Evidence map›Paper›PMID 40686740›Full record

ArticleAdvances in radiation oncology2025

Impact of Normal Lung Volume Choices on Radiation Pneumonitis Risk Prediction in Locally Non-small Cell Lung Cancer Radiation Therapy.

Alyssa Gadsby, Tian Liu, Robert Samstein, Jiahan Zhang, Yang Lei, Kenneth E Rosenzweig, Ming Chao

Abstract read
In one paragraph

Article in Advances in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Alyssa GadsbyDepartment of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York.
Tian LiuDepartment of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York.
Robert SamsteinDepartment of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York.
Jiahan ZhangDepartment of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York.
Yang LeiDepartment of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York.
Kenneth E RosenzweigDepartment of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York.
Ming ChaoDepartment of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aims to evaluate the impact of varying definitions of normal lung volume on the prediction of radiation pneumonitis (RP) risk in patients with locally advanced non-small cell lung cancer undergoing radiation therapy. Methods and Materials: Dosimetric variables V20, V5, and mean lung dose (MLD) were extracted from the treatment plans of 442 patients enrolled in the NRG Oncology Radiation Therapy Oncology Group 0617 trial. Three different definitions of lung volume were evaluated: total lung excluding gross tumor target, total lung excluding clinical target volume, and total lung excluding planning target volume (TL-PTV). Patients were grouped as "no-RP2" ( Results: Statistical analyses showed that V20 and MLD were significantly associated with RP2, while differences among the lung volume definitions were not statistically significant. Both k-nearest neighbor and eXtreme Gradient Boosting classifiers consistently yielded higher area under the receiver operating characteristic curve values for the TL-PTV definition compared to the other definitions, a finding supported by bootstrap analysis. SHAP analysis further indicated that V20 and MLD were the most influential predictors of RP2. Conclusions: In line with previous studies, both statistical analysis and SHAP interpretation confirmed that V20 and MLD were associated with RP2. The machine learning models indicated that defining normal lung volume as TL-PTV yielded the highest predictive performance for RP2 risk. Further validation using external data sets are warranted to confirm these findings.

Identifiers

PMID40686740
PMCPMC12272110

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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.