Evidence map›Paper›PMID 39263028›Full record

ArticleTranslational lung cancer research2024

Evaluation of the novel International Association for the Study of Lung Cancer grading system in adenocarcinoma with spread through air space.

Kuan Xu, Yilv Lv, Tangbing Chen, Yuchen Han, Hanqing Huang, Hong Yu, Bo Ye

Abstract read
In one paragraph

Article in Translational lung cancer research, 2024. 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
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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.

Kuan XuDepartment of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yilv LvDepartment of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Tangbing ChenDepartment of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yuchen HanDepartment of Pathology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Hanqing HuangDepartment of Thoracic Surgery, Maoming People's Hospital, Maoming, China.
Hong YuDepartment of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Bo YeDepartment of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The International Association for the Study of Lung Cancer (IASLC) pathology panel has proposed a new grading system for invasive lung adenocarcinoma (LADC). This study aims to validate this novel grading system for invasive LADC using propensity score matching (PSM), with a specific focus on patients exhibiting spread through air space (STAS). Methods: We retrospectively analyzed the clinicopathologic features of a large cohort of 910 non-mucinous LADCs with STAS from 2017 to 2020 and classified them according to the novel grading system. We applied PSM to adjust for potential confounders between the grading groups. Kaplan-Meier and Cox proportional hazards models were adopted for prognostic evaluation. Results: The results showed that the IASLC grading system (grades 2 and 3) stratified well in terms of recurrence-free survival (RFS) and overall survival (OS) (P Conclusions: This study suggests that the new IASLC grading system is a valuable prognostic tool for patients with STAS-positive LADC.

Indexed as

Histological grading systeminvasive lung adenocarcinoma (invasive LADC)spread through air space (STAS)

Identifiers

PMID39263028
PMCPMC11384489

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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.