ArticleEuropean journal of nuclear medicine and molecular imaging2023
Robust deep learning-based PET prognostic imaging biomarker for DLBCL patients: a multicenter study.
Article in European journal of nuclear medicine and molecular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Artificial Intelligence for Risk Stratification in Diffuse Large B-Cell Lymphoma: A Systematic Review of Classification Models and Predictive Performances.Medical sciences (Basel, Switzerland) · 2025Pooled it
- Deep learning-based PET/CT mixture-of-experts model for relapse risk stratification in relapsed/refractory classical hodgkin lymphoma: a multicenter study.European journal of nuclear medicine and molecular imaging · 2026Article
- Multi-Modal Ultrasound-Based Prognostic Model for Diffuse Large B-Cell Lymphoma with Predominantly Superficial Lymph Node Involvement.Diagnostics (Basel, Switzerland) · 2026Article
- Predicting overall survival after initial chemotherapy for diffuse large B-cell lymphoma using CT nomogram analysis.BMC medical imaging · 2026Article
- Multi-view deep learning for automated lymphoma staging fromEJNMMI research · 2026Article
- Optimal peritumoral regions and fusion strategies for prediction of the double-expressor subtype in diffuse large B-cell lymphoma: a multi-region radiomics study.Frontiers in oncology · 2026Article
- Can we obtain prognostic information from healthy tissue uptake and volume in baselineEuropean journal of nuclear medicine and molecular imaging · 2026Article
- [Advancements in artificial intelligence for the precise diagnosis and treatment of hematological malignancies].Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi · 2025Review
- Review
- Clinical scoring systems, molecular subtypes and baseline [Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Review
- Survival prediction in diffuse large B-cell lymphoma patients: multimodal PET/CT deep features radiomic model utilizing automated machine learning.Journal of cancer research and clinical oncology · 2024Article
- Comparison of Cox regression and generalized Cox regression models to machine learning in predicting survival of children with diffuse large B-cell lymphoma.Translational cancer research · 2024Article
- Comparing the clinical value of baseline [European journal of nuclear medicine and molecular imaging · 2023Article
- [European journal of nuclear medicine and molecular imaging · 2023Article
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Authors and funding
10 authors at 7 institutions in 1 country.
Funding
Abstract
objectiveTo develop and independently externally validate robust prognostic imaging biomarkers distilled from PET images using deep learning techniques for precise survival prediction in patients with diffuse large B cell lymphoma (DLBCL).
methodsA total of 684 DLBCL patients from three independent medical centers were included in this retrospective study. Deep learning scores (DLS) were generated from PET images using deep convolutional neural network architecture known as VGG19 and DenseNet121. These DLSs were utilized to predict progression-free survival (PFS) and overall survival (OS). Furthermore, multiparametric models were designed based on results from the Cox proportional hazards model and assessed through calibration curves, concordance index (C-index), and decision curve analysis (DCA) in the training and validation cohorts.
resultsThe DLS
conclusionsThe DLSs were identified as robust prognostic imaging biomarkers for survival in DLBCL patients. Moreover, the multiparametric models developed in this study exhibited promising potential in accurately stratifying patients based on their survival risk.
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