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

Fatima Zohra Mokrane, 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

Research output: Contribution to journalArticlepeer-review

71 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)558-570
Number of pages13
JournalEuropean Radiology
Volume30
Issue number1
DOIs
StatePublished - Jan 1 2020
Externally publishedYes

Keywords

  • Cirrhosis
  • Hepatocellular carcinoma
  • Radiomics

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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