Evidence map›Paper›PMID 42326344›Full record

ArticleFrontiers in oncology2026

Baseline

Yu He, Shun Wang, Yingchun Li, Xinyang Li, Jingkai Yi, Dan Wang, Kailin Qi, Yongjiang Li, Xiao Jiang, Yutang Yao and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2026. 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

16 authors.

Yu HeMedical Imaging Department, North Sichuan Medical College, Nanchong, China.
Shun WangDepartment of Nuclear Medicine, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Yingchun LiDepartment of Nuclear Medicine and Radiotherapy, Chinese People's Liberation Army Western Theater Command Air Force Hospital, Chengdu, Sichuan, China.
Xinyang LiDepartment of Nuclear Medicine, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Jingkai YiMedical Imaging Department, North Sichuan Medical College, Nanchong, China.
Dan WangDepartment of Nuclear Medicine, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Kailin QiMedical Imaging Department, North Sichuan Medical College, Nanchong, China.
Yongjiang LiDepartment of Nuclear Medicine, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Xiao JiangDepartment of Nuclear Medicine, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Yutang YaoDepartment of Nuclear Medicine, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Ping WuDepartment of Medical Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Meng ZhaoDepartment of Nuclear Medicine, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Hao LuDepartment of Nuclear Medicine, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Taipeng ShenDepartment of Nuclear Medicine, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Zhuzhong ChengMedical Imaging Department, North Sichuan Medical College, Nanchong, China.
Ying KouDepartment of Nuclear Medicine, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To develop baseline ¹ Methods: Patients with DLBCL who underwent baseline Results: A total of 148 patients (101 EMR, 47 non-EMR) were randomly split into training (n = 103) and test (n = 45) sets. This retrospective single-center study used the training set for feature selection and model development, and the test set exclusively for final evaluation. We developed habitat radiomics and dual-channel deep learning models; multilayer perceptron and DenseNet-161 were selected as the optimal architectures, respectively. In the test set, the habitat radiomics model (Habitat_MLP) achieved an AUC of 0.871 (95% CI: 0.7563-0.9857), with a specificity of 0.903, and accuracy of 0.822, showing higher overall performance than the dual-channel deep learning model (DL_DenseNet161), which achieved an AUC of 0.793 (95% CI: 0.6409-0.9444), specificity of 0.677, and accuracy of 0.711. Furthermore, the Habitat_MLP model showed more favorable calibration, higher net benefit on DCA, and improved risk reclassification in this dataset. Conclusion: The habitat radiomics model derived from baseline

Indexed as

deep learningdiffuse large B-cell lymphomahabitat radiomicsinterim PETPET/CT

Identifiers

PMID42326344
PMCPMC13275354

What OpenQuestion holds

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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.