Evidence map›Paper›PMID 30123969›Full record

ArticleCancer medicine2018

A correlation research of Ki67 index, CT features, and risk stratification in gastrointestinal stromal tumor.

Huali Li, Gang Ren, Rong Cai, Jian Chen, Xiangru Wu, Jianxi Zhao

Open access · goldAbstract read
In one paragraph

Article in Cancer medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
4.5field-weighted citation impact, top 5% 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

39 citing papers in PubMed, 60 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Trial
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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 at 3 institutions in 1 country.

Huali LiDepartment of Radiology, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China.
Gang RenDepartment of Radiology, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China.ORCID 0000-0003-2455-9741
Rong CaiDepartment of Radiotherapy, Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China.
Jian ChenDepartment of Radiology, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China.
Xiangru WuDepartment of Pathology, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China.
Jianxi ZhaoDepartment of Radiology, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China.
Shanghai Jiao Tong University · CNRuijin Hospital · CNXinHua Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesRecurrence and metastasis are the most important factors affecting the quality of life and survival rate of patients with gastrointestinal stromal tumors (GISTs). Accurate preoperative determination of the malignant degree of GISTs and the development of a reasonable treatment plan can effectively reduce the recurrence rate. CT is currently considered the preferred imaging modality for initial assessment. Until now, there have only been a few studies investigating the relationship between CT features and recurrence of GISTs. However, the value of CT features in prognostic assessment is still unclear. In this study, we attempted to investigate the prognostic significance of CT features and the Ki67 index in GISTs.

methodsWe retrospectively analyzed the clinicopathological and imaging data for 151 patients with a histopathological diagnosis of GIST who had received contrast-enhanced CT examination and surgical resection at XinHua Hospital from October 2008 to December 2015 or Sir Run Run Shaw Hospital in 2017. Then, we explored the correlation among CT features, the Ki67 index, and risk stratification of GISTs. The correlation among CT features, the Ki67 index, and risk stratification was mainly analyzed using the Spearman rank correlation.

resultsThe incidence of high-risk disease or metastasis was clearly higher in the group with Ki67 > 5% than that in the group with Ki67 ≤ 5% (P < 0.001). The Ki67 index was positively correlated with risk stratification (r = 0.558) or mitotic index (r = 0.619). CT imaging features including size, contour, and margin of the tumor were associated with the Ki67 index (r = 0.332, 0.333, and 0.302, respectively). The multivariate logistic regression analysis revealed that the tumor size [P = 0.043 Exp (B) = 1.150] and the presence of ulceration [P = 0.011, Exp (B) = 3.669] were effective variables in distinguishing between the groups with Ki67 ≤ 5% and >5%. The presence of necrosis or cystic degeneration, tumor contour, tumor margin, and pattern of enhancement were associated with risk stratification (r = 0.530, 0.501, 0.419, and 0.447, respectively).

conclusionsOur findings suggest that the Ki67 index is an effective complementation in predicting the prognosis of GISTs, and CT features including size, contour, and margin of the tumor, presence of necrosis or cystic degeneration, and pattern of enhancement provide evidence to support the importance of preoperative assessment.

Indexed as

Tomography, X-Ray ComputedAdolescentAdultAgedAged, 80 and overChildFemaleGastrointestinal Stromal TumorsHumansKi-67 AntigenMaleMiddle AgedMitotic IndexOdds RatioRisk AssessmentROC CurveKi-67 AntigenCTgastrointestinal stromal tumorsKi67 indexthe risk stratification

Identifiers

PMID30123969
PMCPMC6144253
OpenAlexW2888770844

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.