Evidence map›Paper›PMID 38900043›Full record

ArticleRadiology. Artificial intelligence2024

Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.

Benjamin Hou, Sungwon Lee, Jung-Min Lee, Christopher Koh, Jing Xiao, Perry J Pickhardt, Ronald M Summers

Abstract readEvaluation StudyMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
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

13 citing papers in PubMed.

  1. Review
  2. AI-assisted ascites volumetry for prognostic stratification in HCC treated with Atezolizumab/Bevacizumab.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
    Article
  3. Article
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  5. Article
  6. Review
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  8. Review
  9. Article
  10. Subcutaneous edema segmentation on abdominal CT using multi-class labels and iterative annotation.International journal of computer assisted radiology and surgery · 2025
    Article
  11. Review
  12. Article
  13. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Benjamin HouFrom the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bldg 10, Rm 1C224, Bethesda, MD 20892-1182 (B.H., R.M.S.); Department of Radiology, The Catholic University of Korea, Seoul St. Mary's Hospital, Seoul, Korea (S.L.); Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.M.L.); Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Md (C.K.); Ping An Technology, Shenzhen, China (J.X.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (P.J.P.).ORCID 0000-0003-3968-1707
Sungwon LeeFrom the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bldg 10, Rm 1C224, Bethesda, MD 20892-1182 (B.H., R.M.S.); Department of Radiology, The Catholic University of Korea, Seoul St. Mary's Hospital, Seoul, Korea (S.L.); Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.M.L.); Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Md (C.K.); Ping An Technology, Shenzhen, China (J.X.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (P.J.P.).ORCID 0000-0002-1684-2996
Jung-Min LeeFrom the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bldg 10, Rm 1C224, Bethesda, MD 20892-1182 (B.H., R.M.S.); Department of Radiology, The Catholic University of Korea, Seoul St. Mary's Hospital, Seoul, Korea (S.L.); Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.M.L.); Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Md (C.K.); Ping An Technology, Shenzhen, China (J.X.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (P.J.P.).ORCID 0000-0003-1280-5909
Christopher KohFrom the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bldg 10, Rm 1C224, Bethesda, MD 20892-1182 (B.H., R.M.S.); Department of Radiology, The Catholic University of Korea, Seoul St. Mary's Hospital, Seoul, Korea (S.L.); Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.M.L.); Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Md (C.K.); Ping An Technology, Shenzhen, China (J.X.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (P.J.P.).ORCID 0000-0002-4755-5607
Jing XiaoFrom the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bldg 10, Rm 1C224, Bethesda, MD 20892-1182 (B.H., R.M.S.); Department of Radiology, The Catholic University of Korea, Seoul St. Mary's Hospital, Seoul, Korea (S.L.); Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.M.L.); Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Md (C.K.); Ping An Technology, Shenzhen, China (J.X.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (P.J.P.).ORCID 0000-0001-9615-4749
Perry J PickhardtFrom the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bldg 10, Rm 1C224, Bethesda, MD 20892-1182 (B.H., R.M.S.); Department of Radiology, The Catholic University of Korea, Seoul St. Mary's Hospital, Seoul, Korea (S.L.); Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.M.L.); Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Md (C.K.); Ping An Technology, Shenzhen, China (J.X.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (P.J.P.).ORCID 0000-0002-5534-8202
Ronald M SummersFrom the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bldg 10, Rm 1C224, Bethesda, MD 20892-1182 (B.H., R.M.S.); Department of Radiology, The Catholic University of Korea, Seoul St. Mary's Hospital, Seoul, Korea (S.L.); Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.M.L.); Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Md (C.K.); Ping An Technology, Shenzhen, China (J.X.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (P.J.P.).

Funding

Management and Treatment of Chronic Delta Hepatitis Infection and other Liver DiseasesZIADK075150 · NIDDK · NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES · PI KOH, CHRISTOPHER · 2019 to 2025
$2.8M
Computer Aided Detection for Radiologic ImagesZ01CL040004 · CLC · CLINICAL CENTER · PI SUMMERS, RONALD M. · 2003 to 2008
$41k
Computer Aided Detection for CT ColonographyZIACL040003 · CLC · CLINICAL CENTER · PI SUMMERS, RONALD M. · 2009 to 2025
$0k
Computer Aided Detection for Radiologic ImagesZIACL040004 · CLC · CLINICAL CENTER · PI SUMMERS, RONALD M. · 2009 to 2025
$0k
Intramural NIH HHS Z01 CL040004
6 · The paper itself

Abstract

Purpose To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and patients with ovarian cancer. Materials and Methods This retrospective study included contrast-enhanced and noncontrast abdominal-pelvic CT scans of patients with cirrhotic ascites and patients with ovarian cancer from two institutions, National Institutes of Health (NIH) and University of Wisconsin (UofW). The model, trained on The Cancer Genome Atlas Ovarian Cancer dataset (mean age [±SD], 60 years ± 11; 143 female), was tested on two internal datasets (NIH-LC and NIH-OV) and one external dataset (UofW-LC). Its performance was measured by the F1/Dice coefficient, SDs, and 95% CIs, focusing on ascites volume in the peritoneal cavity. Results On NIH-LC (25 patients; mean age, 59 years ± 14; 14 male) and NIH-OV (166 patients; mean age, 65 years ± 9; all female), the model achieved F1/Dice scores of 85.5% ± 6.1 (95% CI: 83.1, 87.8) and 82.6% ± 15.3 (95% CI: 76.4, 88.7), with median volume estimation errors of 19.6% (IQR, 13.2%-29.0%) and 5.3% (IQR: 2.4%-9.7%), respectively. On UofW-LC (124 patients; mean age, 46 years ± 12; 73 female), the model had a F1/Dice score of 83.0% ± 10.7 (95% CI: 79.8, 86.3) and median volume estimation error of 9.7% (IQR, 4.5%-15.1%). The model showed strong agreement with expert assessments, with

Indexed as

AscitesDeep LearningLiver CirrhosisOvarian NeoplasmsTomography, X-Ray ComputedAdultAgedFemaleHumansMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedRetrospective StudiesAbdomen/GICirrhosisDeep LearningSegmentation

Identifiers

PMID38900043
PMCPMC11449171

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