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Contains fulltext : 221588.pdf (Publisher’s version ) (Closed access)

Item does not contain fulltext Lung cancer is a histologically, immunologically and therefore morphologically and functionally very heterogeneous group of neoplasms with the highest cancer mortality worldwide. Therefore, the range of diseases mimicking lung cancer is also very broad and includes congenital, infectious and inflammatory changes as well as other benign space-occupying lesions and other primary and secondary pulmonary neoplasms. The difficulty in radiology lies in the ability to diagnose lung cancer with a high degree of certainty. This must take the limits of the specific diagnosis, knowledge of the classical pitfalls and rare entities that can imitate lung cancer into consideration. Narrowing the differential diagnosis requires close interdisciplinary cooperation and consideration of the patient's clinical and medical history. An accurate analysis of the computed tomography (CT) pattern and distribution of the lesions as well as consideration of additional changes and involvement of other organ systems can be the key to the diagnosis. The use of fluorodeoxyglucose positron-emission tomography CT (FDG-PET-CT) is helpful only in a few mimics of lung cancer. The article describes clinical and radiological findings of mimics of lung cancer also pointing out the limitations of CT and PET-CT for the diagnosis.

Item does not contain fulltext Lung cancer is the most frequent cause of tumor-associated death and only has a good prognosis if detected at a very early tumor stage.For the first time the American National Lung Screening Trial (NLST) could prove that low-dose computed tomography (CT) screening is able to reduce lung cancer mortality by 20 \%.To date, however, three much smaller and therefore statistically underpowered European trials could not confirm the positive results of the NLST. The results of the largest European trial NELSON are expected within the next 2 years. In addition, there are a number of open or not yet satisfactorily answered questions, such as the definition of the appropriate screening population, the management of nodules detected by screening, the effects of over-diagnosis and the risk of cumulative radiation exposure.The success of the NLST prompted several predominantly American professional societies to issue a positive recommendation about the implementation of lung cancer screening in a population at risk. However, potentially conflicting results of European studies and a number of not yet optimized issues justify caution and call for a pooled analysis of European studies in order to provide statistically sound results and to ensure a high efficiency of screening with respect to the radiation applied, mental and physical patient burden and, last but not least, the financial efforts.

Item does not contain fulltext Chest radiography still represents the most commonly performed X-ray examination because it is readily available, requires low radiation doses and is relatively inexpensive. However, as previously published, many initially undetected lung nodules are retrospectively visible in chest radiographs.The great improvements in detector technology with the increasing dose efficiency and improved contrast resolution provide a better image quality and reduced dose needs.The dual energy acquisition technique and advanced image processing methods (e.g. digital bone subtraction and temporal subtraction) reduce the anatomical background noise by reduction of overlapping structures in chest radiography. Computer-aided detection (CAD) schemes increase the awareness of radiologists for suspicious areas.The advanced image processing methods show clear improvements for the detection of pulmonary lung nodules in chest radiography and strengthen the role of this method in comparison to 3D acquisition techniques, such as computed tomography (CT).Many of these methods will probably be integrated into standard clinical treatment in the near future. Digital software solutions offer advantages as they can be easily incorporated into radiology departments and are often more affordable as compared to hardware solutions.

Item does not contain fulltext The finding of subsolid pulmonary nodules poses a frequent problem in the daily routine of the radiologist. The biological behavior of such subsolid lesions differs significantly from solid nodules. The risk of malignancy is significantly higher in subsolid nodules as compared to solid or purely ground glass opacities or nodules. The recommendations regarding the diagnostic management of subsolid nodules have been adapted according to the tendency of growth and the risk of malignancy. A benign etiology is also seen quite often in subsolid lesions and in this case they will show a reduction of size or disappear completely by the follow-up examination. Therefore, in many cases a short-term follow-up examination is primarily recommended. As the findings will often show no changes for a long period of time, further annual follow-up examinations over a longer, not yet specified period of time are recommended. Subsolid lesions that grow in size and/or show an increase in density or develop a solid part within a ground glass lesion should remain as suspected malignancies until proven otherwise.

Item does not contain fulltext Interstitial 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.

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Contains fulltext : 138328.pdf (Publisher’s version ) (Open Access) Many medical image analysis systems require segmentation of the structures of interest as a first step. For scans with gross pathology, automatic segmentation methods may fail. The authors' aim is to develop a versatile, fast, and reliable interactive system to segment anatomical structures. In this study, this system was used for segmenting lungs in challenging thoracic computed tomography (CT) scans.In volumetric thoracic CT scans, the chest is segmented and divided into 3D volumes of interest (VOIs), containing voxels with similar densities. These VOIs are automatically labeled as either lung tissue or nonlung tissue. The automatic labeling results can be corrected using an interactive or a supervised interactive approach. When using the supervised interactive system, the user is shown the classification results per slice, whereupon he/she can adjust incorrect labels. The system is retrained continuously, taking the corrections and approvals of the user into account. In this way, the system learns to make a better distinction between lung tissue and nonlung tissue. When using the interactive framework without supervised learning, the user corrects all incorrectly labeled VOIs manually. Both interactive segmentation tools were tested on 32 volumetric CT scans of pigs, mice and humans, containing pulmonary abnormalities.On average, supervised interactive lung segmentation took under 9 min of user interaction. Algorithm computing time was 2 min on average, but can easily be reduced. On average, 2.0\% of all VOIs in a scan had to be relabeled. Lung segmentation using the interactive segmentation method took on average 13 min and involved relabeling 3.0\% of all VOIs on average. The resulting segmentations correspond well to manual delineations of eight axial slices per scan, with an average Dice similarity coefficient of 0.933.The authors have developed two fast and reliable methods for interactive lung segmentation in challenging chest CT images. Both systems do not require prior knowledge of the scans under consideration and work on a variety of scans.

Contains fulltext : 231282.pdf (Publisher’s version ) (Open Access) Radboud University, 06 april 2021 Promotores : Schaefer-Prokop, C.M., Ginneken, B. van Co-promotor : Jacobs, C. 303 p.

Contains fulltext : 191598.pdf (Publisher’s version ) (Open Access) Radboud University, 12 juni 2018 Promotores : Ginneken, B. van, Schaefer-Prokop, C.M. Co-promotor : Jacobs, C.

Contains fulltext : 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 2015 Promotores : Ginneken, B. van, Schaefer-Prokop, C.M. Co-promotor : Rikxoort, E.M. van