Evidence map›Paper›PMID 42503527›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2026

The deep learning radiomics nomogram for risk stratification in multiple myeloma using automatic whole-body [

Meiling Xiao, Yan Zhong, Han Hao, Xinhe Yu, Daoyan Hu, Jing Wang, Chentao Jin, Rui Zhou, Rong Tian, Lixiang Yang and 7 more

Abstract read
PubMed Publisher
In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 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

17 authors.

Meiling Xiao *Department of Nuclear Medicine, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China.ORCID http://orcid.org/0000-0003-4646-6183
Yan Zhong *Department of Nuclear Medicine, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Han Hao *Polytechnic Institute of Zhejiang University, Hangzhou, Zhejiang, China.
Xinhe YuPolytechnic Institute of Zhejiang University, Hangzhou, Zhejiang, China.
Daoyan HuCollege of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
Jing WangDepartment of Nuclear Medicine, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Chentao JinDepartment of Nuclear Medicine, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Rui ZhouDepartment of Nuclear Medicine, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Rong TianDepartment of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China.
Lixiang YangDepartment of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China.
Congcong YuDepartment of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Xiaofeng DouDepartment of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Chunlei HanTurku PET Centre, University of Turku, Turku University Hospital, Turku, Finland.
Riku KlénTurku PET Centre, University of Turku, Turku University Hospital, Turku, Finland. riku.klen@utu.fi.
Xiaohui ZhangDepartment of Nuclear Medicine, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China. zhanghui4127@zju.edu.cn.
Mei TianHuman Phenome Institute & Huashan Hospital, Fudan University, Shanghai, China. meitian@zju.edu.cn.
Hong ZhangDepartment of Nuclear Medicine, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China. hzhang21@zju.edu.cn.ORCID http://orcid.org/0000-0002-4084-5150

Funding

Fundamental Research Funds for the Central Universities 226202400059National Key Research and Development Program of China 2021YFA1101700, 2022YFE0118000National Natural Science Foundation of China 82372025, 82030049, 32027802, 82394433, 82361148130, 82502424Postdoctoral Fellowship Program of China Postdoctoral Science Foundation GZC20232302Postdoctoral Science Foundation of China 2023M743024
6 · The paper itself

Abstract

purposeTo develop a deep learning (DL) approach for automatic segmentation and accurate risk stratification in multiple myeloma (MM) using whole-body [

methodsThis retrospective study included MM patients who underwent [

resultsThe study included 345 patients (median age, 59 years [IQR, 35-67 years], 198 male). The nnU-Net achieved a median DSC of 0.64-0.77 for focal lesions segmentation across cohorts. The DLRN was constructed by integrating deep learning radiomics score (DLRS), lactate dehydrogenase (LDH), and β2-microglobulin (β2-MG). The DLRN achieved an area under ROC curve (AUC) of 0.87 (95% confidence interval [CI]: 0.82-0.93), 0.84 (95% CI: 0.73-0.96), and 0.88 (95% CI: 0.76-0.99) for 3-year overall survival (OS) status prediction in the training, internal and external testing cohorts, which outperformed the International Staging System (ISS) (all P < 0.05). Furthermore, the DLRN can effectively identify high-risk individuals (all P < 0.05), demonstrated good agreement between predicted and observed survival probabilities, and provided clinical net benefit.

conclusionThe pattern-specific DL approach achieved automated whole-body tumor segmentation in MM, and the established DLRN demonstrated improved risk stratification capability.

Indexed as

Deep LearningFluorodeoxyglucose F18Image Processing, Computer-AssistedMultiple MyelomaNomogramsPositron Emission Tomography Computed TomographyRadiomicsWhole Body ImagingAdultAgedAutomationFemaleHumansMaleMiddle AgedRetrospective StudiesFluorodeoxyglucose F18[18F]FluorodeoxyglucoseMultiple myelomaPositron emission tomographyRisk stratification

Identifiers

PMID42503527

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.