Evidence map›Paper›PMID 41404069›Full record

ArticleFrontiers in oncology2025

Application of radiomics model based on FDG-PET/CT for the assessment of therapeutic effect in patients with newly-diagnosed multiple myeloma.

Fukai Li, Fei Li, Xiaodan Xu, Xiang Wang, Qingyang Yu, Guangwen Duan, Jiayang Yan, Baiyang Jiang, Hongbiao Sun, Shaochun Xu and 3 more

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 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. 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

13 authors.

Fukai Li *Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Fei Li *Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
Xiaodan Xu *Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Xiang WangDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Qingyang YuDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Guangwen DuanDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Jiayang YanDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Baiyang JiangDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Hongbiao SunDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Shaochun XuDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Kaili ChenDepartment of Hematology, School of Medicine, Shanghai Fourth People's Hospital, Tongji University, Shanghai, China.
Yi XiaoDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Shiyuan LiuDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To evaluate the prediction value of radiomics models based on FDG-PET/CT for the therapeutic effect in patients with newly-diagnosed multiple myeloma (MM). Materials and methods: We retrospectively reviewed the clinical characteristics and Results: Gender was the only one of clinical characteristics found to be independent prognosis factor for treatment evaluation, with a p-value of 0.041. The radiomics models outperformed the Clinical model significantly, among which the PET-CT model yielded the best results with the AUC of 0.809. The PET + CT + Clinical model achieved the optimal performance after integrating clinical and radiomic features, with the AUC of 0.813. Conclusions: The FDG-PET/CT-based radiomics model, particularly when integrated with clinical features, can more effectively predict deep treatment response in newly diagnosed MM patients, offering significant clinical utility for early treatment stratification and personalized therapeutic guidance.

Indexed as

deep responsemultiple myelomaPET/CTradiomicstherapeutic assessment

Identifiers

PMID41404069
PMCPMC12703191

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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.