Evidence map›Paper›PMID 37460990›Full record

ArticleBMC medical imaging2023

Assessment of androgen receptor expression in breast cancer patients using 18 F-FDG PET/CT radiomics and clinicopathological characteristics.

Tongtong Jia, Qingfu Lv, Bin Zhang, Chunjing Yu, Shibiao Sang, Shengming Deng

Open access · goldAbstract read
In one paragraph

Article in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 34% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. 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.

Tongtong Jia *Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Qingfu Lv *Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Bin Zhang *Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Chunjing YuDepartment of Nuclear Medicine, Affiliated Hospital of Jiangnan University, Wuxi, 214122, China. chunjingyu2022@163.com.
Shibiao SangDepartment of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China. sshibiao@163.com.
Shengming DengDepartment of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China. dshming@163.com.
Soochow University · CNFirst Affiliated Hospital of Soochow University · CNJiangnan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveIn the present study, we mainly aimed to predict the expression of androgen receptor (AR) in breast cancer (BC) patients by combing radiomic features and clinicopathological factors in a non-invasive machine learning way. MATERIALS AND

methodsA total of 48 BC patients, who were initially diagnosed by

resultsIn the univariate analysis, the metabolic tumor volume (MTV) was significantly correlated with the expression of AR in BC patients (p < 0.05). However, there only existed feeble correlations between estrogen receptor (ER), progesterone receptor (PR), and AR status (p = 0.127, p = 0.061, respectively). Based on the binary logistic regression method, MTV, SHAPE_Sphericity

conclusionsA prediction model combining radiomic features and clinicopathological characteristics could be a promising approach to predict the expression of AR and noninvasively screen the BC patients who could benefit from anti-AR regimens.

Indexed as

Breast NeoplasmsReceptors, AndrogenAndrogensFemaleFluorodeoxyglucose F18HumansMachine LearningPositron Emission Tomography Computed TomographyRetrospective StudiesAndrogensFluorodeoxyglucose F18Receptors, Androgen18F-FDG PET/CTAndrogen receptorBreast cancerClinicopathologicalMachine learningRadiomics

Identifiers

PMID37460990
PMCPMC10353086
OpenAlexW4384498939

What OpenQuestion holds

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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.