ArticleClinical lung cancer2018
Comparison Between Radiological Semantic Features and Lung-RADS in Predicting Malignancy of Screen-Detected Lung Nodules in the National Lung Screening Trial.
Article in Clinical lung cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 25 citations in OpenAlex.
- Value of a deep learning-based algorithm for detecting Lung-RADS category 4 nodules on chest radiographs in a health checkup population: estimation of the sample size for a randomized controlled trial.European radiology · 2022Trial
- Lessons Learned From International Lung Cancer Screening Trials; People at Risk Deserve Screening for Early Detection.Respirology (Carlton, Vic.) · 2025Review
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- Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge.IEEE transactions on medical imaging · 2021Article
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- Optimizing the timing of diagnostic testing after positive findings in lung cancer screening: a proof of concept radiomics study.Journal of translational medicine · 2021Article
- Integration of PET/CT Radiomics and Semantic Features for Differentiation between Active Pulmonary Tuberculosis and Lung Cancer.Molecular imaging and biology · 2021Article
- Radiomics in immuno-oncology.Immuno-oncology technology · 2021Review
- Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening.Scientific reports · 2020Article
- Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram.Cancer communications (London, England) · 2020Article
- Multi-Window CT Based Radiological Traits for Improving Early Detection in Lung Cancer Screening.Cancer management and research · 2020Article
- Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas.Radiology · 2019Article
- Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features.Tomography (Ann Arbor, Mich.) · 2019Article
- A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning.PloS one · 2019Article
- Relationship between Circulating Inflammation Proteins and Lung Cancer Diagnosis in the National Lung Screening Trial.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2019Article
- Representation of Deep Features using Radiologist defined Semantic Features.Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks · 2018Article
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7 authors at 2 institutions in 2 countries.
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Abstract
rationaleLung computed tomography (CT) Screening Reporting and Data System (lung-RADS) has standardized follow-up and management decisions in lung cancer screening. To date, little is known how lung-RADS classification compares with radiological semantic features in risk prediction and diagnostic discrimination.
objectivesTo compare the performance of radiological semantic features and lung-RADS in predicting nodule malignancy in lung cancer screening.
methodsWe used data and low-dose CT (LDCT) images from the National Lung Screening Trial (NLST). The training cohort contained 60 patients with screen-detected incident lung cancers who had a positive baseline screen (T0) that was not diagnosed and then was diagnosed at second follow-up (T2), and 139 nodule-positive controls who had 3 consecutive positive screens (T0 to T2) that were not diagnosed as lung cancer. The testing cohort included 40 patients with incident lung cancers that were diagnosed at first follow-up (T1) and 40 nodule-positive controls. Twenty-four semantic features were scored on a point scale from the LDCT images. Multivariable linear predictor model was built on the semantic features and the performances were compared with lung-RADS in 3 screening rounds. We also combined non-size-based semantic features with lung-RADS to improve malignancy detection.
resultsAt T0, the average area under the receiver operating characteristic curve (AUROC) for border definition in risk prediction was 0.72. The average AUROC for contour at T1 in risk prediction and T2 in diagnostic discrimination was 0.82 and 0.88, respectively. By comparison, the average AUROC of lung-RADS at T0, T1 and T2 were 0.60, 0.76 and 0.87, respectively. The combined model of the semantic features and lung-RADS shows improvement with AUROCs of 0.74, 0.88 and 0.96 at T0, T1, and T2, respectively, achieved by adding border definition (at T0) or contour (at T1 and T2).
conclusionWe find semantic features defined by border definition and contour performed similar to lung-RADS at follow-up time point and outperformed lung-RADS at baseline. These semantics alongside of lung-RADS shows improved performance to detect malignancy.
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