ArticleFrontiers in oncology2025
Prognostic assessment of diffuse large B-cell lymphoma using ultrasound imaging nomogram and PET/CT indicators.
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 1 paper.
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Who cites it
1 citing paper in PubMed.
- Multi-Modal Ultrasound-Based Prognostic Model for Diffuse Large B-Cell Lymphoma with Predominantly Superficial Lymph Node Involvement.Diagnostics (Basel, Switzerland) · 2026Article
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Authors and funding
5 authors.
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Abstract
Background: Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive subtype of non-Hodgkin lymphoma (NHL). Existing prognostic models, such as the International Prognostic Index (IPI), have limitations. PET/CT with 18F-FDG provides metabolic data for prognosis, but integrating ultrasound imaging remains underexplored. This study aims to develop and validate an ultrasound imaging nomogram combined with PET/CT indicators for DLBCL prognosis, improving risk stratification. Methods: A retrospective analysis of 200 DLBCL patients was conducted. Ultrasound imaging and PET/CT indicators were used to assess the prognosis of the patients. Clinical data, ultrasound imaging parameters, and PET/CT metabolic indicators (SUVmax, MTV, TMTV, TLG, SPD) were collected. ACR-Rad nomogram was developed and validated using Cox regression, ROC curves, and calibration analysis. Results: The ultrasound imaging parameters predicted the prognosis of the patients, with SUVmax, MTV, TMTV, TLG, and SPD showing significant correlations with progression-free survival (PFS) and overall survival (OS). Multivariate Cox regression analysis revealed that age, international prognostic index, pathological subtype, and number of sites involved outside the node were independent risk factors for PFS and OS. The ACR-Rad nomogram showed improved predictive accuracy compared to the International Prognosis Index (IPI) alone. Conclusions: The combination of ultrasound imaging and PET/CT indicators improves prognostic accuracy in DLBCL and supports the implementation of individualized clinical decision-making.
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