Ernst Th. Scholten

Management of Lung Nodules Detected by Volume CT Scanning

BACKGROUND The use of multidetector computed tomography (CT) in lung-cancer screening trials involving subjects with an increased risk of lung cancer has highlighted the problem for the clinician of deciding on the best course of action when noncalcified pulmonary nodules are detected by CT. METHODS A total of 7557 participants underwent CT screening in years 1, 2, and 4 of a randomized trial of lung-cancer screening. We used software to evaluate a noncalcified nodule according to its volume or volume-doubling time. Growth was defined as an increase in volume of at least 25% between two scans. The first-round screening test was considered to be negative if the volume of a nodule was less than 50 mm(3), if it was 50 to 500 mm(3) but had not grown by the time of the 3-month follow-up CT, or if, in the case of those that had grown, the volume-doubling time was 400 days or more. RESULTS In the first and second rounds of screening, 2.6% and 1.8% of the participants, respectively, had a positive test result. In round one, the sensitivity of the screen was 94.6% (95% confidence interval [CI], 86.5 to 98.0) and the negative predictive value 99.9% (95% CI, 99.9 to 100.0). In the 7361 subjects with a negative screening result in round one, 20 lung cancers were detected after 2 years of follow-up. CONCLUSIONS Among subjects at high risk for lung cancer who were screened in three rounds of CT scanning and in whom noncalcified pulmonary nodules were evaluated according to volume and volume-doubling time, the chances of finding lung cancer 1 and 2 years after a negative first-round test were 1 in 1000 and 3 in 1000, respectively. (Current Controlled Trials number, ISRCTN63545820.)

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.

Volumetric computed tomography screening for lung cancer: three rounds of the NELSON trial.

Several medical associations recommended lung cancer screening by low-dose computed tomography scanning for high-risk groups. Counselling of the candidates on the potential harms and benefits and their lung cancer risk is a prerequisite for screening. In the NELSON trial, screenings are considered positive for (part) solid lung nodules with a volume >500 mm3 and for (part) solid or nonsolid nodules with a volume-doubling time <400 days. For this study, the performance of the NELSON strategy in three screening rounds was evaluated and risk calculations were made for a follow-up period of 5.5 years. 458 (6%) of the 7582 participants screened had a positive screen result and 200 (2.6%) were diagnosed with lung cancer. The positive screenings had a predictive value of 40.6% and only 1.2% of all scan results were false-positive. In a period of 5.5 years, the risk of screen-detected lung cancer strongly depends on the result of the first scan: 1.0% after a negative baseline result, 5.7% after an indeterminate baseline and 48.3% after a positive baseline. The screening strategy yielded few positive and false-positive scans with a reasonable positive predictive value. The 5.5-year lung cancer risk calculations aid clinicians in counselling candidates for lung cancer screening with low-dose computed tomography. 5.5-year lung cancer risk calculations aid clinicians in counselling for lung cancer screening with low-dose CT http://ow.ly/p9J3q

Smooth or attached solid indeterminate nodules Detected at baseline CT screening in the NELSON study: Cancer risk during 1 year of follow-up

PURPOSE To retrospectively determine whether baseline nodule characteristics at 3-month and 1-year volume doubling time (VDT) are predictive for lung cancer in solid indeterminate noncalcified nodules (NCNs) detected at baseline computed tomographic (CT) screening. MATERIALS AND METHODS The study, conducted between April 2004 and May 2006, was institutional review board approved. Patient consent was waived for this retrospective evaluation. NCNs between 5 and 10 mm in diameter (n = 891) were evaluated at 3 months and 1 year to assess growth (VDT < 400 days). Baseline assessments were related to growth at 3 months and 1 year by using chi(2) and Mann-Whitney U tests. Baseline assessments and growth were related to the presence of malignancy by using univariate and multivariate logistic regression analyses. RESULTS At 3 months and at 1 year, 8% and 1% of NCNs had grown, of which 15% and 50% were malignant, respectively. One-year growth was related to morphology (P < .01), margin (P < .0001), location (P < .001), and size (P < .01). All cancers were nonspherical and purely intraparenchymal, without attachment to vessels, the pleura, or fissures. In nonsmooth unattached nodules, a volume of 130 mm(3) or larger was the only predictor for malignancy (odds ratio, 6.3; 95% confidence interval [CI]: 1.7, 23.0). After the addition of information on the 3-month VDT, large volume (odds ratio, 4.9; 95% CI: 1.2, 20.1) and 3-month VDT (odds ratio, 15.6; 95% CI: 4.5, 53.5) helped predict malignancy. At 1 year, only the 1-year growth remained (odds ratio, 213.3; 95% CI: 18.7, 2430.9) as predictor for malignancy. CONCLUSION In smooth or attached solid indeterminate NCNs, no malignancies were found at 1-year follow-up. In nonsmooth purely intraparenchymal NCNs, size is the main baseline predictor for malignancy. When follow-up data are available, growth is a strong predictor for malignancy, especially at 1-year follow-up.

Detection of lung cancer through low-dose CT screening (NELSON): a prespecified analysis of screening test performance and interval cancers

BACKGROUND Low-dose CT screening is recommended for individuals at high risk of developing lung cancer. However, CT screening does not detect all lung cancers: some might be missed at screening, and others can develop in the interval between screens. The NELSON trial is a randomised trial to assess the effect of screening with increasing screening intervals on lung cancer mortality. In this prespecified analysis, we aimed to assess screening test performance, and the epidemiological, radiological, and clinical characteristics of interval cancers in NELSON trial participants assigned to the screening group. METHODS Eligible participants in the NELSON trial were those aged 50-75 years, who had smoked 15 or more cigarettes per day for more than 25 years or ten or more cigarettes for more than 30 years, and were still smoking or had quit less than 10 years ago. We included all participants assigned to the screening group who had attended at least one round of screening. Screening test results were based on volumetry using a two-step approach. Initially, screening test results were classified as negative, indeterminate, or positive based on nodule presence and volume. Subsequently, participants with an initial indeterminate result underwent follow-up screening to classify their final screening test result as negative or positive, based on nodule volume doubling time. We obtained information about all lung cancer diagnoses made during the first three rounds of screening, plus an additional 2 years of follow-up from the national cancer registry. We determined epidemiological, radiological, participant, and tumour characteristics by reassessing medical files, screening CTs, and clinical CTs. The NELSON trial is registered at www.trialregister.nl, number ISRCTN63545820. FINDINGS 15,822 participants were enrolled in the NELSON trial, of whom 7915 were assigned to low-dose CT screening with increasing interval between screens, and 7907 to no screening. We included 7155 participants in our study, with median follow-up of 8·16 years (IQR 7·56-8·56). 187 (3%) of 7155 screened participants were diagnosed with 196 screen-detected lung cancers, and another 34 (<1%; 19 [56%] in the first year after screening, and 15 [44%] in the second year after screening) were diagnosed with 35 interval cancers. For the three screening rounds combined, with a 2-year follow-up, sensitivity was 84·6% (95% CI 79·6-89·2), specificity was 98·6% (95% CI 98·5-98·8), positive predictive value was 40·4% (95% CI 35·9-44·7), and negative predictive value was 99·8% (95% CI 99·8-99·9). Retrospective assessment of the last screening CT and clinical CT in 34 patients with interval cancer showed that interval cancers were not visible in 12 (35%) cases. In the remaining cases, cancers were visible when retrospectively assessed, but were not diagnosed because of radiological detection and interpretation errors (17 [50%]), misclassification by the protocol (two [6%]), participant non-compliance (two [6%]), and non-adherence to protocol (one [3%]). Compared with screen-detected cancers, interval cancers were diagnosed at more advanced stages (29 [83%] of 35 interval cancers vs 44 [22%] of 196 screen-detected cancers diagnosed in stage III or IV; p<0·0001), were more often small-cell carcinomas (seven [20%] vs eight [4%]; p=0·003) and less often adenocarcinomas (nine [26%] vs 102 [52%]; p=0·005). INTERPRETATION Lung cancer screening in the NELSON trial yielded high specificity and sensitivity, with only a small number of interval cancers. The results of this study could be used to improve screening algorithms, and reduce the number of missed cancers. FUNDING Zorgonderzoek Nederland Medische Wetenschappen and Koningin Wilhelmina Fonds.

Characteristics of Lung Cancers Detected by Computer Tomography Screening in the Randomized NELSON Trial

RATIONALE The NELSON (Nederlands Leuvens Longkanker Screenings Onderzoek) trial is, with 15,822 participants, the largest European lung cancer computer tomography screening trial. A volumetry-based screening strategy, stringent criteria for a positive screening, and an increasing length of screening interval are particular features of the NELSON trial. OBJECTIVES To determine the effect of stringent referral criteria and increasing screening interval on the characteristics of screen-detected lung cancers, and to compare this across screening rounds, between sexes, and with other screening trials. METHODS All NELSON participants with screen-detected lung cancer in the first three rounds were included. Lung cancer stage at diagnosis, histological subtype, and tumor localization were compared between the screening rounds, the sexes, and with other screening trials. MEASUREMENTS AND MAIN RESULTS In the first three screening rounds, 200 participants were diagnosed with 209 lung cancers. Of these lung cancers, 70.8% were diagnosed at stage I and 8.1% at stage IIIB-IV, and 51.2% were adenocarcinomas. There was no significant difference in cancer stage, histology, or tumor localization across the screening rounds. Women were diagnosed at a significantly more favorable cancer stage than men. Compared with other trials, the screen-detected lung cancers of the NELSON trial were relatively more often diagnosed at stage I and less often at stage IIIB-IV. CONCLUSIONS Despite stringent criteria for a positive screening, an increasing length of screening interval, and few female participants, the screening strategy of the NELSON trial resulted in a favorable cancer stage distribution at diagnosis, which is essential for the effectiveness of our screening strategy. Clinical trial registered with www.trialregister.nl (ISRCTN63545820).

Impact of computed tomography screening for lung cancer on participants in a randomized controlled trial (NELSON trial)

Computed tomography (CT) screening is an important new tool for the early detection of lung cancer. In the current study, the authors assessed the discomfort associated with CT scanning and the subsequent wait for results and health‐related quality of life (HRQoL) over time.

Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.

Long-term effects of lung cancer computed tomography screening on health-related quality of life: the NELSON trial

The long-term effects of lung cancer computed tomography (CT) screening on health-related quality of life (HRQoL) have not yet been investigated. In the Dutch–Belgian Randomised Lung Cancer Screening Trial (NELSON trial), 1,466 participants received questionnaires before randomisation (T0), 2 months after baseline screening (screen group only; T1) and at 2-yr follow-up (T2). HRQoL was measured as generic HRQoL (12-item short-form questionnaire and EuroQoL questionnaire), anxiety (Spielberger State-Trait Anxiety Inventory) and lung cancer-specific distress (impact of event scale (IES)). Repeated measures of ANOVA were used to analyse differences between the screen and control groups, and between indeterminate (requiring a follow-up CT) and negative screening result groups. At T0 and T2 there were no significant differences in HRQoL scores over time between the screen and control groups, or between the indeterminate or negative second-round screening result group. There was a temporary increase in IES scores after an indeterminate baseline result (T0: mean 4.0 (95% CI 2.8–5.3); T1: mean 7.8 (95% CI 6.5–9.0); T2: mean 4.5 (95% CI 3.3–5.8)). At 2-yr follow-up, the HRQoL of screened subjects was similar to that of control subjects, the unfavourable short-term effects of an indeterminate baseline screening result had resolved and an indeterminate result at the second screening round had no impact on HRQoL.

Automatic Detection of Subsolid Pulmonary Nodules in Thoracic Computed Tomography Images

Subsolid pulmonary nodules occur less often than solid pulmonary nodules, but show a much higher malignancy rate. Therefore, accurate detection of this type of pulmonary nodules is crucial. In this work, a computer-aided detection (CAD) system for subsolid nodules in computed tomography images is presented and evaluated on a large data set from a multi-center lung cancer screening trial. The paper describes the different components of the CAD system and presents experiments to optimize the performance of the proposed CAD system. A rich set of 128 features is defined for subsolid nodule candidates. In addition to previously used intensity, shape and texture features, a novel set of context features is introduced. Experiments show that these features significantly improve the classification performance. Optimization and training of the CAD system is performed on a large training set from one site of a lung cancer screening trial. Performance analysis on an independent test from another site of the trial shows that the proposed system reaches a sensitivity of 80% at an average of only 1.0 false positive detections per scan. A retrospective analysis of the output of the CAD system by an experienced thoracic radiologist shows that the CAD system is able to find subsolid nodules which were not contained in the screening database.