Evidence map›Paper›PMID 40149380›Full record

ArticleCancers2025

Diagnostic Performance of Combined Conventional CT Imaging Features and Radiomics Signature in Differentiating Grade 1 Tumors from Higher-Grade Pancreatic Neuroendocrine Neoplasms.

Florent Tixier, Felipe Lopez-Ramirez, Alejandra Blanco, Ammar A Javed, Linda C Chu, Ralph H Hruban, Mohammad Yasrab, Daniel Fadaei Fouladi, Shahab Shayesteh, Saeed Ghandili and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2025. 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. Review
  2. Article
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.

Florent TixierThe Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.ORCID 0000-0002-9668-6574
Felipe Lopez-RamirezThe Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.ORCID 0000-0002-1560-9172
Alejandra BlancoThe Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.ORCID 0000-0003-1856-5184
Ammar A JavedDepartment of Surgery, The NYU Grossman School of Medicine and NYU Langone Health, New York, NY 10016, USA.ORCID 0000-0002-5463-5250
Linda C ChuThe Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.ORCID 0000-0001-9729-2756
Ralph H HrubanThe Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, School of Medicine, Hopkins University, Baltimore, MD 21205, USA.
Mohammad YasrabThe Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.ORCID 0009-0007-8177-097X
Daniel Fadaei FouladiThe Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.ORCID 0000-0002-8582-8485
Shahab ShayestehThe Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
Saeed GhandiliThe Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.ORCID 0000-0001-6292-3212
Elliot K FishmanThe Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.ORCID 0000-0002-2567-1658
Satomi KawamotoThe Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.ORCID 0000-0002-3577-1388

Funding

Lustgarten Foundation for Pancreatic Cancer Research Not applicable
6 · The paper itself

Abstract

BACKGROUND/

objectivesAccurate identification of grade 1 (G1) pancreatic neuroendocrine tumors (PanNETs) is crucial due to their rising incidence and emerging nonsurgical management strategies. This study evaluated whether combining conventional CT imaging features, CT radiomics features, and clinical data improves differentiation of G1 PanNETs from higher-grade tumors (G2/G3 PanNETs and pancreatic neuroendocrine carcinomas [PanNECs]) compared to using these features individually.

methodsA retrospective analysis included 133 patients with pathologically confirmed PanNETs or PanNECs (70 males, 63 females; mean age, 58.5 years) who underwent pancreas protocol CT. A total of 28 conventional imaging features, 4892 radiomics features, and clinical data (age, gender, and tumor location) were analyzed using a support vector machine (SVM) model. Data were divided into 70% training and 30% testing sets.

resultsThe SVM model using the top 10 conventional imaging features (e.g., suspicious lymph nodes and hypoattenuating tumors) achieved 75% sensitivity, 81% specificity, and 79% accuracy for identifying higher-grade tumors (G2/G3 PanNETs and PanNECs). The top 10 radiomics features yielded 94% sensitivity, 46% specificity, and 69% accuracy. Combining all features (imaging, radiomics, and clinical data) improved performance, with 94% sensitivity, 69% specificity, 79% accuracy, and an F1-score of 0.77. The radiomics score demonstrated an AUC of 0.85 in the training and 0.83 in the testing set.

conclusionsConventional imaging features provided higher specificity, while radiomics offered greater sensitivity for identifying higher-grade tumors. Integrating all three features improved diagnostic accuracy, highlighting their complementary roles. This combined model may serve as a valuable tool for distinguishing higher-grade tumors from G1 PanNETs and potentially guiding patient management.

Indexed as

machine learning algorithmspancreatic neuroendocrine neoplasmpancreatic neuroendocrine tumorpathologic graderadiomicssupport vector machine modeltumor grade prediction

Identifiers

PMID40149380
PMCPMC11941307

What OpenQuestion holds

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Registered trials

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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.