ArticleRadiology2019
Bone Marrow and Tumor Radiomics at
Article in Radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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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
36 citing papers in PubMed.
- Molecular imaging usingAnnals of nuclear medicine · 2025Trial
- An interpretable PET/CT-based radiomic-clinical model for predicting bone marrow involvement in follicular lymphoma: comparison of pelvic and spine-pelvis VOI frameworks.European journal of nuclear medicine and molecular imaging · 2026Article
- From imaging heterogeneity to clinical decision: a novel imaging biomarker based on bone marrow kinetics for advanced NSCLC patients in partial response.EJNMMI research · 2026Article
- Thorax-encompassing multi-modality PET/CT deep learning model for resected lung cancer prognostication: A retrospective, multicenter study.Medical physics · 2025Article
- Reproducibility of the CT radiomic features of pulmonary nodules: the effects of the CT reconstruction algorithm, radiation dose, and contrast agent.Quantitative imaging in medicine and surgery · 2025Article
- Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images.Journal of medical systems · 2025Article
- [European radiology · 2024Article
- Development and validation of radiomics nomogram for metastatic status of epithelial ovarian cancer.Scientific reports · 2024Article
- Predicting event-free survival after induction of remission in high-risk pediatric neuroblastoma: combiningPediatric radiology · 2024Article
- Does FDG PET-Based Radiomics Have an Added Value for Prediction of Overall Survival in Non-Small Cell Lung Cancer?Journal of clinical medicine · 2024Article
- Article
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
- Review of the use of radiomics to assess the risk of recurrence in early-stage non-small cell lung cancer.Translational lung cancer research · 2023Review
- Relationship of FDG Uptake of the Reticuloendothelial System with Tumor Immune Microenvironment and Prognosis in Patients with Gastric Cancer.Life (Basel, Switzerland) · 2023Article
- Review
- A CT-based radiomics signature for prediction of HER2 overexpression and treatment efficacy of trastuzumab in advanced gastric cancer.Translational cancer research · 2022Article
- Predicting recurrence risks in lung cancer patients using multimodal radiomics and random survival forests.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Applying a CT texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis.Journal of applied clinical medical physics · 2022Article
- Prognostic Value of Bone Marrow Uptake Using 18F-FDG PET/CT Scans in Solid Neoplasms.Journal of imaging · 2022Article
- Effects of Tracer Uptake Time in Non-Small Cell Lung CancerJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Background Primary tumor maximum standardized uptake value is a prognostic marker for non-small cell lung cancer. In the setting of malignancy, bone marrow activity from fluorine 18-fluorodeoxyglucose (FDG) PET may be informative for clinical risk stratification. Purpose To determine whether integrating FDG PET radiomic features of the primary tumor, tumor penumbra, and bone marrow identifies lung cancer disease-free survival more accurately than clinical features alone. Materials and Methods Patients were retrospectively analyzed from two distinct cohorts collected between 2008 and 2016. Each tumor, its surrounding penumbra, and bone marrow from the L3-L5 vertebral bodies was contoured on pretreatment FDG PET/CT images. There were 156 bone marrow and 512 tumor and penumbra radiomic features computed from the PET series. Randomized sparse Cox regression by least absolute shrinkage and selection operator identified features that predicted disease-free survival in the training cohort. Cox proportional hazards models were built and locked in the training cohort, then evaluated in an independent cohort for temporal validation. Results There were 227 patients analyzed; 136 for training (mean age, 69 years ± 9 [standard deviation]; 101 men) and 91 for temporal validation (mean age, 72 years ± 10; 91 men). The top clinical model included stage; adding tumor region features alone improved outcome prediction (log likelihood, -158 vs -152;
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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.