ArticleJournal of medical imaging (Bellingham, Wash.)2018
Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography.
Article in Journal of medical imaging (Bellingham, Wash.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- A Novel Methylation-based Model for Prognostic Prediction in Lung Adenocarcinoma.Current genomics · 2024Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- A Survey of Dental Caries Segmentation and Detection Techniques.TheScientificWorldJournal · 2022Review
- Article
- Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans.European journal of cancer (Oxford, England : 1990) · 2021Article
- Prediction of squamous cell carcinoma cases from squamous cell hyperplasia in throat lesions using CT radiomics model.Saudi medical journal · 2021Article
- Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges.Neuro-oncology advances · 2020Article
- Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study.Lung cancer (Amsterdam, Netherlands) · 2020Article
- Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non-Small Cell Lung Cancer.Cancer immunology research · 2020Article
- Comparison of Radiomic Models Based on Low-Dose and Standard-Dose CT for Prediction of Adenocarcinomas and Benign Lesions in Solid Pulmonary Nodules.Frontiers in oncology · 2020Article
- Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features.Lung cancer (Amsterdam, Netherlands) · 2019Article
- Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma.Radiology. Artificial intelligence · 2019Article
- Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas.Radiology · 2019Article
- The Value of Topological Radiomics Analysis in Predicting Malignant Risk of Pulmonary Ground-Glass Nodules: A Multi-Center Study.Technology in cancer research & treatmentArticle
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15 authors.
Funding
Abstract
Differentiation between benign and malignant nodules is a problem encountered by radiologists when visualizing computed tomography (CT) scans. Adenocarcinomas and granulomas have a characteristic spiculated appearance and may be fluorodeoxyglucose avid, making them difficult to distinguish for human readers. In this retrospective study, we aimed to evaluate whether a combination of radiomic texture and shape features from noncontrast CT scans can enable discrimination between granulomas and adenocarcinomas. Our study is composed of CT scans of 195 patients from two institutions, one cohort for training ([Formula: see text]) and the other ([Formula: see text]) for independent validation. A set of 645 three-dimensional texture and 24 shape features were extracted from CT scans in the training cohort. Feature selection was employed to identify the most informative features using this set. The top ranked features were also assessed in terms of their stability and reproducibility across the training and testing cohorts and between scans of different slice thickness. Three different classifiers were constructed using the top ranked features identified from the training set. These classifiers were then validated on the test set and the best classifier (support vector machine) yielded an area under the receiver operating characteristic curve of 77.8%.
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