Evidence map›Paper›PMID 33552993›Full record

ArticleFrontiers in oncology2020

Pre-Treatment Tumor Growth Rate Predicts Clinical Outcomes of Patients With Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD-1/PD-L1 Therapy.

Li-Na He, Xuanye Zhang, Haifeng Li, Tao Chen, Chen Chen, Yixin Zhou, Zuan Lin, Wei Du, Wenfeng Fang, Yunpeng Yang and 4 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.6field-weighted citation impact, top 10% 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

16 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. 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

14 authors at 3 institutions in 1 country.

Li-Na HeState Key Laboratory of Oncology in South China, Guangzhou, China.
Xuanye ZhangState Key Laboratory of Oncology in South China, Guangzhou, China.
Haifeng LiState Key Laboratory of Oncology in South China, Guangzhou, China.
Tao ChenState Key Laboratory of Oncology in South China, Guangzhou, China.
Chen ChenState Key Laboratory of Oncology in South China, Guangzhou, China.
Yixin ZhouState Key Laboratory of Oncology in South China, Guangzhou, China.
Zuan LinState Key Laboratory of Oncology in South China, Guangzhou, China.
Wei DuState Key Laboratory of Oncology in South China, Guangzhou, China.
Wenfeng FangState Key Laboratory of Oncology in South China, Guangzhou, China.
Yunpeng YangState Key Laboratory of Oncology in South China, Guangzhou, China.
Yan HuangState Key Laboratory of Oncology in South China, Guangzhou, China.
Hongyun ZhaoState Key Laboratory of Oncology in South China, Guangzhou, China.
Shaodong HongState Key Laboratory of Oncology in South China, Guangzhou, China.
Li ZhangState Key Laboratory of Oncology in South China, Guangzhou, China.
State Key Laboratory of Oncology in South ChinaSun Yat-sen University Cancer Center · CNSun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumor growth rate (TGR; percent size change per month [%/m]) is postulated as an early radio-graphic predictor of response to anti-cancer treatment to overcome limitations of RECIST. We aimed to evaluate the predictive value of pre-treatment TGR (TGR

Indexed as

anti-PD-1/PD-L1 therapyimmunotherapynon-small cell lung cancerNSCLCprogression-free survivaltumor growth rate

Identifiers

PMID33552993
PMCPMC7863973
OpenAlexW3123718048

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.