Evidence map›Paper›PMID 37652458›Full record

ArticleThe Journal of international medical research2023

Identifying the optimal cutoff point of Ki-67 in breast cancer: a single-center experience.

Wang Li, Ning Lu, Caiping Chen, Xiang Lu

Abstract read
In one paragraph

Article in The Journal of international medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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

4 authors.

Wang LiSchool of Graduate, Bengbu Medical College, Bengbu, China.
Ning LuDepartment of Pathology, Affiliated Hospital of Jiaxing University (the First Hospital of Jiaxing), Jiaxing, China.
Caiping ChenDepartment of Breast Surgery, Affiliated Hospital of Jiaxing University (the First Hospital of Jiaxing), Jiaxing, China.
Xiang LuSchool of Graduate, Bengbu Medical College, Bengbu, China.ORCID 0000-0002-7906-6701

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveKi-67 is associated with breast cancer subtypes, but the optimal cutoff point of Ki-67 has not been established in our center. We evaluated the cutoff point of Ki-67 in breast cancer and analyzed the associations among Ki-67, clinicopathological features, and prognosis.

methodsThe clinicopathological data and prognostic information of patients with breast cancer treated in our center were retrospectively collected, and the optimal cutoff point of Ki-67 was determined by univariate and multivariate survival risk analyses. The cutoff point was used to group the patients, and the differences in the clinicopathological features and prognosis were analyzed between the two groups.

resultsIn total, 609 patients with estrogen receptor-positive and human epidermal growth factor receptor 2-negative primary breast cancer were enrolled. The mean Ki-67 value was 22.3% ± 15.4%, the median was 20%, and a cutoff point of 30% was an independent factor influencing recurrence-free survival. When 30% was used as the cutoff point, patients with a Ki-67 value of ≤30% had a better prognosis and lower tumor malignancy.

conclusionThe optimal cutoff point of Ki-67 in breast cancer in our center is 30%.

Indexed as

Breast NeoplasmsFemaleHumansKi-67 AntigenMultivariate AnalysisRetrospective StudiesKi-67 Antigenbreast cancercutoff pointKi-67malignancymolecular subtypeprognosis

Identifiers

PMID37652458
PMCPMC10478558

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.