ArticleThe British journal of radiology2019
Comparison of radiomics tools for image analyses and clinical prediction in nasopharyngeal carcinoma.
Article in The British journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Radiomics of the Paranasal Sinuses: A Systematic Review of Computer-Assisted Techniques to Assess Computed Tomography Radiological Data.American journal of rhinology & allergy · 2025Pooled it
- Machine learning based classification of spontaneous intracranial hemorrhages using radiomics features.Neuroradiology · 2025Article
- Does consensus contour improve robustness and accuracy inEJNMMI physics · 2024Article
- Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- Developments and future prospects of personalized medicine in head and neck squamous cell carcinoma diagnoses and treatments.Cancer reports (Hoboken, N.J.) · 2024Review
- Revolutionizing radiation therapy: the role of AI in clinical practice.Journal of radiation research · 2024Review
- A radiomics nomogram based on MSCT and clinical factors can stratify fibrosis in inflammatory bowel disease.Scientific reports · 2024Article
- Radiomics for therapy-specific head and neck squamous cell carcinoma survival prognostication (part I).BMC medical imaging · 2023Article
- Radiomics-Based Machine Learning Model for Predicting Overall and Progression-Free Survival in Rare Cancer: A Case Study for Primary CNS Lymphoma Patients.Bioengineering (Basel, Switzerland) · 2023Article
- Evolutionary route of nasopharyngeal carcinoma metastasis and its clinical significance.Nature communications · 2023Article
- Radiomic Signatures Associated with CD8Cancers · 2022Review
- [Research progress in CT-based radiomics constructing hypopharyngeal cancer and multisystem tumor prediction model].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2022Review
- Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence.La Radiologia medica · 2022Review
- Radiomics in Nasopharyngeal Carcinoma.Clinical Medicine Insights. Oncology · 2022Review
- Establishment and Validation of a Novel MRI Radiomics Feature-Based Prognostic Model to Predict Distant Metastasis in Endemic Nasopharyngeal Carcinoma.Frontiers in oncology · 2022Article
- Image Genetic Analysis and Application Research Based on QRFPR and Other Neural Network-Related SNP Loci.BioMed research international · 2022Article
- Comparison of three freeware software packages forJapanese journal of radiology · 2021Article
- Review
- Article
- Dependence of radiomic features on pixel size affects the diagnostic performance of radiomic signature for the invasiveness of pulmonary ground-glass nodule.The British journal of radiology · 2021Article
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Authors and funding
24 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveRadiomics pipelines have been developed to extract novel information from radiological images, which may help in phenotypic profiling of tumours that would correlate to prognosis. Here, we compared two publicly available pipelines for radiomics analyses on head and neck CT and MRI in nasopharynx cancer (NPC). METHODS AND MATERIALS: 100 biopsy-proven NPC cases stratified by T- and N-categories were enrolled in this study. Two radiomics pipeline, Moddicom (v. 0.51) and Pyradiomics (v. 2.1.2) were used to extract radiomics features of CT and MRI. Segmentation of primary gross tumour volume was performed using Velocity v. 4.0 by consensus agreement between three radiation oncologists. Intraclass correlation between common features of the two pipelines was analysed by Spearman's rank correlation. Unsupervised hierarchical clustering was used to determine association between radiomics features and clinical parameters.
resultsWe observed a high proportion of correlated features in the CT data set, but not for MRI; 76.1% (51 of 67 common between Moddicom and Pyradiomics) of CT features and 28.6% (20 of 70 common) of MRI features were significantly correlated. Of these, 100% were shape-related for both CT and MRI, 100 and 23.5% were first-order-related, 61.9 and 19.0% were texture-related, respectively. This interpipeline heterogeneity affected the downstream clustering with known prognostic clinical parameters of cTN-status and GTVp. Nonetheless, shape features were the most reproducible predictors of clinical parameters among the different radiomics modules.
conclusionHere, we highlighted significant heterogeneity between two publicly available radiomics pipelines that could affect the downstream association with prognostic clinical factors in NPC. ADVANCES IN KNOWLEDGE: The present study emphasized the broader importance of selecting stable radiomics features for disease phenotyping, and it is necessary prior to any investigation of multicentre imaging datasets to validate the stability of CT-related radiomics features for clinical prognostication.
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