Evidence map›Paper›PMID 38681577›Full record

ReviewHeliyon2024

Prognostic models for immunotherapy in non-small cell lung cancer: A comprehensive review.

Siqi Ni, Qi Liang, Xingyu Jiang, Yinping Ge, Yali Jiang, Lingxiang Liu

Abstract readReview
In one paragraph

Review in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. [Recent Advances in Peripheral Immunoscore in Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025
    Review
  6. Article
  7. Article
  8. Review
  9. 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

6 authors.

Siqi NiDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Qi LiangDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Xingyu JiangDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Yinping GeThe Friendship Hospital of Ili Kazakh Autonomous Prefecture Ili & Jiangsu Joint Institute of Health, Yining 835000, Xinjiang Uygur Autonomous Regio, China.
Yali JiangThe Friendship Hospital of Ili Kazakh Autonomous Prefecture Ili & Jiangsu Joint Institute of Health, Yining 835000, Xinjiang Uygur Autonomous Regio, China.
Lingxiang LiuDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The introduction of immune checkpoint inhibitors (ICIs) has revolutionized the treatment of lung cancer. Given the limited clinical benefits of immunotherapy in patients with non-small cell lung cancer (NSCLC), various predictors have been shown to significantly influence prognosis. However, no single predictor is adequate to forecast patients' survival benefit. Therefore, it's imperative to develop a prognostic model that integrates multiple predictors. This model would be instrumental in identifying patients who might benefit from ICIs. Retrospective analysis and small case series have demonstrated the potential role of these models in prognostic prediction, though further prospective investigation is required to evaluate more rigorously their application in these contexts. This article presents and summarizes the latest research advancements on immunotherapy prognostic models for NSCLC from multiple omics perspectives and discuss emerging strategies being developed to enhance the domain.

Indexed as

Immune checkpoint inhibitorsNon-small cell lung cancerPrognostic model

Identifiers

PMID38681577
PMCPMC11053285

What OpenQuestion holds

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LicenceCC BY
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.