ReviewInternational journal of computer assisted radiology and surgery2020
Leveraging RSF and PET images for prognosis of multiple myeloma at diagnosis.
Review in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- EANM guidelines on the use of [European journal of nuclear medicine and molecular imaging · 2024Guideline
- FDG-PET medullary total tumor volume highlights high-risk patients with newly diagnosed multiple myeloma in CASSIOPEIA trial.Blood advances · 2026Trial
- Relationship Between [European journal of breast health · 2025Article
- A systematic epidemiological trends analysis study in global burden of multiple myeloma and 29 years forecast.Scientific reports · 2025Article
- The Ratio of Serum Urea Nitrogen to Albumin Is a Better Predictor of Overall Survival in Multiple Myeloma Patients than Urea Nitrogen Alone.Acta haematologica · 2025Article
- 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
- Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis.Frontiers in pharmacology · 2024Article
- Recent advances in imaging and artificial intelligence (AI) for quantitative assessment of multiple myeloma.American journal of nuclear medicine and molecular imaging · 2024Review
- New Developments in Myeloma Treatment and Response Assessment.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- Article
- Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis.BMC public health · 2023Article
- Radiomics analysis of bone marrow biopsy locations in [Physical and engineering sciences in medicine · 2023Article
- Radiomics model of 18F-FDG PET/CT imaging for predicting disease-free survival of early-stage uterine cervical squamous cancer.Cancer biomarkers : section A of Disease markers · 2022Review
- A preliminary study using spinal MRI-based radiomics to predict high-risk cytogenetic abnormalities in multiple myeloma.La Radiologia medica · 2021Article
- Random survival forest to predict transplant-eligible newly diagnosed multiple myeloma outcome including FDG-PET radiomics: a combined analysis of two independent prospective European trials.European journal of nuclear medicine and molecular imaging · 2021Article
- Article
- Review
- Article
- Radiomic Analysis of Positron-Emission Tomography and Computed Tomography Images to Differentiate between Multiple Myeloma and Skeletal Metastases.Indian journal of nuclear medicine : IJNM : the official journal of the Society of Nuclear Medicine, IndiaArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
purposeMultiple myeloma (MM) is a bone marrow cancer that accounts for 10% of all hematological malignancies. It has been reported that FDG PET imaging provides prognostic information for both baseline and therapeutic follow-up of MM patients using visual analysis. In this study, we aim to develop a computer-assisted method based on PET quantitative image features to assist diagnoses and treatment decisions for MM patients.
methodsOur proposed model relies on a two-stage method with Random Survival Forest (RFS) and variable importance (VIMP) for both feature selection and prediction. The targeted variable for prediction is the progression-free survival (PFS). We consider texture-based (radiomics), conventional (e.g., SUVmax) and clinical biomarkers. We evaluate PFS predictions in terms of C-index and final prognosis separation in two risk groups, from a database of 66 patients who were part of the prospective multi-centric french IMAJEM study.
resultsOur method (VIMP + RSF) provides better results (1-C-index of 0.36) than conventional methods such as Lasso-Cox and gradient-boosting Cox (0.48 and 0.56, respectively). We experimentally proved the interest of using selection (0.61 for RSF without selection) and showed that VIMP selection is more stable and gives better results than minimal depth and variable hunting (0.47 and 0.43). The approach gives better prognosis group separation (a p value of 0.05 against 0.11 to 0.4 for others).
conclusionOur results confirm the predictive value of radiomics for MM patients, in particular, they demonstrate that quantitative/heterogeneity image-based features reduce the error of the predicted progression. To our knowledge, this is the first work using RFS on PET images for the progression prediction of MM patients. Moreover, we provide an analysis of the feature selection process, which points toward the identification of clinically relevant biomarkers.
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