Evidence map›Paper›PMID 40897832›Full record

ArticleJournal of imaging informatics in medicine2026

A Pilot Study on Quantitative Accuracy and Radiomic Feature Stability of Deep Progressive Learning Reconstruction in NSCLC PET.

Takuro Shiiba, Takeru Abe, Masanori Watanabe

Abstract read
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In one paragraph

Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Takuro ShiibaDepartment of Molecular Imaging, Clinical and Educational Collaboration Unit, School of Medical Sciences, Fujita Health University, Toyoake, Aichi, Japan. takuro.shiiba@fujita-hu.ac.jp.
Takeru AbeDepartment of Radiology, Tsujimura Surgical Hospital, Kariya, Aichi, Japan.
Masanori WatanabeDepartment of Radiology, Fujita Health University Hospital, Toyoake, Aichi, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep progressive learning reconstruction (DPR) is a novel deep learning-based algorithm for PET imaging, yet its impact on quantitative metrics and radiomic feature stability is not fully characterized. This preliminary study systematically evaluated DPR against conventional ordered-subset expectation maximization (OSEM) in non-small cell lung cancer (NSCLC) PET imaging. In this retrospective study of 24 NSCLC patients, PET data were reconstructed using OSEM and three DPR strength levels. We compared standardized uptake values (SUV), contrast-to-noise ratio (CNR), and background noise. As a secondary objective, the stability of 93 radiomic features was quantified using an intra-patient coefficient of variation (COV

Indexed as

Carcinoma, Non-Small-Cell LungDeep LearningImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedLung NeoplasmsPositron-Emission TomographyAgedAlgorithmsFemaleHumansMaleMiddle AgedPilot ProjectsRadiomicsReproducibility of ResultsRetrospective StudiesDeep progressive learning reconstructionNon-small-cell lung cancerOrdered-subset expectation maximizationRadiomicsStandardized uptake value

Identifiers

PMID40897832
PMCPMC13230344

What OpenQuestion holds

Textmetadata
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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.