Evidence map›Paper›PMID 41222630›Full record

ReviewPediatric radiology2026

Pediatric pulmonary nodules: current state of knowledge, AI applications, and future directions.

Aki A Tanimoto, Cara E Morin, Andrew H Schapiro, Eric J Crotty, Andrew T Trout, Jonathan R Dillman, Russell C Hardie

Abstract readReview
In one paragraph

Review in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

7 authors.

Aki A TanimotoCincinnati Children's Hospital Medical Center, Cincinnati, United States. aki.tanimoto@cchmc.org.
Cara E MorinCincinnati Children's Hospital Medical Center, Cincinnati, United States.
Andrew H SchapiroCincinnati Children's Hospital Medical Center, Cincinnati, United States.
Eric J CrottyCincinnati Children's Hospital Medical Center, Cincinnati, United States.
Andrew T TroutCincinnati Children's Hospital Medical Center, Cincinnati, United States.
Jonathan R DillmanCincinnati Children's Hospital Medical Center, Cincinnati, United States.
Russell C HardieDepartment of Electrical and Computer Engineering, University of Dayton, Dayton, United States.

Funding

Pediatric-specific computer aided detection of pulmonary nodules in computed tomography scansR03EB036572 · NIBIB · UNIVERSITY OF DAYTON · PI Russell Hardie · 2025 to 2026
$150k
NIBIB NIH HHS R03 EB036572
6 · The paper itself

Abstract

Pulmonary nodules are commonly encountered in pediatric patients. While metastases make up the majority of malignant pulmonary nodules found in children, nodules are also frequently identified in healthy children. Currently, there are no definitive nodule features to differentiate benign from malignant nodules, and there are no guidelines for incidental nodule follow-up in children similar in scope to the 2017 Fleischner Society Guidelines for adults. In this review, we discuss the epidemiology, histology, and imaging findings of pulmonary nodules found in healthy children, pulmonary metastases, primary pediatric lung cancer, and other causes of pulmonary nodules in children. We also explore current applications of artificial intelligence for the evaluation of pediatric pulmonary nodules.

Indexed as

Artificial IntelligenceLung NeoplasmsMultiple Pulmonary NodulesSolitary Pulmonary NoduleTomography, X-Ray ComputedChildDiagnosis, DifferentialHumansArtificial intelligenceChildrenComputed tomographyMalignancyPulmonary nodules

Identifiers

PMID41222630
PMCPMC12831686

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.