ArticleDiagnostics (Basel, Switzerland)2023
A Critical Analysis of the Robustness of Radiomics to Variations in Segmentation Methods in
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- ADC-based radiomics for risk stratification in prostate cancer: a clinical decision support study.Frontiers in oncology · 2026Article
- Insights into radiomics: a comprehensive review for beginners.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Radiomics Reproducibility in Prostate Cancer Diagnosis Based on PROSTATEx.International neurourology journal · 2025Article
- From texture analysis to artificial intelligence: global research landscape and evolutionary trajectory of radiomics in hepatocellular carcinoma.Discover oncology · 2025Article
- A Robust [Journal of imaging informatics in medicine · 2025Article
- Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography.Diagnostics (Basel, Switzerland) · 2025Article
- Preclinical Implementation of matRadiomics: A Case Study for Early Malformation Prediction in Zebrafish Model.Journal of imaging · 2024Article
- Prostate-Specific Membrane Antigen-Positron Emission Tomography-Guided Radiomics and Machine Learning in Prostate Carcinoma.Cancers · 2024Review
- Prognostic nomogram combiningScientific reports · 2024Article
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13 authors.
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Abstract
backgroundRadiomics shows promising results in supporting the clinical decision process, and much effort has been put into its standardization, thus leading to the Imaging Biomarker Standardization Initiative (IBSI), that established how radiomics features should be computed. However, radiomics still lacks standardization and many factors, such as segmentation methods, limit study reproducibility and robustness.
aimWe investigated the impact that three different segmentation methods (manual, thresholding and region growing) have on radiomics features extracted from
conclusionsOur study showed that segmentation methods influence radiomics features and that Shape features were the least reproducible (average ICC: 0.27), while GLCM features the most reproducible. Moreover, feature reproducibility changed depending on segmentation type, resulting in 51.18% of LoG features exhibiting excellent reproducibility (range average ICC: 0.68-0.87) and 47.85% of wavelet features exhibiting poor reproducibility that varied between wavelet sub-bands (range average ICC: 0.34-0.80) and resulted in the LLL band showing the highest average ICC (0.80). Finally, model performance showed that region growing led to the highest accuracy (74.49%), improved sensitivity (84.38%) and AUC (79.20%) in contrast with manual segmentation.
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