Evidence map›Paper›PMID 40796801›Full record

ArticleBMC medical imaging2025

Multimodal fusion radiomic-immunologic scoring model: accurate identification of prostate cancer progression.

Zhonglin Zhang, Huan Liu, Xiling Gu, Yang Qiu, Jiangqing Ma, Guangyong Ai, Xiaojing He

Abstract read
In one paragraph

Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
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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.

Zhonglin Zhang *Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 76 Linjiang Rd, Chongqing, 400010, China.
Huan Liu *State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China.
Xiling GuDepartment of Pathology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
Yang QiuDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 76 Linjiang Rd, Chongqing, 400010, China.
Jiangqing MaDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 76 Linjiang Rd, Chongqing, 400010, China.
Guangyong AiDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 76 Linjiang Rd, Chongqing, 400010, China.
Xiaojing HeDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 76 Linjiang Rd, Chongqing, 400010, China. he_xiaojing@hospital.cqmu.edu.cn.

Funding

The chongqing Natural Science Foundation Project CSTB2024NSCQ-MSX0616The Science and Health Joint Medical Research Project of Chongqing 2024ZDXM004
6 · The paper itself

Abstract

objectivesThis study aims to conceptualize, develop, and rigorously validate an innovative Radiomic-Immunologic Score (RDIS) model for accurately distinguishing prostate cancer (PCa) progression.

methodsThis single-center, retrospective cohort study analyzed PCa patients diagnosed between 2019 and 2022. This study employed a comprehensive interdisciplinary approach, integrating CD3+/CD8 + T cell immunoanalysis with Multiparametric Magnetic Resonance Imaging (mpMRI) analysis, while adhering to a robust multi-phase feature selection process. This included the Akaike Information Criterion (AIC), Maximum Relevance Minimum Redundancy (mRMR), and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, validated through 10-fold cross-validation. Logistic regression models were constructed for radiomic, immunologic, and combined RDIS models, with predictive performance rigorously evaluated using Receiver Operating Characteristic (ROC) curve analysis, calibration curve assessments, and Decision Curve Analysis (DCA).

resultsThe RDIS model achieved an Area Under the Curve (AUC) of 0.874 in the validation cohort, outperforming traditional single-omics models, including the radiomic model (AUC: 0.844) and the immunologic model (AUC: 0.767), supporting potential use in early intervention decision-making. The correlation heatmap reveals weak to moderate correlations among 7 pairs of radiomic and immunologic features associated with PCa progression. The RDIS model demonstrates good specificity in further predicting bone metastases and castration-resistant prostate cancer (CRPC).

conclusionsThe RDIS model effectively distinguished the progression status of PCa, with its multi-omics integrative attributes likely providing comprehensive insights into the factors influencing disease progression. ADVANCES IN KNOWLEDGE: The immunologic and radiologic characteristics are associated with prostate cancer progression. The RDIS multi-omics integrative scoring system shows great potential in distinguishing whether prostate cancer has progressed.

Indexed as

Multiparametric Magnetic Resonance ImagingProstatic NeoplasmsAgedDisease ProgressionHumansMaleMiddle AgedRadiomicsRetrospective StudiesDisease progressionImmunohistochemical profiling (IHC)Multiparametric magnetic resonance imaging (mpMRI)Prostate cancer (PCa)Radiomics

Identifiers

PMID40796801
PMCPMC12341337

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LicenceCC BY-NC-ND
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Registered trials

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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.