Evidence map›Paper›PMID 41251737›Full record

ArticleAbdominal radiology (New York)2026

Feasibility of deep learning-accelerated HASTE-FS for pancreatic cystic lesion surveillance: comparison with conventional HASTE and MRCP.

Linda Le, Luke A Ginocchio, Sooah Kim, Hersh Chandarana, Jessica T Lovett, Chenchan Huang

Abstract readComparative Study
In one paragraph

Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

6 authors.

Linda LeDepartment of Radiology, NYU Grossman School of Medicine, NYU Langone Health, NY, New York, USA. linda.le2@nyulangone.org.
Luke A GinocchioDepartment of Radiology, NYU Grossman School of Medicine, NYU Langone Health, NY, New York, USA.
Sooah KimDepartment of Radiology, NYU Grossman School of Medicine, NYU Langone Health, NY, New York, USA.
Hersh ChandaranaDepartment of Radiology, NYU Grossman School of Medicine, NYU Langone Health, NY, New York, USA.
Jessica T LovettDepartment of Internal Medicine, NYU Grossman School of Medicine, NYU Langone Health, NY, New York, USA.
Chenchan HuangDepartment of Radiology, NYU Grossman School of Medicine, NYU Langone Health, NY, New York, USA.

Funding

TR&D 4: Revealing Microstructure: Biophysical modeling and validation for discovery and clinical careP41EB017183 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Hersh Chandarana · 2014 to 2026
$19.3M
NIBIB NIH HHS P41 EB017183
6 · The paper itself

Abstract

purposePancreatic cystic lesions (PCL) commonly undergo surveillance using MRI with MR cholangiopancreatography (MRCP). Our objective is to compare the performance of a single-shot fat-saturated T2-weighted technique with deep-learning reconstruction (DL HASTE-FS) to a conventional T2-weighted Half fourier Single-shot Turbo spin-Echo (HASTE) sequence and to MRCP for the purpose of PCL detection, characterization, and surveillance.

methodsIn this retrospective study, 91 consecutive patients underwent 3T abdominal MRI with MRCP protocol including DL HASTE-FS and conventional HASTE between 8/2/2023 and 10/3/2023. Three abdominal radiologists rated overall and lesion-specific image quality on a 5-point Likert scale, including pancreatic margin and duct sharpness, and PCL conspicuity. A subset of 70 preselected index PCLs were evaluated for cyst features, confidence of diagnosing side-branch IPMN, and suitability of DL HASTE-FS in replacing MRCP for PCL surveillance.

resultsDL HASTE-FS received higher scores for pancreatic duct border sharpness (4.1 vs. 3.9; p = .004), pancreatic duct visibility compared to MRCP (2.0 vs. 1.9; p = .04), cyst conspicuity (4.4 vs. 3.9; p < .001), and sharpness of cyst wall and internal septations (4.3 vs. 3.7; p < .001) compared to conventional HASTE. In contrast, conventional HASTE received higher scores for pancreatic margin sharpness (4.2 vs. 3.8; p < .001) and peripancreatic vessel clarity (4.2 vs. 3.4; p < .001). For the 70 preselected index PCLs, readers visualized more PCLs and had higher confidence in diagnosing SB-IPMN on DL HASTE-FS than on conventional HASTE (3.6 vs. 3.4; p < .001). Finally, DL HASTE-FS was deemed a suitable replacement to MRCP for more cases than conventional HASTE (83% vs. 48%; p < .001).

conclusionDL HASTE-FS outperforms conventional HASTE for PCL detection and characterization, and is a suitable alternative to 3D MRCP in the context of PCL surveillance, potentially reducing exam time and cost.

Indexed as

Cholangiopancreatography, Magnetic ResonanceDeep LearningImage Interpretation, Computer-AssistedMagnetic Resonance ImagingPancreatic CystPancreatic NeoplasmsAdultAgedAged, 80 and overFeasibility StudiesFemaleHumansMaleMiddle AgedRetrospective StudiesDeep learning, accelerated MRIMRCPPancreatic cystic lesionsPCL surveillance, pancreatic intraductal papillary mucinous neoplasm (IPMN)

Identifiers

PMID41251737
PMCPMC13032782

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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.