Evidence map›Paper›PMID 40673067›Full record

ArticleBreathe (Sheffield, England)2025

Pulmonary function prediction in lung cancer resection candidates: the latest insights.

Riccardo Orlandi, Sara Degiovanni, Clarissa Uslenghi, Asia Anghinelli, Paola Besana, Alessandro Palleschi

Abstract read
In one paragraph

Article in Breathe (Sheffield, England), 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

6 authors.

Riccardo OrlandiDepartment of Thoracic Surgery, University of Milan, Milan, Italy.ORCID https://orcid.org/0000-0002-4113-1598
Sara DegiovanniDepartment of Thoracic Surgery, University of Milan, Milan, Italy.
Clarissa UslenghiDepartment of Thoracic Surgery, University of Milan, Milan, Italy.
Asia AnghinelliDepartment of Thoracic Surgery, University of Milan, Milan, Italy.
Paola BesanaDepartment of Thoracic Surgery, University of Milan, Milan, Italy.
Alessandro PalleschiDepartment of Thoracic Surgery, University of Milan, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Surgical resection remains the most effective curative option for early-stage nonsmall cell lung cancer (NSCLC) in fit patients. However, many individuals with NSCLC have comorbidities that elevate surgical risks and complicate postoperative recovery. These challenges necessitate thorough preoperative assessments to evaluate patient suitability and predict postoperative lung function. Pulmonary function tests (PFTs) and imaging modalities, including computed tomography (CT), ventilation-perfusion scintigraphy and ultrasound, are integral to these evaluations. Advances in technology, such as ventilation and perfusion single-photon emission computed tomography (SPECT)/CT, quantitative CT and magnetic resonance imaging (MRI) techniques, have enhanced the accuracy of postoperative predictions, offering valuable insights into respiratory mechanics and regional lung function. Despite these advancements, no comprehensive evaluation exists to establish the reliability of various prediction methods. This review explores the role of traditional and emerging preoperative tools in assessing lung resection candidates, emphasising their contributions to clinical decision-making. By improving the precision of postoperative lung function predictions, these tools not only optimise surgical outcomes but also support shared decision-making, balancing risks and patient preferences. Further refinement and integration of these methods promises to enhance the management of high-risk patients and advance the standard of care in thoracic surgery.

Identifiers

PMID40673067
PMCPMC12260915

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.