Evidence map›Paper›PMID 31259153›Full record

ArticleFrontiers in oncology2019

Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer.

Tianwen Xie, Zhe Wang, Qiufeng Zhao, Qianming Bai, Xiaoyan Zhou, Yajia Gu, Weijun Peng, He Wang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 1 pooled it
8.0field-weighted citation impact, top 2% 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

41 citing papers in PubMed, 1 synthesis or guideline pooled it, 82 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Hype or hope? A review of challenges in balancing tumor control and treatment toxicity in breast cancer from the perspective of the radiation oncologist.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024
    Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  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

8 authors at 3 institutions in 1 country.

Tianwen XieDepartment of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
Zhe WangHuman Phenome Institute, Fudan University, Shanghai, China.
Qiufeng ZhaoDepartment of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Qianming BaiDepartment of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.
Xiaoyan ZhouDepartment of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.
Yajia GuDepartment of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
Weijun PengDepartment of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
He WangHuman Phenome Institute, Fudan University, Shanghai, China.
Fudan University Shanghai Cancer Center · CNFudan University · CNLonghua Hospital Shanghai University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

breast cancerdiffusion-weighted imagingdynamic contrast-enhanced imagingimmunohistochemical subtypesmachine learningradiomics

Identifiers

PMID31259153
PMCPMC6587031
OpenAlexW2952610116

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.