ArticleBMC medical imaging2025
Multimodal fusion radiomic-immunologic scoring model: accurate identification of prostate cancer progression.
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.
What it found
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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.
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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.
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Who cites it
4 citing papers in PubMed.
- Theranostic Smart Bionanosensors for Solid Tumors: Advances, Challenges, and Future Directions.Applied biochemistry and biotechnology · 2026Review
- Review
- Clinically oriented immune heterogeneity in prostate cancer: emerging targets and strategies.Frontiers in immunology · 2026Review
- Towards precision medicine in Tourette syndrome: a perspective on AI-driven predictive modelling and personalised care.Frontiers in computational neuroscience · 2026Review
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Authors and funding
7 authors.
Funding
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.
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Registered trials
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.