ArticleBMC medical imaging2023
Article in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 citations in OpenAlex.
- The [European journal of nuclear medicine and molecular imaging · 2026Pooled it
- Pooled it
- Automated Deauville Score computation from baseline [¹⁸F]FDG PET/CT predicts progression-free survival in multiple myeloma: a radiogenomic framework.European journal of nuclear medicine and molecular imaging · 2026Article
- The deep learning radiomics nomogram for risk stratification in multiple myeloma using automatic whole-body [European journal of nuclear medicine and molecular imaging · 2026Article
- Correlation analysis of serum CCL3, TRACP-5b, and sclerostin with the prognosis of multiple myeloma patients.Journal of medical biochemistry · 2026Article
- Associations between CT Radiomics Analyses and Hematopoietic Cell Mobilization in Patients with Multiple Myeloma: An Exploratory Analysis.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2026Article
- Radiogenomics in Lymphoma and Multiple Myeloma: A Systematic Review of Current Evidence and Future Directions.Journal of clinical medicine · 2026Review
- CCT2 regulates the proliferation, apoptosis, and cell cycle arrest of multiple myeloma cells through the P53 signalling pathway.Scientific reports · 2026Article
- Explainable machine-learning prediction of overall and cancer-specific survival in adult triple-negative breast cancer using SEER: a comparative study of nomograms and random survival forests.Translational cancer research · 2026Article
- Next-Generation Biomarkers in Multiple Myeloma: Advancing Diagnosis, Risk Stratification, and Precision Therapy Beyond Current Guidelines.Pharmaceuticals (Basel, Switzerland) · 2026Review
- A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data.Scientific reports · 2025Article
- Whole-body low-dose computed tomography in patients with newly diagnosed multiple myeloma predicts cytogenetic risk: a deep learning radiogenomics study.Skeletal radiology · 2025Article
- Article
- Deep learning and pathomics analyses predict prognosis of high-grade gliomas.Frontiers in neurology · 2025Article
- Application of radiomics model based on FDG-PET/CT for the assessment of therapeutic effect in patients with newly-diagnosed multiple myeloma.Frontiers in oncology · 2025Article
- Review
- Coefficient of variation and texture analysis of 18F-FDG PET/CT images for the prediction of outcome in patients with multiple myeloma.Annals of hematology · 2024Article
- Imaging of Multiple Myeloma: Present and Future.Journal of clinical medicine · 2024Review
- Recent advances in imaging and artificial intelligence (AI) for quantitative assessment of multiple myeloma.American journal of nuclear medicine and molecular imaging · 2024Review
- Positron Emission Tomography-Derived Radiomics and Artificial Intelligence in Multiple Myeloma: State-of-the-Art.Journal of clinical medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeMultiple myeloma (MM), the second most hematological malignancy, have been studied extensively in the prognosis of the clinical parameters, however there are only a few studies have discussed the role of dual modalities and multiple algorithms of
methodsWe extensively explored the predictive ability and clinical decision-making ability of different combination image data of PET, CT, clinical parameters and six machine learning algorithms, Cox proportional hazards model (Cox), linear gradient boosting models based on Cox's partial likelihood (GB-Cox), Cox model by likelihood based boosting (CoxBoost), generalized boosted regression modelling (GBM), random forests for survival model (RFS) and support vector regression for censored data model (SVCR). And the model evaluation methods include Harrell concordance index, time dependent receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).
resultsWe finally confirmed 5 PET based features, and 4 CT based features, as well as 6 clinical derived features significantly related to progression free survival (PFS) and we included them in the model construction. In various modalities combinations, RSF and GBM algorithms significantly improved the accuracy and clinical net benefit of predicting prognosis compared with other algorithms. For all combinations of various modalities based models, single-modality PET based prognostic models' performance was outperformed baseline clinical parameters based models, while the performance of models of PET and CT combined with clinical parameters was significantly improved in various algorithms.
conclusion
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What OpenQuestion holds
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.