with indeterminate liver nodules, which could be leveraged to optimize patient management.

• Radiomics features contributing the most to a better characterization of visually indeterminate liver nodules include changes in nodule phenotype between arterial and portal venous phases: the “washout” pattern appraised visually using EASL and EASL guidelines.

• A clinical decision algorithm using radiomics could be applied to reduce the rate of cirrhotic patients requiring liver biopsy (EASL guidelines) or wait-and-see strategy (AASLD guidelines) and therefore improve their management and outcome.

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Abbreviations

A:

Arterial CT phase

AASLD:

The American Association for the Study of Liver Diseases

AUC:

Area under the receiver operating characteristic curve

CCC:

Concordance correlation coefficient

CT:

Computed tomography

Delta:

Dual phase defined by the change between imaging features extracted from two different CT phases (A-NC, V-A, V-NC)

Delta1A-P:

Dual phase defined by the change between arterial and non-contrast CT phases, using standardized subtraction (delta 1)

Delta1V-A:

Dual phase defined by the change between portal venous and arterial CT phases, using standardized subtraction (delta 1)

Delta1V-NC:

Dual phase defined by the change between portal venous and non-contrast CT phases, using standardized subtraction (delta 1)

Delta2A-NC:

Dual phase defined by the change between arterial and non-contrast CT phases, using direct subtraction (delta 2)

Delta2V-A:

Dual phase defined by the change between portal venous and arterial CT phases, using direct subtraction (delta 2)

Delta2V-NC:

Dual phase defined by the change between portal venous and non-contrast CT phases, using direct subtraction (delta 2)

Delta3A-NC:

Dual phase defined by the change between arterial and non-contrast CT phases, using relative subtraction (delta 3)

Delta3V-A:

Dual phase defined by the change between portal venous and arterial CT phases, using relative subtraction (delta 3)

Delta3V-NC:

Dual phase defined by the change between portal venous and non-contrast CT phases, using relative subtraction (delta 3)

DWF_LL:

Discrete wavelet frame in the low-pass channel radiomics feature

DWT1_LL:

Dual-tree wavelet transform in the low-pass channel radiomics feature

EASL:

The European Association for the Study of the Liver

GLCM:

Gray-level co-occurrence matrix radiomics feature

HCC:

Hepatocellular carcinoma

KNN:

K-Nearest neighbor machine-learning algorithm

LI-RADS:

Liver Imaging Reporting and Data System

LR-1 to 5 and M:

LI-RADS categories, according to LI-RADS criteria version 2017

MRI:

Magnetic resonance imaging

NASH:

Non-alcoholic steatohepatitis

NC:

Non-contrast CT phase

RF:

Random forest machine-learning algorithm

SMOTE:

Synthetic Minority Over-sampling Technique

SVM:

Support vector machine machine-learning algorithm

V:

Portal venous CT phase

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Acknowledgments

This manuscript was revised by Qian Min, statistician. Sincere appreciation is expressed to Tavis Allison and Ronan Trépos for their assistance in the preparation of this manuscript.

Funding

This study was supported through research grants from Alain Rahmouni French Society of Radiology-CERF 2018 (FZM), Fondation Philanthropia (LD), and Fondation Nuovo-Soldati (LD).

Author information

Author notes

  1. Fatima-Zohra Mokrane and Lin Lu contributed equally to this work.

Authors and Affiliations

  1. Radiology Department, Rangueil University Hospital, Toulouse, France

    Fatima-Zohra Mokrane, Adrien Vavasseur, Philippe Otal & Herve Rousseau

  2. Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA

    Fatima-Zohra Mokrane, Lin Lu, Lyndon Luk, Hao Yang, Binsheng Zhao, Lawrence H. Schwartz & Laurent Dercle

  3. Hepatology Department, Purpan University Hospital, Toulouse, France

    Jean-Marie Peron

  4. Service de Radiologie, Gustave-Roussy, Université Paris-Saclay, Villejuif, France

    Samy Ammari

  5. Department of Medicine, Division of Hematology/Oncology, Columbia University Medical Center/New York Presbyterian, New York, NY, USA

    Yvonne Saenger

  6. INSERM U1015, Gustave Roussy Institute, Université Paris-Saclay, F-94805, Villejuif, France

    Laurent Dercle

Authors
  1. Fatima-Zohra Mokrane
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Corresponding author

Correspondence to Fatima-Zohra Mokrane.

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Guarantor

The scientific guarantor of this publication is Fatima-Zohra Mokrane.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Qian Min, PhD, and Ronan Trépos, PhD, kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained (IRB form: AAAR9377).

Methodology

• retrospective

• diagnostic

• multicenter study

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Mokrane, FZ., Lu, L., Vavasseur, A. et al. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. Eur Radiol 30, 558–570 (2020). https://doi.org/10.1007/s00330-019-06347-w

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  • DOI: https://doi.org/10.1007/s00330-019-06347-w

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