Radiomics analysis of pulmonary nodules in low‐dose CT for early detection of lung cancer
Sign Up to like & getrecommendations! Published in 2018 at "Medical Physics"
DOI: 10.1002/mp.12820
Abstract: PURPOSE To develop a radiomics prediction model to improve pulmonary nodule (PN) classification in low-dose CT. To compare the model with the American College of Radiology (ACR) Lung CT Screening Reporting and Data System (Lung-RADS)… read more here.
Keywords: low dose; model; lung rads; svm lasso ... See more keywords
Examining Gender Differences in Lung Cancer Screening
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DOI: 10.1007/s10900-020-00826-8
Abstract: It is unknown if gender influences outcome of lung cancer screening with Low Dose CT (LDCT), especially with frequent and continued underrepresentation of women in clinical trials. We examined a balanced cohort of men and… read more here.
Keywords: females males; cancer screening; gender; lung cancer ... See more keywords
Lung-RADS Category 4X: Does It Improve Prediction of Malignancy in Subsolid Nodules?
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DOI: 10.1148/radiol.2017161624
Abstract: Purpose To evaluate the added value of Lung CT Screening Reporting and Data System (Lung-RADS) assessment category 4X over categories 3, 4A, and 4B for differentiating between benign and malignant subsolid nodules (SSNs). Materials and… read more here.
Keywords: subsolid nodules; category; malignancy; lung rads ... See more keywords
Cancer Risk in Subsolid Nodules in the National Lung Screening Trial.
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DOI: 10.1148/radiol.2019190905
Abstract: Background Subsolid pulmonary nodules, comprising pure ground-glass nodules (GGNs) and part-solid nodules (PSNs), have a high risk of indolent malignancy. Lung Imaging Reporting and Data System (Lung-RADS) nodule management guidelines are based on expert opinion… read more here.
Keywords: subsolid nodules; risk; trial; malignancy ... See more keywords
Automatic Lung-RADS™ classification with a natural language processing system.
Sign Up to like & getrecommendations! Published in 2017 at "Journal of thoracic disease"
DOI: 10.21037/jtd.2017.08.13
Abstract: Background Our aim was to train a natural language processing (NLP) algorithm to capture imaging characteristics of lung nodules reported in a structured CT report and suggest the applicable Lung-RADS™ (LR) category. Methods Our study… read more here.
Keywords: natural language; language processing; lung rads; language ... See more keywords
Effectiveness and Feasibility of Complementary Lung-RADS Version 1.1 in Risk Stratification for pGGN in LDCT Lung Cancer Screening in a Chinese Population
Sign Up to like & getrecommendations! Published in 2020 at "Cancer Management and Research"
DOI: 10.2147/cmar.s232269
Abstract: Purpose To evaluate the effectiveness of using a modified lung imaging reporting and data system (Lung-RADS) for risk stratification of pure ground-glass nodules (pGGNs) in low-dose computed tomography (LDCT) for lung cancer (LC) screenings in… read more here.
Keywords: lung; cancer; ldct lung; risk stratification ... See more keywords
A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans
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DOI: 10.3390/cancers13112781
Abstract: Simple Summary The great majority of pulmonary nodules on screening CT scans are benign (95%). Due to inaccurate diagnoses of granulomas from adenocarcinomas on CT scans, many patients with benign nodules are subjected to unnecessary… read more here.
Keywords: lung rads; contrast scans; non contrast; lung ... See more keywords