ReviewJournal of digital imaging2018
Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture.
Review in Journal of digital imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images.Journal of medical systems · 2019Pooled it
- Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study.European radiology · 2022Trial
- Article
- Value of dual-layer spectral detector computed tomography in the diagnosis of benign/malignant solid solitary pulmonary nodules and establishment of a prediction model.Frontiers in oncology · 2023Article
- Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans.Sensors (Basel, Switzerland) · 2022Article
- Computed Tomography Features of Lung Structure Have Utility for Differentiating Malignant and Benign Pulmonary Nodules.Chronic obstructive pulmonary diseases (Miami, Fla.) · 2022Article
- Radiomic Quantification for MRI Assessment of Sacroiliac Joints of Patients with Spondyloarthritis.Journal of digital imaging · 2022Article
- A Clinical Decision Support System for Diabetes Patients with Deep Learning: Experience of a Taiwan Medical Center.International journal of medical sciences · 2022Article
- Role ofJournal of clinical medicine · 2021Article
- The Effects of Perinodular Features on Solid Lung Nodule Classification.Journal of digital imaging · 2021Article
- Value of Shape and Texture Features fromDiagnostics (Basel, Switzerland) · 2020Article
- Classification of lung nodules based on CT images using squeeze-and-excitation network and aggregated residual transformations.La Radiologia medica · 2020Article
- CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms.International journal of computer assisted radiology and surgery · 2020Article
- Differentiation of non-small cell lung cancer and histoplasmosis pulmonary nodules: insights from radiomics model performance compared with clinician observers.Translational lung cancer research · 2019Article
- Post-imaging pulmonary nodule mathematical prediction models: are they clinically relevant?European radiology · 2019Observational
- Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT.Medical physics · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is the leading cause of cancer-related deaths in the world, and one of its manifestations occurs with the appearance of pulmonary nodules. The classification of pulmonary nodules may be a complex task to specialists due to temporal, subjective, and qualitative aspects. Therefore, it is important to integrate computational tools to the early pulmonary nodule classification process, since they have the potential to characterize objectively and quantitatively the lesions. In this context, the goal of this work is to perform the classification of pulmonary nodules based on image features of texture and margin sharpness. Computed tomography scans were obtained from a publicly available image database. Texture attributes were extracted from a co-occurrence matrix obtained from the nodule volume. Margin sharpness attributes were extracted from perpendicular lines drawn over the borders on all nodule slices. Feature selection was performed by different algorithms. Classification was performed by several machine learning classifiers and assessed by the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Highest classification performance was obtained by a random forest algorithm with all 48 extracted features. However, a decision tree using only two selected features obtained statistically equivalent performance on sensitivity and specificity.
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Registered trials
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.