ArticleMolecular imaging and biology2020
Subregional Radiomics Analysis of PET/CT Imaging with Intratumor Partitioning: Application to Prognosis for Nasopharyngeal Carcinoma.
Article in Molecular imaging and biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 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
- Explainable PET-Based Habitat Modeling for Predicting Postoperative Recurrence Risk in Invasive Lung Adenocarcinoma.Annals of surgical oncology · 2026Article
- Article
- Habitat Radiomics Predict HPV Status in Oropharyngeal Cancer.Cancer medicine · 2025Article
- Integrating Radiomics and Deep-Learning for Prognostic Evaluation in Nasopharyngeal Carcinoma.Medicina (Kaunas, Lithuania) · 2025Review
- Performance of MRI-based radiomics for prediction of residual disease status in patients with nasopharyngeal carcinoma after radical radiotherapy.Scientific reports · 2025Article
- Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study.Translational oncology · 2025Article
- Prediction of induction chemotherapy efficacy in patients with locally advanced nasopharyngeal carcinoma using habitat subregions derived from multi-modal MRI radiomics.Frontiers in oncology · 2025Article
- A CT-based subregional radiomics nomogram for predicting local recurrence-free survival in esophageal squamous cell cancer patients treated by definitive chemoradiotherapy: a multicenter study.Journal of translational medicine · 2024Article
- Article
- Role ofCancers · 2024Review
- Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer.Radiology. Artificial intelligence · 2024Article
- Comprehensive evaluation of similarity between synthetic and real CT images for nasopharyngeal carcinoma.Radiation oncology (London, England) · 2023Article
- Multimodality radiomics analysis based on [European radiology · 2023Article
- Prognostic value ofEJNMMI research · 2023Article
- Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study.Insights into imaging · 2023Article
- The Efficacy of PretreatmentClinical Medicine Insights. Oncology · 2023Article
- Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy.Journal of applied clinical medical physics · 2022Article
- Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [Cancers · 2022Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis work aims to identify intratumoral habitats with distinct heterogeneity based on 2-deoxy-2-[ PROCEDURES: In total, 128 NPC patients (85 vs. 43 for primary vs. validation cohorts) who underwent pre-treatment PET/CT scan were enrolled retrospectively. Each tumor was partitioned into several phenotypically consistent subregions based on individual- and population-level clustering. For each subregion, 202 radiomics features were extracted to construct imaging biomarker for prognosis via Cox's proportional hazard model combined with forward stepwise feature selection. Relevance of imaging biomarkers and clinicopathological factors were assessed by multivariate Cox regression analysis and Spearman's correlation analysis. To investigate whether imaging biomarkers could provide complementary prognosis information beyond existing predictors, a scoring system was further developed for risk stratification and compared with AJCC staging system.
resultsThree subregions (denoted as S1, S2, and S3) were discovered with distinct PET/CT imaging characteristics in the two cohorts. The prognostic performance of imaging biomarker S3 outperformed the whole tumor (C-index, 0.69 vs. 0.58; log-rank test, p < 0.001 vs. p = 0.552). Imaging biomarker S3 and AJCC stage were identified as independent predictors (p = 0.011 and 0.042, respectively) after adjusting for clinicopathological factors. The scoring system outperformed the traditional AJCC staging system (log-rank test, p < 0.0001 vs. p = 0.0002 in primary cohort and p = 0.0021 vs. p = 0.0277 in validation cohort, respectively).
conclusionsSubregional radiomics analysis of PET/CT imaging has the potential to predict PFS in patients with NPC, which also provides complementary prognostic information for traditional predictors.
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