ArticleJapanese journal of radiology2014
Kurtosis and skewness assessments of solid lung nodule density histograms: differentiating malignant from benign nodules on CT.
Article in Japanese journal of radiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Whole-tumor radiomics analysis of T2-weighted imaging in differentiating neuroblastoma from ganglioneuroblastoma/ganglioneuroma in children: an exploratory study.Abdominal radiology (New York) · 2023Trial
- Computed tomography radiomics-based distinction of invasive adenocarcinoma from minimally invasive adenocarcinoma manifesting as pure ground-glass nodules with bubble-like signs.General thoracic and cardiovascular surgery · 2022Trial
- Texture Analysis of Non-Contrast-Enhanced Computed Tomography for Assessing Angiogenesis and Survival of Soft Tissue Sarcoma.Journal of computer assisted tomographyTrial
- Density homogeneity as a crucial CT indicator for differentiating malignant and benign subcentimeter solid pulmonary nodules: A retrospective multi-center study.Insights into imaging · 2026Article
- Article
- Correlation between imaging features of pure ground-glass opacities and pathological subtypes of lung minimally invasive adenocarcinoma and precursor lesions.Scientific reports · 2025Article
- Radiomic analysis of patient and interorgan heterogeneity in response to immunotherapies and BRAF-targeted therapy in metastatic melanoma.Journal for immunotherapy of cancer · 2025Article
- Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma.Insights into imaging · 2024Article
- Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study.La Radiologia medica · 2024Article
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- Non-small cell lung cancer in China.Cancer communications (London, England) · 2022Review
- Reproducibility of radiomic features of pulmonary nodules between low-dose CT and conventional-dose CT.Quantitative imaging in medicine and surgery · 2022Article
- A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification.Journal of healthcare engineering · 2022Review
- Quantification of Cancer-Developing Idiopathic Pulmonary Fibrosis Using Whole-Lung Texture Analysis of HRCT Images.Cancers · 2021Article
- Optimizing the timing of diagnostic testing after positive findings in lung cancer screening: a proof of concept radiomics study.Journal of translational medicine · 2021Article
- Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact.Cancer imaging : the official publication of the International Cancer Imaging Society · 2021Article
- Radiomics in lung cancer for oncologists.Journal of clinical and translational research · 2020Article
- Multiclassifier fusion based on radiomics features for the prediction of benign and malignant primary pulmonary solid nodules.Annals of translational medicine · 2020Article
- Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening.Quantitative imaging in medicine and surgery · 2019Article
- Solid Indeterminate Nodules with a Radiological Stability Suggesting Benignity: A Texture Analysis of Computed Tomography Images Based on the Kurtosis and Skewness of the Nodule Volume Density Histogram.Pulmonary medicine · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe purpose of our study was to assess pulmonary nodule characteristics using density histogram kurtosis and skewness and to distinguish malignant from benign nodules. MATERIALS AND
methodsNinety-three lung nodules on CT were analyzed, including 72 malignant and 21 benign nodules. They were completely solid or solid with limited ground-glass opacity. Based on their CT characteristics, nodules were categorized into type A, homogeneous nodules with uniform internal structures and clear margins, and type B, inhomogeneous nodules with heterogeneous structures or uneven margins. Kurtosis and skewness were calculated from density histograms to compare type A and B nodules and malignant and benign nodules. Receiver-operating characteristic (ROC) curves were generated to assess kurtosis and skewness for discriminating between different nodule types.
resultsType A nodules (n = 35) had greater kurtosis and reduced skewness (p < 0.001) compared to type B nodules (n = 58). Malignant tumor kurtosis was greater than that of benign nodules (type A, p < 0.05; type B, p = 0.001). Type B malignant tumors had reduced skewness compared to benign nodules (p < 0.05). ROC curves provided relatively high values for the area under the curve (0.71-0.83).
conclusionKurtosis and skewness assessments of density histograms may be useful for differentiating malignant from benign nodules.
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