Evidence map›Paper›PMID 40606989›Full record

ArticleFrontiers in oncology2025

Development of a model to predict Ki-67 expression status in non-Hodgkin's lymphoma based on PET radiomics.

Ke Tian, Pengfei Tong, Keliu Wu, Amina Azhar, Yuan Fang, Nan Xu, Ruihua Wang

Abstract read
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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Ke TianDepartment of Radiology, Air Force Medical Center, Air Force Medical University, Beijing, China.
Pengfei TongDepartment of Neurosurgery, Henan Provincial Third People's Hospital, Zhengzhou, China.
Keliu WuDepartment of Nuclear Medicine, Henan Medical Key Laboratory of Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Amina AzharImaging and Nuclear Medicine, Zhengzhou University, Zhengzhou, China.
Yuan FangDepartment of Radiology, Air Force Medical Center, Air Force Medical University, Beijing, China.
Nan XuDepartment of Radiology, Air Force Medical Center, Air Force Medical University, Beijing, China.
Ruihua WangDepartment of Nuclear Medicine, Henan Medical Key Laboratory of Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study aims to evaluate the effectiveness of conventional metabolic parameters and radiomic features from Methods: We analyzed clinical, immunohistochemical(IHC), and Results: Among the patients, 70 exhibited low Ki-67 expression while 127 had high Ki-67 expression (113 males, 84 females, aged 5-85 years). The high Ki-67 group showed a higher proportion of fever(75.9% vs. 24.1%, P < 0.05) and tumor SUV max value/mediastinal SUV max value (T/MB) (P < 0.01). Five radiomic features formed the radiomics score (AUC: training 0.827; validation 0.883). The combined model showed the highest AUC(training 0.921; validation 0.916), indicating strong predictive capability. Conclusion: The radiomics model derived from

Indexed as

invasiveKi-67metabolic parameternon-Hodgkin lymphomaPET/CTradiomics

Identifiers

PMID40606989
PMCPMC12213689

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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.