Evidence map›Paper›PMID 41710038›Full record

ArticleCancer management and research2026

Independent Risk Factors and Nomogram-Based Prediction of Pulmonary Fungal Infection in Lung Cancer Inpatients: A Single-Center Retrospective Study.

Yanran Xu, Yan Chen

Abstract read
In one paragraph

Article in Cancer management and research, 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

2 authors.

Yanran XuDepartment of Respiratory and Critical Care Medicine, The Second Clinical Medical College of North Sichuan Medical College, Nanchong, Sichuan, 637000, People's Republic of China.ORCID 0009-0005-4502-4609
Yan ChenDepartment of Respiratory and Critical Care Medicine, The Second Clinical Medical College of North Sichuan Medical College, Nanchong, Sichuan, 637000, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To investigate independent risk factors and construct an internally validated risk prediction model for invasive pulmonary fungal infection (IPFI) in patients with lung cancer. Patients and Methods: Clinical data from 250 consecutive lung cancer inpatients admitted to Nanchong Central Hospital between February 2022 and March 2025 were retrospectively analyzed; 41 patients developed IPFI and 209 did not. Patients were randomly assigned to a training set (n=175) and a validation set (n=75) at a 7:3 ratio. Candidate predictors were screened by univariate logistic regression, reduced using least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation, and entered into multivariable logistic regression to construct a nomogram. Model performance was evaluated using bootstrap internal validation (1000 resamples), calibration curves and goodness-of-fit testing, receiver operating characteristic analysis, and decision curve analysis. Results: Diabetes mellitus, invasive procedures, systemic glucocorticoid use, lower CD4+ T-cell count, and length of hospital stay >14 days were associated with IPFI and were retained as independent predictors in the final model. The model showed good discrimination, with an area under the curve of 0.876 (95% CI: 0.809-0.944) in the training set and 0.861 (95% CI: 0.750-0.973) in the validation set, and demonstrated clinical net benefit across threshold probability ranges of 0.03-0.90 (training) and 0.04-0.78 (validation). Conclusion: This nomogram may support early risk stratification for IPFI among lung cancer inpatients, while confirmation in external, multi-center cohorts is needed before broader clinical application.

Indexed as

invasive pulmonary fungal infectionlung cancernomogramprediction modelrisk factors

Identifiers

PMID41710038
PMCPMC12912138

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.