Evidence map›Paper›PMID 39838946›Full record

ArticleAnnals of medicine2025

Prognostic significance of pathological response and lymph node status in neoadjuvant immunotherapy for potentially resectable non-small cell lung cancer.

Yuan Xu, Dongjie Ma, Yingzhi Qin, Hongsheng Liu

Abstract read
In one paragraph

Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

4 authors.

Yuan XuDepartment of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.
Dongjie MaDepartment of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.
Yingzhi QinDepartment of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.
Hongsheng LiuDepartment of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the predictive value of pathological response and lymph node status on progression-free survival (PFS) in patients with potentially resectable non-small cell lung cancer (NSCLC) receiving neoadjuvant immunotherapy.

methodsA retrospective analysis was conducted on 143 patients with potentially resectable NSCLC who underwent neoadjuvant immunotherapy followed by surgical resection. Pathological response, lymph node involvement, and clinical outcomes were comprehensively assessed using Kaplan-Meier analysis and Cox regression.

resultsBoth major pathological response (MPR) and complete pathological response (CPR) significantly correlated with improved PFS (

conclusionIn potentially resectable NSCLC, MPR and CPR demonstrate similar PFS benefits after neoadjuvant immunotherapy. Lymph node status significantly influences prognosis, even in initially unresectable cases. The proposed risk stratification provides a valuable tool for personalized management in this challenging patient population.

Indexed as

Carcinoma, Non-Small-Cell LungImmunotherapyLung NeoplasmsLymph NodesNeoadjuvant TherapyAdultAgedFemaleHumansKaplan-Meier EstimateLymphatic MetastasisMaleMiddle AgedPrognosisProgression-Free SurvivalRetrospective Studieslymph node involvementNeoadjuvant immunotherapyNSCLCpathologic responseprogression-free survivalrisk stratification

Identifiers

PMID39838946
PMCPMC11755730

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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.