ArticleEuropean radiology2018
Robustness versus disease differentiation when varying parameter settings in radiomics features: application to nasopharyngeal PET/CT.
Article in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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46 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and CT radiomics features: a multicentric study.Radiation oncology (London, England) · 2024Article
- Machine-learning methods based on the texture and non-texture features of MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer.Translational cancer research · 2023Article
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- Limited impact of discretization/interpolation parameters on the predictive power of CT radiomic features in a surgical cohort of pancreatic cancer patients.La Radiologia medica · 2023Article
- Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to NasopharyngealCancers · 2023Article
- Investigation of the Inter- and Intrascanner Reproducibility and Repeatability of Radiomics Features in T1-Weighted Brain MRI.Journal of magnetic resonance imaging : JMRI · 2022Article
- Development and verification of radiomics framework for computed tomography image segmentation.Medical physics · 2022Article
- Development and validation of a radiomic model for the diagnosis of dopaminergic denervation on [18F]FDOPA PET/CT.European journal of nuclear medicine and molecular imaging · 2022Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- Imbalanced Data Correction Based PET/CT Radiomics Model for Predicting Lymph Node Metastasis in Clinical Stage T1 Lung Adenocarcinoma.Frontiers in oncology · 2022Article
- Radiomics in Nasopharyngeal Carcinoma.Clinical Medicine Insights. Oncology · 2022Review
- Reply to Letter to Editor RE: "Integration of PET/CT Radiomics and Semantic Features for Differentiation Between Active Pulmonary Tuberculosis and Lung Cancer".Molecular imaging and biology · 2021Article
- A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features.Physics and imaging in radiation oncology · 2021Review
- Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review.Quantitative imaging in medicine and surgery · 2021Review
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10 authors.
Funding
Abstract
objectivesTo investigate the impact of parameter settings as used for the generation of radiomics features on their robustness and disease differentiation (nasopharyngeal carcinoma (NPC) versus chronic nasopharyngitis (CN) in FDG PET/CT imaging).
methodsWe studied 106 patients (69/37 NPC/CN, pathology confirmed), and extracted 57 radiomics features under different parameter settings. Robustness was assessed by the intra-class correlation coefficient (ICC). Logistic regression with leave-one-out cross validation was used to generate classification probabilities, and diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC).
resultsVarying averaging strategies and symmetry, 4/26 GLCM features showed poor range of pairwise ICCs of 0.02-0.98, while depicting good AUCs of 0.82-0.91. Varying distances, 5/26 GLCM features showed ICCs of 0.82-0.99 while corresponding AUCs were 0.52-0.91. 6/13 GLRLM features showed both high AUC (0.81-0.89) and high ICC (0.85-0.99) regarding to averaging strategies. 7/13 GLSZM features showed AUCs of 0.81-0.90 while having ICCs of 0.01-0.99 under different neighbourhoods. 2/5 NGTDM features showed AUCs of 0.81-0.85 while having ICCs of 0.19-0.89 for different window sizes. Differentiating a subset of NPC (stages I-II) form CN, both SumEntropy and SZLGE achieved significantly higher AUCs than metabolically active tumour volume (AUC: 0.91 vs. 0.72, p<0.01).
conclusionsRadiomics features depicting poor absolute-scale robustness regarding to parameter settings can still lead to good diagnostic performance. As such, robustness of radiomics features should not be overemphasized for removal of features towards assessment of clinical tasks. For differentiating NPC from CN, some radiomics features (e.g. SumEntropy, SZLGE, LGZE) outperformed conventional metrics. KEY POINTS: • Poor robustness did not necessarily translate into poor differentiation performance. • Absolute-scale robustness of radiomics features should not be overemphasized. • Radiomics features SumEntropy, SZLGE and LGZE outperformed conventional metrics.
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