Mathias Prokop

Diagnostic accuracy of 64-slice computed tomography coronary angiography: a prospective, multicenter, multivendor study.

OBJECTIVES This study sought to determine the diagnostic accuracy of 64-slice computed tomographic coronary angiography (CTCA) to detect or rule out significant coronary artery disease (CAD). BACKGROUND CTCA is emerging as a noninvasive technique to detect coronary atherosclerosis. METHODS We conducted a prospective, multicenter, multivendor study involving 360 symptomatic patients with acute and stable anginal syndromes who were between 50 and 70 years of age and were referred for diagnostic conventional coronary angiography (CCA) from September 2004 through June 2006. All patients underwent a nonenhanced calcium scan and a CTCA, which was compared with CCA. No patients or segments were excluded because of impaired image quality attributable to either coronary motion or calcifications. Patient-, vessel-, and segment-based sensitivities and specificities were calculated to detect or rule out significant CAD, defined as >or=50% lumen diameter reduction. RESULTS The prevalence among patients of having at least 1 significant stenosis was 68%. In a patient-based analysis, the sensitivity for detecting patients with significant CAD was 99% (95% confidence interval [CI]: 98% to 100%), specificity was 64% (95% CI: 55% to 73%), positive predictive value was 86% (95% CI: 82% to 90%), and negative predictive value was 97% (95% CI: 94% to 100%). In a segment-based analysis, the sensitivity was 88% (95% CI: 85% to 91%), specificity was 90% (95% CI: 89% to 92%), positive predictive value was 47% (95% CI: 44% to 51%), and negative predictive value was 99% (95% CI: 98% to 99%). CONCLUSIONS Among patients in whom a decision had already been made to obtain CCA, 64-slice CTCA was reliable for ruling out significant CAD in patients with stable and unstable anginal syndromes. A positive 64-slice CTCA scan often overestimates the severity of atherosclerotic obstructions and requires further testing to guide patient management.

Risk-based selection from the general population in a screening trial : Selection criteria, recruitment and power for the Dutch-Belgian randomised lung cancer multi-slice CT screening trial (NELSON)

A method to obtain the optimal selection criteria, taking into account available resources and capacity and the impact on power, is presented for the Dutch‐Belgian randomised lung cancer screening trial (NELSON). NELSON investigates whether 16‐detector multi‐slice computed tomography screening will decrease lung cancer mortality compared to no screening. A questionnaire was sent to 335,441 (mainly) men, aged 50–75. Smoking exposure (years smoked, cigarettes/day, years quit) was determined, and expected lung cancer mortality was estimated for different selection scenarios for the 106,931 respondents, using lung cancer mortality data by level of smoking exposure (US Cancer Prevention Study I and II). Selection criteria were chosen so that the required response among eligible subjects to reach sufficient sample size was minimised and the required sample size was within our capacity. Inviting current and former smokers (quit ≤ 10 years ago) who smoked >15 cigarettes/day during >25 years or >10 cigarettes/day during >30 years was most optimal. With a power of 80%, 17,300–27,900 participants are needed to show a 20–25% lung cancer mortality reduction 10 years after randomisation. Until October 18, 2005 11,103 (first recruitment round) and 4,325 (second recruitment round) (total = 15,428) participants have been randomised. Selecting participants for lung cancer screening trials based on risk estimates is feasible and helpful to minimize sample size and costs. When pooling with Danish trial data (n = ±4,000) NELSON is the only trial without screening in controls that is expected to have 80% power to show a lung cancer mortality reduction of at least 25% 10 years after randomisation.

Relation of epicardial and pericoronary fat to coronary atherosclerosis and coronary artery calcium in patients undergoing coronary angiography.

Fat surrounding coronary arteries might aggravate coronary artery disease (CAD). We investigated the relation between epicardial adipose tissue (EAT) and pericoronary fat and coronary atherosclerosis and coronary artery calcium (CAC) in patients with suspected CAD and whether this relation is modified by total body weight. This was a cross-sectional study of 128 patients with angina pectoris (61 +/- 6 years of age) undergoing coronary angiography. EAT volume and pericoronary fat thickness were measured with cardiac computed tomography. Severity of coronary atherosclerosis was assessed by the number of stenotic (> or =50%) coronary vessels; extent of CAC was determined by the Agatston score. Patients were stratified for median total body weight (body mass index [BMI] 27 kg/m(2)). Overall, EAT and pericoronary fat were not associated with severity of coronary atherosclerosis and extent of CAC. In patients with low BMI, those with multivessel disease had increased EAT volume (100 vs 67 cm(3), p = 0.04) and pericoronary fat thickness (9.8 vs 8.4 mm, p = 0.06) compared with those without CAD. Also, patients with severe CAC had increased EAT volume (108.0 vs 69 cm(3), p = 0.02) and pericoronary fat thickness (10.0 vs 8.2 mm, p value = 0.01) compared with those with minimal/absent CAC. In conclusion, EAT and pericoronary fat were not associated with severity of coronary atherosclerosis and CAC in patients with suspected CAD. However, in those with low BMI, increased EAT and pericoronary fat were related to more severe coronary atherosclerosis and CAC. Fat surrounding coronary arteries may be involved in the process of coronary atherosclerosis, although this is different for patients with low and high BMIs.

General principles of MDCT.

Multidetector CT (MDCT, multislice CT, multidetector-row CT, multisection CT) represents a breakthrough in CT technology. It has transformed CT from an transaxial cross-sectional technique into a true 3D imaging modality that allows for arbitrary cut planes as well as excellent 3D displays of the data volume. Multislice CT scanners provide a huge gain in performance that can be used to reduce scan time, to reduce section collimation, or to increase scan length substantially. The following article will provide an overview of the principles of multislice CT scanning. It describes the various detector systems and gives an introduction to the most important acquisition and reconstruction parameters. The article describes how reconstruction of thick multiplanar reformations can be used to take advantage of the 3D capabilities of multislice CT while keep radiation exposure to a minimum.

Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017

The Fleischner Society Guidelines for management of solid nodules were published in 2005, and separate guidelines for subsolid nodules were issued in 2013. Since then, new information has become available; therefore, the guidelines have been revised to reflect current thinking on nodule management. The revised guidelines incorporate several substantive changes that reflect current thinking on the management of small nodules. The minimum threshold size for routine follow-up has been increased, and recommended follow-up intervals are now given as a range rather than as a precise time period to give radiologists, clinicians, and patients greater discretion to accommodate individual risk factors and preferences. The guidelines for solid and subsolid nodules have been combined in one simplified table, and specific recommendations have been included for multiple nodules. These guidelines represent the consensus of the Fleischner Society, and as such, they incorporate the opinions of a multidisciplinary international group of thoracic radiologists, pulmonologists, surgeons, pathologists, and other specialists. Changes from the previous guidelines issued by the Fleischner Society are based on new data and accumulated experience.

Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection

Lung segmentation is a prerequisite for automated analysis of chest CT scans. Conventional lung segmentation methods rely on large attenuation differences between lung parenchyma and surrounding tissue. These methods fail in scans where dense abnormalities are present, which often occurs in clinical data. Some methods to handle these situations have been proposed, but they are too time consuming or too specialized to be used in clinical practice. In this article, a new hybrid lung segmentation method is presented that automatically detects failures of a conventional algorithm and, when needed, resorts to a more complex algorithm, which is expected to produce better results in abnormal cases. In a large quantitative evaluation on a database of 150 scans from different sources, the hybrid method is shown to perform substantially better than a conventional approach at a relatively low increase in computational cost.

Pulmonary Ground-Glass Nodules: Increase in Mass as an Early Indicator of Growth

PURPOSE To compare manual measurements of diameter, volume, and mass of pulmonary ground-glass nodules (GGNs) to establish which method is best for identifying malignant GGNs by determining change across time. MATERIALS AND METHODS In this ethics committee-approved retrospective study, baseline and follow-up CT examinations of 52 GGNs detected in a lung cancer screening trial were included, resulting in 127 GGN data sets for evaluation. Two observers measured GGN diameter with electronic calipers, manually outlined GGNs to obtain volume and mass, and scored whether a solid component was present. Observer 1 repeated all measurements after 2 months. Coefficients of variation and limits of agreement were calculated by using Bland-Altman methods. In a subgroup of GGNs containing all resected malignant lesions, the ratio between intraobserver variability and growth (growth-to-variability ratio) was calculated for each measurement technique. In this subgroup, the mean time for growth to exceed the upper limit of agreement of each measurement technique was determined. RESULTS The kappa values for intra- and interobserver agreement for identifying a solid component were 0.55 and 0.38, respectively. Intra- and interobserver coefficients of variation were smallest for GGN mass (P < .001). Thirteen malignant GGNs were resected. Mean growth-to-variability ratios were 11, 28, and 35 for diameter, volume, and mass, respectively (P = .03); mean times required for growth to exceed the upper limit of agreement were 715, 673, and 425 days, respectively (P = .02). CONCLUSION Mass measurements can enable detection of growth of GGNs earlier and are subject to less variability than are volume or diameter measurements.

Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study

Numerous publications and commercial systems are available that deal with automatic detection of pulmonary nodules in thoracic computed tomography scans, but a comparative study where many systems are applied to the same data set has not yet been performed. This paper introduces ANODE09 ( http://anode09.isi.uu.nl), a database of 55 scans from a lung cancer screening program and a web-based framework for objective evaluation of nodule detection algorithms. Any team can upload results to facilitate benchmarking. The performance of six algorithms for which results are available are compared; five from academic groups and one commercially available system. A method to combine the output of multiple systems is proposed. Results show a substantial performance difference between algorithms, and demonstrate that combining the output of algorithms leads to marked performance improvements.

A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification

A scheme for the automatic detection of nodules in thoracic computed tomography scans is presented and extensively evaluated. The algorithm uses the local image features of shape index and curvedness in order to detect candidate structures in the lung volume and applies two successive k-nearest-neighbour classifiers in the reduction of false-positives. The nodule detection system is trained and tested on three databases extracted from a large-scale experimental screening study. The databases are constructed in order to evaluate the algorithm on both randomly chosen screening data as well as data containing higher proportions of nodules requiring follow-up. The system results are extensively evaluated including performance measurements on specific nodule types and sizes within the databases and on lesions which later proved to be malignant. In a random selection of 813 scans from the screening study a sensitivity of 80% with an average 4.2 false-positives per scan is achieved. The detection results presented are a realistic measure of a CAD system performance in a low-dose screening study which includes a diverse array of nodules of many varying sizes, types and textures.

British Thoracic Society guidelines for the investigation and management of pulmonary nodules: accredited by NICE

This guideline is based on a comprehensive review of the literature on pulmonary nodules and expert opinion. Although the management pathway for the majority of nodules detected is straightforward it is sometimes more complex and this is helped by the inclusion of detailed and specific recommendations and the 4 management algorithms below. The Guideline Development Group (GDG) wanted to highlight the new research evidence which has led to significant changes in management recommendations from previously published guidelines. These include the use of two malignancy prediction calculators (section ‘Initial assessment of the probability of malignancy in pulmonary nodules’, algorithm 1) to better characterise risk of malignancy. There are recommendations for a higher nodule size threshold for follow-up (≥5 mm or ≥80 mm3) and a reduction of the follow-up period to 1 year for solid pulmonary nodules; both of these will reduce the number of follow-up CT scans (sections ‘Initial assessment of the probability of malignancy in pulmonary nodules’ and ‘Imaging follow-up’, algorithms 1 and 2). Volumetry is recommended as the preferred measurement method and there are recommendations for the management of nodules with extended volume doubling times (section ‘Imaging follow-up’, algorithm 2). Acknowledging the good prognosis of sub-solid nodules (SSNs), there are recommendations for less aggressive options for their management (section ‘Management of SSNs’, algorithm 3). The guidelines provide more clarity in the use of further imaging, with ordinal scale reporting for PET-CT recommended to facilitate incorporation into risk models (section ‘Further imaging in management of pulmonary nodules’) and more clarity about the place of biopsy (section ‘Non-imaging tests and non-surgical biopsy’, algorithm 4). There are recommendations for the threshold for treatment without histological confirmation (sections ‘Surgical excision biopsy’ and ‘Non-surgical treatment without pathological confirmation of malignancy’, algorithm 4). Finally, and possibly most importantly, there are evidence-based recommendations about the information that people …