ArticleMolecular imaging and biology2019
Radiomics Analysis of PET and CT Components of PET/CT Imaging Integrated with Clinical Parameters: Application to Prognosis for Nasopharyngeal Carcinoma.
Article in Molecular imaging and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.
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Who cites it
57 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review and meta-analysis of predictive and prognostic models for outcome prediction using positron emission tomography radiomics in head and neck squamous cell carcinoma patients.Cancer medicine · 2023Pooled it
- Clinico-biological-radiomics (CBR) based machine learning for improving the diagnostic accuracy of FDG-PET false-positive lymph nodes in lung cancer.European journal of medical research · 2023Trial
- Radiomic analysis for predicting prognosis of colorectal cancer from preoperativeJournal of translational medicine · 2022Trial
- Radiomics Using Fused PET and CT Data for Prognostic and Diagnostic Modeling in Head and Neck Cancer: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Estimation of pulmonary function from time-resolved dynamic chest radiography using machine learning in patients with respiratory disease.Journal of applied clinical medical physics · 2026Article
- Article
- Integrating Radiomics and Deep-Learning for Prognostic Evaluation in Nasopharyngeal Carcinoma.Medicina (Kaunas, Lithuania) · 2025Review
- Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic Carcinoma.Advances in radiation oncology · 2025Article
- Predicting Human Epidermal Growth Factor Receptor 2 Expression in Breast Cancer Based on Radiomics of MRI Habitat and US.Breast cancer (Dove Medical Press) · 2025Article
- Adaptive segmentation-to-survival learning for survival prediction from multi-modality medical images.NPJ precision oncology · 2024Article
- Does consensus contour improve robustness and accuracy inEJNMMI physics · 2024Article
- The [Cancers · 2024Article
- Precision Medicine for Nasopharyngeal Cancer-A Review of Current Prognostic Strategies.Cancers · 2024Review
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
- Comprehensive evaluation of similarity between synthetic and real CT images for nasopharyngeal carcinoma.Radiation oncology (London, England) · 2023Article
- Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma.European journal of nuclear medicine and molecular imaging · 2023Article
- Radiomics-clinical nomogram based on pretreatment 18F-FDG PET-CT radiomics features for individualized prediction of local failure in nasopharyngeal carcinoma.Scientific reports · 2023Article
- Multimodality radiomics analysis based on [European radiology · 2023Article
- Development of a Model Based on Delta-Radiomic Features for the Optimization of Head and Neck Squamous Cell Carcinoma Patient Treatment.Medicina (Kaunas, Lithuania) · 2023Article
- Does consensus contours improve robustness and accuracy onEJNMMI physics · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo investigate the prognostic performance of radiomics features, as extracted from positron emission tomography (PET) and X-ray computed tomography (CT) components of baseline 2-deoxy-2-[ PROCEDURES: One hundred twenty-eight NPC patients (85 vs. 43 for training vs. validation), containing a subset of 86 patients with local-regional advanced stage, were enrolled. All patients underwent pretreatment PET/CT scans (mean follow-up time 24 ± 14 months). Three thousand two hundred seventy-six radiomics features extracted from PET or CT components and 13 clinical parameters were used to predict progression-free survival (PFS). Univariate analysis with Benjamini-Hochberg false discovery rate (FDR) correction was first used to screen significant features, and redundant features with Spearman's correlation > 0.8 were further eliminated. Then, seven multivariate models involving PET features and/or CT features and/or clinical parameters (denoted as clinical, PET, CT, clinical + PET, clinical + CT, PET + CT and clinical + PET + CT) were constructed by forward stepwise multivariate Cox regression. Model performance was evaluated by concordance index (C-index).
resultsSixty patients encountered events (28 recurrences, 17 metastases, and 15 deaths). Six clinical parameters, 3 PET features, and 14 CT features in training cohort and 4 clinical parameters, 10 PET features, and 4 CT features in subset of local-regional advanced stage were significantly associated with PFS. Combining PET and/or CT features with clinical parameters showed equal or higher prognostic performance than models with PET or CT or clinical parameters alone (C-index 0.71-0.76 vs. 0.67-0.73 and 0.62-0.75 vs. 0.54-0.75 for training and validation cohorts, respectively), while the prognostic performance was significantly improved in local-regional advanced cohort (C-index 0.67-0.84 vs. 0.64-0.77, p value 0.001-0.059).
conclusionRadiomics features extracted from the PET and CT components of baseline PET/CT images provide complementary prognostic information and improved outcome prediction for NPC patients compared with use of clinical parameters alone.
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