Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules.
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 i...
| Publicado en: | European Radiology Vol. 30; no. 1; pp. 558 - 571 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=140064734&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 140064734 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jan2020 vid: 30 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 140064734 140064734 NLM31444598 140064734 10.1007/s00330-019-06347-w NLM31444598 140064734 ppf: 558 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. aug: au: Mokrane, Fatima-Zohra Lu, Lin Vavasseur, Adrien Otal, Philippe Peron, Jean-Marie Luk, Lyndon Yang, Hao Ammari, Samy Saenger, Yvonne Rousseau, Herve Zhao, Binsheng Schwartz, Lawrence H. Dercle, Laurent affil: Radiology Department, Rangueil University Hospital, Toulouse, France sug: subj: Liver Cirrhosis Complications Tomography, X-Ray Computed Methods Liver Cirrhosis Carcinoma, Hepatocellular Complications Liver Neoplasms Liver Neoplasms Complications Carcinoma, Hepatocellular Liver Cirrhosis Pathology Male Diagnosis, Differential Liver Retrospective Design Carcinoma, Hepatocellular Pathology Liver Neoplasms Pathology Female Liver Pathology Aged Artificial Intelligence Middle Age Contrast Media Pharmacodynamics Funding Source Aged: 65+ years Middle Aged: 45-64 years Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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