Evidence map›Paper›PMID 36204530›Full record

ArticleRadiology. Artificial intelligence2022

Fully Automated and Explainable Liver Segmental Volume Ratio and Spleen Segmentation at CT for Diagnosing Cirrhosis.

Sungwon Lee, Daniel C Elton, Alexander H Yang, Christopher Koh, David E Kleiner, Meghan G Lubner, Perry J Pickhardt, Ronald M Summers

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
5.0field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 37 citations in OpenAlex.

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  13. [Characteristics of liver volume and pathological changes with different stages of liver fibrosis in chronic liver disease].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2024
    Article
  14. Review
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  20. Opportunistic Screening:Radiology · 2023
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Sungwon LeeImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Building 10, Room 1C224D, Bethesda, MD 20892-1182 (S.L., D.C.E., R.M.S.); Liver Diseases Branch, Intramural Research Program, National Institute of Diabetes and Digestive and Kidney Diseases (A.H.Y., C.K.), and Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.K.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (M.G.L., P.J.P.).ORCID https://orcid.org/0000-0002-1684-2996
Daniel C EltonImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Building 10, Room 1C224D, Bethesda, MD 20892-1182 (S.L., D.C.E., R.M.S.); Liver Diseases Branch, Intramural Research Program, National Institute of Diabetes and Digestive and Kidney Diseases (A.H.Y., C.K.), and Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.K.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (M.G.L., P.J.P.).ORCID 0000-0003-0249-1387
Alexander H YangImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Building 10, Room 1C224D, Bethesda, MD 20892-1182 (S.L., D.C.E., R.M.S.); Liver Diseases Branch, Intramural Research Program, National Institute of Diabetes and Digestive and Kidney Diseases (A.H.Y., C.K.), and Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.K.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (M.G.L., P.J.P.).
Christopher KohImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Building 10, Room 1C224D, Bethesda, MD 20892-1182 (S.L., D.C.E., R.M.S.); Liver Diseases Branch, Intramural Research Program, National Institute of Diabetes and Digestive and Kidney Diseases (A.H.Y., C.K.), and Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.K.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (M.G.L., P.J.P.).ORCID 0000-0002-4755-5607
David E KleinerImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Building 10, Room 1C224D, Bethesda, MD 20892-1182 (S.L., D.C.E., R.M.S.); Liver Diseases Branch, Intramural Research Program, National Institute of Diabetes and Digestive and Kidney Diseases (A.H.Y., C.K.), and Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.K.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (M.G.L., P.J.P.).ORCID 0000-0003-3442-4453
Meghan G LubnerImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Building 10, Room 1C224D, Bethesda, MD 20892-1182 (S.L., D.C.E., R.M.S.); Liver Diseases Branch, Intramural Research Program, National Institute of Diabetes and Digestive and Kidney Diseases (A.H.Y., C.K.), and Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.K.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (M.G.L., P.J.P.).ORCID 0000-0003-3788-2871
Perry J Pickhardt *Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Building 10, Room 1C224D, Bethesda, MD 20892-1182 (S.L., D.C.E., R.M.S.); Liver Diseases Branch, Intramural Research Program, National Institute of Diabetes and Digestive and Kidney Diseases (A.H.Y., C.K.), and Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.K.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (M.G.L., P.J.P.).ORCID 0000-0002-5534-8202
Ronald M Summers *Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Building 10, Room 1C224D, Bethesda, MD 20892-1182 (S.L., D.C.E., R.M.S.); Liver Diseases Branch, Intramural Research Program, National Institute of Diabetes and Digestive and Kidney Diseases (A.H.Y., C.K.), and Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.K.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (M.G.L., P.J.P.).ORCID 0000-0001-8081-7376
National Institute of Diabetes and Digestive and Kidney Diseases · USUniversity of Wisconsin–Madison · USNational Institutes of Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To evaluate the performance of a deep learning (DL) model that measures the liver segmental volume ratio (LSVR) (ie, the volumes of Couinaud segments I-III/IV-VIII) and spleen volumes from CT scans to predict cirrhosis and advanced fibrosis. Materials and Methods: For this Health Insurance Portability and Accountability Act-compliant, retrospective study, two datasets were used. Dataset 1 consisted of patients with hepatitis C who underwent liver biopsy (METAVIR F0-F4, 2000-2016). Dataset 2 consisted of patients who had cirrhosis from other causes who underwent liver biopsy (Ishak 0-6, 2001-2021). Whole liver, LSVR, and spleen volumes were measured with contrast-enhanced CT by radiologists and the DL model. Areas under the receiver operating characteristic curve (AUCs) for diagnosing advanced fibrosis (≥METAVIR F2 or Ishak 3) and cirrhosis (≥METAVIR F4 or Ishak 5) were calculated. Multivariable models were built on dataset 1 and tested on datasets 1 (hold out) and 2. Results: Datasets 1 and 2 consisted of 406 patients (median age, 50 years [IQR, 44-56 years]; 297 men) and 207 patients (median age, 50 years [IQR, 41-57 years]; 147 men), respectively. In dataset 1, the prediction of cirrhosis was similar between the manual versus automated measurements for spleen volume (AUC, 0.86 [95% CI: 0.82, 0.9] vs 0.85 [95% CI: 0.81, 0.89]; significantly noninferior, Conclusion: The CT-based DL model performed similarly to radiologists. LSVR and splenic volume were predictive of advanced fibrosis and cirrhosis.

Indexed as

CirrhosisComputer Applications-Detection/DiagnosisCTLiver

Identifiers

PMID36204530
PMCPMC9530761
OpenAlexW4293005121

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.