Evidence map›Paper›PMID 40325259›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2025

Metabolic characterization and radiomics-based composite model for breast cancer immune microenvironment types using

Yuan Gao, Zijian Fu, Xiaojuan Zhu, Hongfeng Li, Lei Yin, Caixia Wu, Jinzhi Chen, Yulong Chen, Li Liang, Jingming Ye and 2 more

Abstract read
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In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2025. 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
–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

2 citing papers in PubMed.

  1. A pretreatmentTranslational cancer research · 2026
    Article
  2. 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

12 authors.

Yuan Gao *Department of Nuclear Medicine, Peking University First Hospital, Beijing, China.
Zijian Fu *Department of Nuclear Medicine, Peking University First Hospital, Beijing, China.
Xiaojuan ZhuDepartment of Pathology, Peking University First Hospital, Beijing, China.
Hongfeng LiInstitute of Medical Technology, Peking University Health Science Center, Beijing, China.
Lei YinDepartment of Nuclear Medicine, Peking University First Hospital, Beijing, China.
Caixia WuDepartment of Nuclear Medicine, Peking University First Hospital, Beijing, China.
Jinzhi ChenDepartment of Nuclear Medicine, Peking University First Hospital, Beijing, China.
Yulong ChenDepartment of Nuclear Medicine, Peking University First Hospital, Beijing, China.
Li LiangDepartment of Pathology, Peking University First Hospital, Beijing, China.
Jingming YeThyroid and Breast Surgery, Peking University First Hospital, Beijing, China. md_ye@sina.com.ORCID 0009-0007-5740-0602
Ling XuThyroid and Breast Surgery, Peking University First Hospital, Beijing, China. Ling.xu@pkufh.com.ORCID 0000-0002-9236-3014
Meng LiuDepartment of Nuclear Medicine, Peking University First Hospital, Beijing, China. louisa_liu@bjmu.edu.cn.ORCID 0000-0002-6960-3163

Funding

Beijing Natural Science Foundation Z210007National High-Level Hospital Clinical Research Funding (Interdepartmental Clinical Research Project of Peking University First Hospital) 2022CR34National Natural Science Foundation of China 82172052Youth Clinical Research Special Fund Project of Peking University First Hospital 2023YC26
6 · The paper itself

Abstract

purposeThe intricateness of tumor immune microenvironment types (TIMTs) complicates identifying responders to immune checkpoint inhibitors (ICIs). Our purpose was to explore the metabolic characteristics of TIMTs in breast cancer using

methodsConsecutive 207 breast cancer patients (211 primary lesions), who underwent

resultsTIMT-II represented the highest proportion in HER2+ (14/22, 64%) and triple-negative (17/27, 63%) breast cancer. Most metabolic parameters (such as SUVmax and TLR) exhibited significant differences in TIMT-II vs. -I or TIMT-II vs. -III (P < 0.05). TLR (P = 0.03; OR: 1.1) and Nottingham grade (P = 0.006; OR: 3.1) were independent impact factors of TIMT-II. We further developed a composite model that integrated radiomics, metabolic parameter, and clinicopathological data, which demonstrated promising predictive efficacy for TIMT-II (AUC

conclusionMetabolic differences existed among different TIMTs, with TIMT-II exhibiting markedly elevated metabolic characteristics. The composite model based on radiomics demonstrated high predictive efficacy for TIMT-II and has the potential to screen ICIs responders.

Indexed as

Breast NeoplasmsFluorodeoxyglucose F18Image Processing, Computer-AssistedPositron Emission Tomography Computed TomographyTumor MicroenvironmentAdultAgedAged, 80 and overFemaleHumansLymphocytes, Tumor-InfiltratingMiddle AgedRadiomicsRetrospective StudiesFluorodeoxyglucose F1818F-FDG PET/CTBreast cancerImmune checkpoint inhibitors (ICIs)RadiomicsTumor immune microenvironment types (TIMTs)

Identifiers

PMID40325259

What OpenQuestion holds

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Registered trials

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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.