Evidence map›Paper›PMID 38102442›Full record

ArticleAbdominal radiology (New York)2024

Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies.

Satomi Kawamoto, Zhuotun Zhu, Linda C Chu, Ammar A Javed, Benedict Kinny-Köster, Christopher L Wolfgang, Ralph H Hruban, Kenneth W Kinzler, Daniel Fadaei Fouladi, Alejandra Blanco and 2 more

Abstract read
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In one paragraph

Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
3.9field-weighted citation impact, top 6% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Artificial Intelligence in Pancreatic Imaging: A Systematic Review.United European gastroenterology journal · 2025
    Pooled it
  2. Article
  3. Article
  4. Optimized Spatial Transformer for Segmenting Pancreas Abnormalities.Journal of imaging informatics in medicine · 2025
    Article
  5. Article
  6. 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

12 authors at 1 institution in 2 countries.

Satomi KawamotoThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline Street, Baltimore, MD, 21287, USA. skawamo1@jhmi.edu.ORCID http://orcid.org/0000-0002-3577-1388
Zhuotun ZhuThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline Street, Baltimore, MD, 21287, USA.
Linda C ChuThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline Street, Baltimore, MD, 21287, USA.
Ammar A JavedDepartment of Surgery, School of Medicine, Johns Hopkins University, Blalock Building, 600 N. Wolfe Street, Baltimore, MD, 21287, USA.
Benedict Kinny-KösterDepartment of Surgery, School of Medicine, Johns Hopkins University, Blalock Building, 600 N. Wolfe Street, Baltimore, MD, 21287, USA.
Christopher L WolfgangDepartment of Surgery, School of Medicine, Johns Hopkins University, Blalock Building, 600 N. Wolfe Street, Baltimore, MD, 21287, USA.
Ralph H HrubanDepartment of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
Kenneth W KinzlerThe Ludwig Center, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21231, USA.
Daniel Fadaei FouladiThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline Street, Baltimore, MD, 21287, USA.
Alejandra BlancoThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline Street, Baltimore, MD, 21287, USA.
Shahab ShayestehThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline Street, Baltimore, MD, 21287, USA.
Elliot K FishmanThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline Street, Baltimore, MD, 21287, USA.
Johns Hopkins University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeDelay in diagnosis can contribute to poor outcomes in pancreatic ductal adenocarcinoma (PDAC), and new tools for early detection are required. Recent application of artificial intelligence to cancer imaging has demonstrated great potential in detecting subtle early lesions. The aim of the study was to evaluate global and local accuracies of deep neural network (DNN) segmentation of normal and abnormal pancreas with pancreatic mass.

methodsOur previously developed and reported residual deep supervision network for segmentation of PDAC was applied to segment pancreas using CT images of potential renal donors (normal pancreas) and patients with suspected PDAC (abnormal pancreas). Accuracy of DNN pancreas segmentation was assessed using DICE simulation coefficient (DSC), average symmetric surface distance (ASSD), and Hausdorff distance 95% percentile (HD95) as compared to manual segmentation. Furthermore, two radiologists semi-quantitatively assessed local accuracies and estimated volume of correctly segmented pancreas.

resultsForty-two normal and 49 abnormal CTs were assessed. Average DSC was 87.4 ± 3.1% and 85.5 ± 3.2%, ASSD 0.97 ± 0.30 and 1.34 ± 0.65, HD95 4.28 ± 2.36 and 6.31 ± 6.31 for normal and abnormal pancreas, respectively. Semi-quantitatively, ≥95% of pancreas volume was correctly segmented in 95.2% and 53.1% of normal and abnormal pancreas by both radiologists, and 97.6% and 75.5% by at least one radiologist. Most common segmentation errors were made on pancreatic and duodenal borders in both groups, and related to pancreatic tumor including duct dilatation, atrophy, tumor infiltration and collateral vessels.

conclusionPancreas DNN segmentation is accurate in a majority of cases, however, minor manual editing may be necessary; particularly in abnormal pancreas.

Indexed as

Carcinoma, Pancreatic DuctalPancreatic NeoplasmsArtificial IntelligenceHumansImage Processing, Computer-AssistedNeural Networks, ComputerPancreasTomography, X-Ray ComputedCTDeep neural network segmentationManual segmentationPancreasPancreatic ductal adenocarcinoma

Identifiers

PMID38102442
OpenAlexW4389781081

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.