A Meta-Analysis of Deep-Learning-Based Fusion of Positron Emission Tomography and Computed Tomography in Oncologic Imaging.

Ghazal Mehri-Kakavand, Sibusiso Mdletshe, Mohamad Pursamimi, Alan Wang

Abstract readReview

In one paragraph

Review 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

4 authors.

Ghazal Mehri-KakavandDepartment of Anatomy and Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand. gmeh097@aucklanduni.ac.nz.ORCID http://orcid.org/0000-0002-8053-8032

Sibusiso MdletsheDepartment of Anatomy and Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.

Mohamad PursamimiCancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Alan WangDepartment of Anatomy and Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The objective of the study is to assess whether deep-learning fusion of positron emission tomography (PET) and computed tomography (CT) improves performance compared with matched unimodal PET and CT and whether different fusion architectures are associated with model performance. Five databases were searched (January 2018-8 May 2025) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. Studies were classified by clinical task, fusion level (early, intermediate, late, or hybrid), and fusion mechanism (fixed or learnable). Incremental and absolute performance were synthesised using robust variance estimation with publication-level clustering. Of 892 records, 103 studies (133 analytical units) were included; 30 units entered incremental synthesis, and 61 entered absolute synthesis (60 weighted). PET/CT fusion showed positive effects for Dice similarity coefficient (mean difference (MD) = 0.044; 95% confidence interval (CI), 0.027-0.060), area under the receiver operating characteristic curve (AUC; MD = 0.090; 95% CI, 0.036-0.144), and concordance index (C-index; MD = 0.038; 95% CI, 0.037-0.038); Dice and C-index were exploratory. Incremental I

Indexed as

Deep learningMeta-analysisMultimodal fusionOncologic imagingPET/CTSystematic review

Identifiers

PMID42771114

DOI10.1002/mp.13331

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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.