Evidence map›Paper›PMID 41059297›Full record

ArticleFrontiers in oncology2025

Predictive value of radiomics modeling based on dynamic and static

Xieraili Wumener, Zhiheng Hu, Jiuhui Zhao, Hongjian Wang, Yarong Zhang, Yaohong Deng, Jun Zhao, Ying Liang

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PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Xieraili Wumener *Department of Graduate School, Dalian Medical University, Dalian, China.
Zhiheng Hu *Department of Nuclear Medicine, The Fourth Affiliated Hospital of Xinjiang Medical University, (Xinjiang Uygur Autonomous Region Hospital of Traditional Chinese Medicine), Urumqi, China.
Jiuhui ZhaoDepartment of Nuclear Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Shenzhen Clinical Research Center for Cancer, Shenzhen, China.
Hongjian WangDepartment of Nuclear Medicine, The Fourth Affiliated Hospital of Xinjiang Medical University, (Xinjiang Uygur Autonomous Region Hospital of Traditional Chinese Medicine), Urumqi, China.
Yarong ZhangDepartment of Nuclear Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Shenzhen Clinical Research Center for Cancer, Shenzhen, China.
Yaohong DengShanghai United Imaging Intelligence Co., Ltd, Shanghai, China.
Jun ZhaoDepartment of Nuclear Medicine, Shanghai East Hospital Tongji University, Shanghai, China.
Ying LiangDepartment of Nuclear Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Shenzhen Clinical Research Center for Cancer, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: We aimed to identify the most effective machine learning model for predicting the differential diagnosis of lymph nodes (LNs) in lung cancer using dynamic and static Methods: A total of 279 pathologically confirmed LNs from 74 patients with lung cancer were retrospectively analyzed. These were randomly divided into a training group ( Results: Of the three machine learning models, the RF model demonstrated the greatest predictive efficacy in both the training [area under the curve (AUC) = 0.823] and test groups (AUC = 0.819). The quantitative model based on Conclusion: A composite model based on an RF model of PET/CT+K

Indexed as

18F-FDGdynamiclung cancerPET/CTradiomics model

Identifiers

PMID41059297
PMCPMC12497862

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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.