Evidence map›Paper›PMID 40672750›Full record

ArticleFrontiers in oncology2025

The predictive value of total body PET/CT in high PD-L1 expression and immunotherapy in advanced non-small cell lung cancer patients.

Huibin Jin, Bingxin Hu, Jie Zhang, Ye Long, Ang Xuan, Xinyu Wu, Junling Xu, Yongju Gao

Abstract read
In one paragraph

Article in Frontiers in oncology, 2025. 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
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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

8 authors.

Huibin Jin *Department of Nuclear Medicine of Henan Provincial People's Hospital and The People's Hospital of Zhengzhou University, Zhengzhou, China.
Bingxin Hu *Department of Nuclear Medicine of Henan Provincial People's Hospital and The People's Hospital of Zhengzhou University, Zhengzhou, China.
Jie ZhangDepartment of Nuclear Medicine of Henan Provincial People's Hospital and The People's Hospital of Zhengzhou University, Zhengzhou, China.
Ye LongDepartment of Nuclear Medicine of Henan Provincial People's Hospital and The People's Hospital of Zhengzhou University, Zhengzhou, China.
Ang XuanDepartment of Nuclear Medicine of Henan Provincial People's Hospital and The People's Hospital of Zhengzhou University, Zhengzhou, China.
Xinyu WuDepartment of Nuclear Medicine of Henan Provincial People's Hospital and The People's Hospital of Zhengzhou University, Zhengzhou, China.
Junling XuDepartment of Nuclear Medicine of Henan Provincial People's Hospital and The People's Hospital of Zhengzhou University, Zhengzhou, China.
Yongju GaoDepartment of Nuclear Medicine of Henan Provincial People's Hospital and The People's Hospital of Zhengzhou University, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Total-body positron emission tomography/computed tomography (PET/CT) using uEXPLORER scanners demonstrates superior imaging capabilities for assessing programmed death ligand 1 (PD-L1) expression heterogeneity between primary tumors (PTs) and metastatic tumors (MTs) in advanced nonsmall cell lung cancer (NSCLC). Methods: This retrospective study of 99 treatment-naïve NSCLC patients revealed that metabolic parameters (SUVmax, SUR-L, and SUR-BP) derived from biopsy-correlated PET/CT sites significantly predicted high PD-L1 expression (TC ≥ 50% or IC ≥ 10% by IHC). Results: SUR-L exhibited the highest diagnostic accuracy (AUC = 0.758, Discussion: Our findings establish SUR-BP as a novel non-invasive biomarker for immunotherapy prognosis in NSCLC, addressing spatial heterogeneity challenges in PD-L1 assessment.

Indexed as

immunotherapyNSCLCPD-L1prognosisSUR

Identifiers

PMID40672750
PMCPMC12263409

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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.