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European Radiology

, Volume 30, Issue 1, pp 558–570 | Cite as

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

  • Fatima-Zohra MokraneEmail author
  • Lin Lu
  • Adrien Vavasseur
  • Philippe Otal
  • Jean-Marie Peron
  • Lyndon Luk
  • Hao Yang
  • Samy Ammari
  • Yvonne Saenger
  • Herve Rousseau
  • Binsheng Zhao
  • Lawrence H. Schwartz
  • Laurent Dercle
Imaging Informatics and Artificial Intelligence

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.

Keywords

Cirrhosis Radiomics Hepatocellular carcinoma 

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

Notes

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).

Compliance with ethical standards

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

Supplementary material

330_2019_6347_MOESM1_ESM.docx (23.2 mb)
ESM 1 (DOCX 23794 kb)

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Fatima-Zohra Mokrane
    • 1
    • 2
    Email author
  • Lin Lu
    • 2
  • Adrien Vavasseur
    • 1
  • Philippe Otal
    • 1
  • Jean-Marie Peron
    • 3
  • Lyndon Luk
    • 2
  • Hao Yang
    • 2
  • Samy Ammari
    • 4
  • Yvonne Saenger
    • 5
  • Herve Rousseau
    • 1
  • Binsheng Zhao
    • 2
  • Lawrence H. Schwartz
    • 2
  • Laurent Dercle
    • 2
    • 6
  1. 1.Radiology DepartmentRangueil University HospitalToulouseFrance
  2. 2.Department of Radiology, New York Presbyterian HospitalColumbia University Vagelos College of Physicians and SurgeonsNew York CityUSA
  3. 3.Hepatology DepartmentPurpan University HospitalToulouseFrance
  4. 4.Service de Radiologie, Gustave-RoussyUniversité Paris-SaclayVillejuifFrance
  5. 5.Department of Medicine, Division of Hematology/OncologyColumbia University Medical Center/New York PresbyterianNew YorkUSA
  6. 6.INSERM U1015, Gustave Roussy InstituteUniversité Paris-SaclayVillejuifFrance

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