Evidence map›Paper›PMID 41645520›Full record

ReviewCurrent opinion in pulmonary medicine2026

Improving lung cancer screening diagnostic efficiency.

Christopher R Caruso, Roger Y Kim

Abstract readReview
In one paragraph

Review in Current opinion in pulmonary medicine, 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
  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

2 authors.

Christopher R CarusoDivision of Pulmonary, Allergy, and Critical Care, Department of Medicine.
Roger Y KimDivision of Pulmonary, Allergy, and Critical Care, Department of Medicine.

Funding

Assessment of a Radiomics-Based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary NodulesK08CA279881 · NCI · UNIVERSITY OF PENNSYLVANIA · PI Roger Yeon-Kyu Kim · 2023 to 2026
$998k
NCI NIH HHS K08 CA279881
6 · The paper itself

Abstract

purpose of reviewWe discuss opportunities to improve lung cancer screening (LCS) diagnostic efficiency, which necessitates simultaneous focus on both diagnosis of early-stage lung cancer and reduction of diagnostic errors during diagnostic evaluation. RECENT

findingsRecent efforts have focused on three distinct targets for improving LCS diagnostic efficiency: Eligibility and uptake, Adherence to annual screening, and Diagnostic evaluation of concerning findings. There has been ongoing debate regarding who should be screened and how to consider lung cancer risk factors, even as LCS uptake remains suboptimal. LCS annual adherence has emerged as an important quality metric, as it is associated with increased early-stage lung cancer detection. Finally, optimization of the diagnostic pathway once concerning findings are identified via LCS is necessary to minimize exposing those without cancer to the harms of invasive diagnostic testing. Efforts to improve pulmonary nodule risk assessment, the nonmalignant resection rate, and lung cancer overdiagnosis will be crucial. SUMMARY: Improving LCS diagnostic efficiency requires a careful balance between prioritizing lung cancer sensitivity (i.e., ability to diagnose early-stage lung cancers) and avoiding both false negatives (i.e., failure to diagnose an early-stage lung cancer) and false positives (i.e., unnecessary performance of invasive testing for benign lesions).

Indexed as

Early Detection of CancerLung NeoplasmsMass ScreeningHumansOverdiagnosisRisk AssessmentRisk Factorsdiagnostic efficiencyhealth services researchlung cancer screeningquality

Identifiers

PMID41645520
PMCPMC12908840

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.