Evidence map›Paper›PMID 36271354›Full record

ArticleBMC cancer2022

Tumour stroma ratio is a potential predictor for 5-year disease-free survival in breast cancer.

Dandan Yan, Xianli Ju, Bin Luo, Feng Guan, Huihua He, Honglin Yan, Jingping Yuan

Abstract read
In one paragraph

Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Exploring the 89Zr/177Lu Theranostic Pairing with an Extracellular Matrix-Targeted Radiopharmaceutical against Triple-Negative Breast Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Targeting Extra Domain A of Fibronectin to Improve Noninvasive Detection of Triple-Negative Breast Cancer.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. 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

7 authors.

Dandan YanDepartment of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan, 430060, People's Republic of China.
Xianli JuDepartment of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan, 430060, People's Republic of China.
Bin LuoDepartment of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan, 430060, People's Republic of China.
Feng GuanDepartment of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan, 430060, People's Republic of China.
Huihua HeDepartment of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan, 430060, People's Republic of China.
Honglin YanDepartment of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan, 430060, People's Republic of China.
Jingping YuanDepartment of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan, 430060, People's Republic of China. yuanjingping@whu.edu.cn.

Funding

Fundamental Research Funds for the Central Universities 2042020kf0119National Natural Science Foundation of China 82003490
6 · The paper itself

Abstract

backgroundThe tumour-stroma ratio (TSR) is identified as a promising prognostic parameter for breast cancer, but the cutoff TSR value is mostly assessed by visual assessment, which lacks objective measurement. The aims of this study were to optimize the cutoff TSR value, and evaluate its prognosis value in patients with breast cancer both as continuous and categorical variables.

methodsMajor clinicopathological and follow-up data were collected for a series of patients with breast cancer. Tissue microarray images stained with cytokeratin immunohistochemistry were evaluated by automated quantitative image analysis algorithms to assess TSR. The potential cutoff point for TSR was optimized using maximally selected rank statistics. The association between TSR and 5-year disease-free survival (5-DFS) was assessed by Cox regression analysis. Kaplan-Meier analysis and log-rank test were used to assess the significance in survival analysis.

resultsThe optimal cut-off TSR value was 33.5%. Using this cut-off point, categorical variable analysis found that low TSR (i.e., high stroma, TSR ≤ 33.5%) predicts poor outcomes for 5-DFS (hazard ratio [HR] = 2.82, 95% confidence interval [CI] = 1.81-4.40, P = 0.000). When TSR was considered as a continuous parameter, results showed that increased stroma content was associated with worse 5-DFS (HR = 1.71, 95% CI = 1.34-2.18, P = 0.000). Similar results were also obtained in three molecular subtypes in continuous and categorical variable analyses. Moreover, in the Kaplan-Meier analysis, log-rank test showed that low TSR displayed a worse 5-DFS than high TSR (P = 0.000). Similar results were also obtained in patients with triple-negative breast cancer, human epidermal growth factor receptor 2 (HER2)-positive breast cancer, and luminal-HER2-negative breast cancer.

conclusionTSR is an independent predictor for 5-DFS in breast cancer with worse survival outcomes in low TSR. The prognostic value of TSR was also observed in other three molecular subtypes.

Indexed as

Stromal CellsTriple Negative Breast NeoplasmsDisease-Free SurvivalHumansKeratinsPrognosisKeratinsBreast cancerDigital image-quantified analysisPrognosis predictionTumor-stroma ratio

Identifiers

PMID36271354
PMCPMC9585868

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.