Evidence map›Paper›PMID 30310292›Full record

ArticleOncoTargets and therapy2018

Predictive value of LDH kinetics in bevacizumab treatment and survival of patients with advanced NSCLC.

Butuo Li, Cheng Li, Meiying Guo, Shuheng Shang, Xiaogang Li, Peng Xie, Xindong Sun, Jinming Yu, Linlin Wang

Open access · goldAbstract read
In one paragraph

Article in OncoTargets and therapy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.8field-weighted citation impact, top 14% of its field
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

18 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Article
  3. The serum LDH level and KELIM scores are potential predictors of a benefit from bevacizumab first-line therapy for patients with advanced ovarian cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
    Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Review
  14. Review
  15. Article
  16. Article
  17. Article
  18. Review
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

9 authors at 3 institutions in 1 country.

Butuo LiDepartment of Radiation Oncology and Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University, Tianjin, People's Republic of China, sdyujinming@163.com.
Cheng LiDepartment of Dean's Office, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, Jinan, People's Republic of China.
Meiying GuoDepartment of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, Jinan, People's Republic of China, wanglinlinatjn@163.com, sdyujinming@163.com.
Shuheng ShangDepartment of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, Jinan, People's Republic of China, wanglinlinatjn@163.com, sdyujinming@163.com.
Xiaogang LiDepartment of Radiation Oncology, School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan, People's Republic of China.
Peng XieDepartment of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, Jinan, People's Republic of China, wanglinlinatjn@163.com, sdyujinming@163.com.
Xindong SunDepartment of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, Jinan, People's Republic of China, wanglinlinatjn@163.com, sdyujinming@163.com.
Jinming YuDepartment of Radiation Oncology and Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University, Tianjin, People's Republic of China, sdyujinming@163.com.
Linlin WangDepartment of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, Jinan, People's Republic of China, wanglinlinatjn@163.com, sdyujinming@163.com.
Shandong First Medical University · CNShandong University · CNTianjin Medical University Cancer Institute and Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe combination of bevacizumab and chemotherapy is still one of the standard treatments for advanced non-small-cell lung cancer (NSCLC) patients in the new era of targeted therapy. Although a high level of baseline lactate dehydrogenase (LDH) was found to predict survival benefit from bevacizumab in patients with metastatic colorectal cancer, the predictive value of serum level of LDH in NSCLC patients treated with bevacizumab has not been investigated yet. Moreover, dynamic evaluation of serum level of LDH changes may be more informative and promising in predicting patients' prognosis. We thus sought to analyze LDH kinetics and evaluate its predictive role in the response and survival of advanced NSCLC patients treated with bevacizumab.

methodWe retrospectively collected and analyzed a total of 161 advanced NSCLC patients who had undergone treatment with bevacizumab. Univariate and multivariate logistic regression analyses of serum level of LDH were used for response analyses, and Cox models for both overall survival (OS) and progression-free survival analyses (PFS). Longitudinal analysis of LDH was performed using a mixed-effect regression model.

resultsOn multivariate Cox models, increase of serum level of LDH after 4 cycles with bevacizumab (INC4) treatment was shown to be the independent risk factor for OS (hazard ratio =2.17, 95% CI: 1.21-3.90,

conclusionDynamic changes of LDH were superior to baseline LDH in predicting prognosis of NSCLC patients treated with bevacizumab. Serum level of LDH reducing over time was a potential biomarker for patients to achieve good clinical response (CR/PR) to bevacizumab.

Indexed as

bevacizumablactate dehydrogenasemixed-effect modelnon-small-cell lung cancerpredictive factor

Identifiers

PMID30310292
PMCPMC6166744
OpenAlexW2893495784

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.