ArticleJapanese journal of radiology2022
Effects of slice thickness on CT radiomics features and models for staging liver fibrosis caused by chronic liver disease.
Article in Japanese journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Role of radiomics in staging liver fibrosis: a meta-analysis.BMC medical imaging · 2024Pooled it
- Multi-modal AI for opportunistic screening, staging and progression risk stratification of steatotic liver disease.Nature communications · 2026Article
- CT-Based Radiomic Models in Biopsy-Proven Liver Fibrosis Staging: Direct Comparison of Segmentation Types and Organ Inclusion.Diagnostics (Basel, Switzerland) · 2025Article
- The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Radiomics-based kidney lesion classification: Mitigating batch effect with nested combat harmonization.Medical physics · 2025Article
- Comparative analysis of U-Mamba and no new U-Net for the detection and segmentation of esophageal cancer in contrast-enhanced computed tomography images.Quantitative imaging in medicine and surgery · 2025Article
- Review
- Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases.The journal of machine learning for biomedical imaging · 2025Article
- Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- Clinical applications of artificial intelligence in liver imaging.La Radiologia medica · 2023Review
- Impact of slice thickness on reproducibility of CT radiomic features of lung tumors.F1000Research · 2023Article
- A comparative study between deep learning and radiomics models in grading liver tumors using hepatobiliary phase contrast-enhanced MR images.BMC medical imaging · 2022Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
Abstract
objectivesTo investigate the effects of slice thickness on CT radiomics features and models for staging liver fibrosis.
methodsA total of 108 pathologically confirmed liver fibrosis patients from a single center were retrospectively collected and divided into different groups. Both thick (5- or 7-mm) and thin slices (1.3- or 2-mm) were analyzed. A fivefold cross-validation with 100 repeats was conducted. The minimum redundancy-maximum relevance algorithm was used to reduce the radiomics features, and the top 10 ranking features were included for further analysis for each loop. The random forest was used for model establishment. The models with median AUC were selected for the assessment of the discriminative performance for both datasets. Mutual features selected by the models with AUC > 0.8 were searched and considered as the most predictive ones.
resultsA total of 162 and 643 radiomics features with excellent reliability were selected from thick- and thin-slice datasets, respectively. The overall discriminative performance of the 500 AUCs from the thin-slice dataset was better than the thick slice. The median AUC values of the thick-sliced datasets were significantly lower than those of the thin-sliced datasets (0.78 and 0.90 for differentiating F1 vs. F2-4, 0.72 and 0.85 for differentiating F1-2 vs. F3-4, both P = 0.03). For differentiating F1-3 vs. F4, no significant difference was found (0.85 vs 0.94, P = 0.15). Six mutual predictive features across all the datasets were found.
conclusionsThe radiomics features extracted from thin-slice images and their corresponding models were better and more stable for staging liver fibrosis.
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