1,721,076 research outputs found
Lung cancer screening: use the scan to decide who to scan when
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231282.pdf (Publisher’s version ) (Open Access)Radboud University, 06 april 2021Promotores : Schaefer-Prokop, C.M., Ginneken, B. van Co-promotor : Jacobs, C.303 p
Malignancy risk estimation of subsolid nodules
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191598.pdf (Publisher’s version ) (Open Access)Radboud University, 12 juni 2018Promotores : Ginneken, B. van, Schaefer-Prokop, C.M. Co-promotor : Jacobs, C
Advanced processing in chest radiography: impact on observer performance
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142565pub.pdf (Publisher’s version ) (Open Access)Radboud Universiteit Nijmegen, 10 september 2015Promotores : Karssemeijer, N., Ginneken, B. van Co-promotor : Schaefer-Prokop, C.M
Automatic detection and characterization of pulmonary nodules in thoracic CT scans
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145307.pdf (Publisher’s version ) (Open Access)Lung cancer is the most deadly cancer in both men and women. This can be largely attributed to the fact that lung cancer is usually detected in a late stage. If the disease is detected in an early stage, the survival rate is much better. Therefore, early detection of lung cancer, in which it is still treatable, is of major importance to reduce lung cancer mortality.
Early stage lung cancer manifests itself as pulmonary nodules, which are described as round opacities, well or poorly defined, measuring up to 3 cm in diameter. Thin-slice helical chest CT scans have a sub-millimeter resolution at which small pulmonary nodules can be detected. Computer-aided detection of lung nodules has the potential to increase reader sensitivity for the detection of pulmonary nodules and may reduce reading time. Furthermore, automated characterization of pulmonary nodules may assist the radiologist in assessing the likelihood of malignancy of lung nodules.
In this thesis, novel detection and characterization systems for pulmonary nodules are described. We proposed a novel subsolid CAD system which aims to detect subsolid nodules, a system to detect and quantify micronodules, and a system to automatically detect interval change between consecutive CT scans. All three systems were evaluated on large datasets and showed promising performance. In addition, we performed a comparative study with three CAD algorithms on the largest publicly available reference database for pulmonary nodules. Next, we described a method which automatically classifies pulmonary nodules into solid, part-solid, or non-solid nodules. This is crucial for selecting the appropriate workup for pulmonary nodules. Finally, we discussed how the developed methods can be efficiently integrated into clinical practice.Radboud Universiteit Nijmegen, 19 november 2015Promotores : Ginneken, B. van, Schaefer-Prokop, C.M. Co-promotor : Rikxoort, E.M. va
Advances in digital chest radiography: impact on reader performance
Full text linkDigitalisering binnen de radiologie heeft de nodige verbeteringen opgeleverd, onder andere voor de longfoto. Vroege stadia van longkanker en kleine uitzaaiingen naar de longen worden geregeld gemist op scans, maar computerprogramma’s kunnen deze gemiste afwijkingen soms wel detecteren. De programma’s markeren echter ook normale structuren; dat kan leiden tot fout-positieven. Diederick De Boo concentreerde zich op de interpretatie van resultaten van computerprogramma’s en op de mogelijkheid de radioloog hierin te trainen
[HRCT patterns of the most important interstitial lung diseases]
No full textItem does not contain fulltextInterstitial lung diseases are a mixed group of diffuse parenchymal lung diseases which can have an acute or chronic course. Idiopathic diseases and diseases with an underlying cause (e.g. collagen vascular diseases) share the same patterns. Thin section computed tomography (CT) plays a central role in the diagnostic work-up. The article describes the most important interstitial lung diseases following a four pattern approach with a predominant nodular or reticular pattern or a pattern with increased or decreased lung density
Perifissural nodules: ready for application into lung cancer CT screening?
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229472.pdf (Publisher’s version ) (Open Access
Chest Radiography in COVID-19: No Role in Asymptomatic and Oligosymptomatic Disease
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232880.pdf (Publisher’s version ) (Open Access
[Radiological evaluation of incidental pulmonary nodules]
No full textItem does not contain fulltextSince the widespread use of computed tomography (CT), the detection of pulmonary nodules has considerably increased and has become part of the daily clinical routine. In the evaluation of pulmonary nodules, malignant nodules have to be differentiated from benign pulmonary nodules with a high level of confidence. The diagnostic approach for pulmonary nodules depends on the pretest probability for malignancy. For indeterminate pulmonary nodules 8 mm, management is based on patient surgical risk and pretest probability for malignancy. Either CT follow-up alone, 18-fluorodeoxyglucose-positron emission tomography (FDG-PET) or non-surgical biopsy for tissue diagnosis are utilized to evaluate the lesions. For pulmonary nodules with a high pretest probability for malignancy, surgical resection is recommended unless specifically contraindicated
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