ArticleCancer imaging : the official publication of the International Cancer Imaging Society2019
Clinical impact of variability on CT radiomics and suggestions for suitable feature selection: a focus on lung cancer.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Computed Tomography-Based Radiomics Prediction of Biochemical Failure and Distant Metastasis in Patients With High- and Very High-Risk Localized Prostate Cancer.Advances in radiation oncology · 2025Article
- CT-based radiomics model to predict platinum sensitivity in epithelial ovarian carcinoma: a multicentre study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Radiomics: Assessing Significance and Correlation with Ground-Truth Data in Precision Medicine in Lung Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Article
- Analysis of the association between vestibular schwannoma and hearing status using a newly developed radiomics technique.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
- Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence.Scientific reports · 2024Article
- Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors.Frontiers in veterinary science · 2024Article
- Optimization and validation of voxel size-related radiomics variability by combatting batch effect harmonization in pulmonary nodules: a phantom and clinical study.Quantitative imaging in medicine and surgery · 2023Article
- Article
- Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- An integrated nomogram combined semantic-radiomic features to predict invasive pulmonary adenocarcinomas in subjects with persistent subsolid nodules.Quantitative imaging in medicine and surgery · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- Improved Prognosis of Treatment Failure in Cervical Cancer with Nontumor PET/CT Radiomics.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- Automated segmentation of the fractured vertebrae on CT and its applicability in a radiomics model to predict fracture malignancy.Scientific reports · 2022Article
- Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification.Cancers · 2022Article
- A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features.Physics and imaging in radiation oncology · 2021Review
- Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis.Journal of digital imaging · 2021Article
- Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review.Quantitative imaging in medicine and surgery · 2021Review
- Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers.European radiology · 2021Article
- Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19.Journal of translational medicine · 2021Article
- Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundRadiomics suffers from feature reproducibility. We studied the variability of radiomics features and the relationship of radiomics features with tumor size and shape to determine guidelines for optimal radiomics study.
methodsWe dealt with 260 lung nodules (180 for training, 80 for testing) limited to 2 cm or less. We quantified how voxel geometry (isotropic/anisotropic) and the number of histogram bins, factors commonly adjusted in multi-center studies, affect reproducibility. First, features showing high reproducibility between the original and isotropic transformed voxel settings were identified. Second, features showing high reproducibility in various binning settings were identified. Two hundred fifty-two features were computed and features with high intra-correlation coefficient were selected. Features that explained nodule status (benign/malignant) were retained using the least absolute shrinkage selector operator. Common features among different settings were identified, and the final features showing high reproducibility correlated with nodule status were identified. The identified features were used for the random forest classifier to validate the effectiveness of the features. The properties of the uncalculated feature were inspected to suggest a tentative guideline for radiomics studies.
resultsNine features showing high reproducibility for both the original and isotropic voxel settings were selected and used to classify nodule status (AUC 0.659-0.697). Five features showing high reproducibility among different binning settings were selected and used in classification (AUC 0.729-0.748). Some texture features are likely to be successfully computed if a nodule was larger than 1000 mm
conclusionsFeatures showing high reproducibility among different settings correlated with nodule status were identified.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.