Evidence map›Paper›PMID 34233487›Full record

ArticleThe British journal of radiology2021

Predicting the pathological grade of breast phyllodes tumors: a nomogram based on clinical and magnetic resonance imaging features.

Xiaowen Ma, Lijuan Shen, Feixiang Hu, Wei Tang, Yajia Gu, Weijun Peng

Abstract read
In one paragraph

Article in The British journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Prognostic factors of breast phyllodes tumors.Histology and histopathology · 2023
    Review
  3. Article
  4. Article
  5. Review
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.

Xiaowen MaDepartment of Radiology, Fudan University Shanghai Cancer Center, Xuhui, Shanghai, China.
Lijuan ShenDepartment of Radiology, Fudan University Shanghai Cancer Center, Xuhui, Shanghai, China.
Feixiang HuDepartment of Radiology, Fudan University Shanghai Cancer Center, Xuhui, Shanghai, China.
Wei TangDepartment of Radiology, Fudan University Shanghai Cancer Center, Xuhui, Shanghai, China.
Yajia GuDepartment of Radiology, Fudan University Shanghai Cancer Center, Xuhui, Shanghai, China.
Weijun PengDepartment of Radiology, Fudan University Shanghai Cancer Center, Xuhui, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo explore the potential factors related to the pathological grade of breast phyllodes tumors (PTs) and to establish a nomogram to improve their differentiation ability.

methodsPatients with PTs diagnosed by post-operative pathology who underwent pretreatment magnetic resonance imaging (MRI) from January 2015 to June 2020 were retrospectively reviewed. Traditional clinical features and MRI features evaluated according to the fifth BI-RADS were analyzed by statistical methods and introduced to a stepwise multivariate logistic regression analysis to develop a prediction model. Then, a nomogram was developed to graphically predict the probability of non-benign (borderline/malignant) PTs.

resultsFinally, 61 benign, 73 borderline and 48 malignant PTs were identified in 182 patients. Family history of tumor, diameter, lobulation, cystic component, signal on fat saturated

conclusionFamily history of tumor, lobulation, cystic components, signals on FS ADVANCES IN KNOWLEDGE: More sample size and characteristics were used to explore the factors related to the pathological grade of PTs and establish a predictive nomogram for the first time.

Indexed as

Magnetic Resonance ImagingNomogramsAdolescentAdultAgedAged, 80 and overBreast NeoplasmsFemaleHumansMiddle AgedNeoplasm GradingPhyllodes TumorRetrospective StudiesYoung Adult

Identifiers

PMID34233487
PMCPMC8764923

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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.