Evidence map›Paper›PMID 34745955›Full record

SynthesisFrontiers in oncology2021

Comparison of Efficacy and Safety of Third-Line Treatments for Advanced Gastric Cancer: A Systematic Review With Bayesian Network Meta-Analysis.

Miao Huang, Jisheng Li, Xuejun Yu, Qian Xu, Xue Zhang, Xin Dai, Song Li, Lei Sheng, Kai Huang, Lian Liu

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 3 pooled it
2.2field-weighted citation impact, top 11% 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

7 citing papers in PubMed, 3 syntheses or guidelines pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
  5. Article
  6. Article
  7. 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

10 authors at 3 institutions in 1 country.

Miao HuangDepartment of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Jisheng LiDepartment of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Xuejun YuDepartment of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Qian XuDepartment of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Xue ZhangDepartment of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Xin DaiDepartment of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Song LiDepartment of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Lei ShengDepartment of Thyroid Surgery, General Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Kai HuangDepartment of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Lian LiuDepartment of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Qilu Hospital of Shandong University · CNShandong Provincial Hospital · CNShandong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough various third-line treatments of advanced gastric cancer (AGC) significantly improved the overall survival, the optimal regimen has not been determined by now. This study aims to evaluate the efficacy and safety of multiple third-line treatments of AGC

methodsBy searching the databases of PubMed, Embase and the Cochrane Central Register of Controlled Trials from Jan 01, 2005 to Dec 31, 2020, we included phase II/III randomized clinical trials (RCTs) of the third-line treatments for AGC to perform NMA. The main outcomes for NMA were median overall survival (mOS), median progression-free survival (mPFS), disease control rate (DCR) and adverse events (AEs). We also included phase IB/II non-RCTs and II/III RCTs of the third-line immune checkpoint inhibitors (ICIs) for integrated analysis for pooled mOS (POS), pooled mPFS (PPFS) and other outcomes.

resultsEight phase II/III RCTs and 2 ICIs-related phase IB/II non-RCTs were included for analysis, involving 9 treatment regimens and 3012 AGC patients. In terms of mOS, apatinib (hazard ratio [HR] 0.61, 95% credible interval [CrI] 0.48-0.78) and nivolumab (HR 0.62, 95% CrI 0.51-0.76) were the most effective treatments compared with placebo. Apatinib also significantly improved mPFS versus placebo (HR 0.38, 95% CrI 0.29-0.49). Nivolumab ranked first among all regimens for 1-year OS rate and achieved the best OS in patients with HER-2 positive tumor, patients with gastroesophageal junction (GEJ) cancer and patients without gastrectomy history. TAS-102 (OR 7.46, 95% CrI 4.61-12.51) was the most toxic treatment in terms of AEs of grade 3 and higher (≥3 AEs). Pembrolizumab was more likely to cause immune related adverse event. Finally, the POS, pooled 1-year OS rate, pooled ORR and PPFS of AGC patients treated with third-line ICIs were 5.1 months, 25%, 10% and 1.71 months respectively.

conclusionsApatinib and nivolumab are the most effective treatments for the third-line treatment of AGC in contrast to the third-line chemotherapy. For AGC patients with HER-2 positive tumor, patients with GEJ cancer and patients without gastrectomy history, ICIs could be the optimal third-line treatment choice.

Indexed as

anti-angiogenic therapychemotherapygastric cancerimmune checkpoint inhibitorsnetwork meta-analysisthird-line

Identifiers

PMID34745955
PMCPMC8570109
OpenAlexW3209917967

What OpenQuestion holds

Textmetadata
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.