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J.H. Pedersen

Moderator of

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    MS 24 - CT Screening: Minimize Harm/Cost and Risk Assessment (ID 42)

    • Event: WCLC 2015
    • Type: Mini Symposium
    • Track: Screening and Early Detection
    • Presentations: 4
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      MS24.01 - Definition of Positive Cases and False Positives (ID 1954)

      14:20 - 14:40  |  Author(s): D.F. Yankelevitz

      • Abstract
      • Presentation

      Abstract:
      With the ever increasing acceptance of CT screening the need to now minimize harms becomes even greater. One of the harms which occurs with the greatest frequency are “false positives” as they can lead to unnecessary additional work up, sometimes invasive, added cost, and cause anxiety for the person being screened. The term “false positive” is somewhat confusing and is defined differently by different groups. In the computer aided diagnosis domain, it refers to a finding that does not represent a nodule and is mistakenly labelled by the computer to represent nodule. Most frequently this is a blood vessel. Thus, positive results are nodules (often described as actionable based on a size criteria) and false positives are findings not representing nodules. In the clinical domain, when interpreting a CT scan, a positive finding is something that meets a specified definition to be considered a positive result. A positive finding is not something that is inherent to the image but requires certain criteria to be met. Thus, a nodule by itself is not necessarily a positive finding, but must meet certain criteria to be considered positive. Typically it is a non-calcified nodule of a specific size. Thus, in the National Lung Screening Trial the cutoff was at 4 mm, while in I-ELCAP it was at 5 mm for non-calcified nodules. Given a positive result, the confusion now occurs in terms of whether the nodule actually turns out to be a cancer or not. Some prefer to call these cases “false positive” even though they are truly nodules and positive in the sense that they meet the definition of positive based on the CT criteria. Others merely refer to the rate at which positive results occur considering them all positive regardless of their final disposition with the view that imaging does not determine malignancy. Regardless of the linguistics and their potential for causing some confusion, the main concern is to limit the excess amount of work up on those cases which are not cancer. This can be accomplished primarily in two ways. First, to be certain that the population being screened is at high risk for cancer, and secondly, to identify those criteria most associated with cancer and use that in the definition of a positive result. By far, the most dominant of those criteria is size defined either volumetrically or by diameter. An important consideration when defining size cutoffs for positive results, is that the frequency of nodules decreases with increasing size, and the frequency of cancer increases with increasing size. Also, with increasing size of the cancer, the chance for cure decreases. The extent to which all this occurs is not fully known and has many additional considerations. As a start however, and especially in the era of increased scanner resolution, the frequency of positive results would approach 100% if the size criteria is made small enough and the overwhelming majority would be benign. One approach to determining an optimal size criteria is to perform a sensitivity analysis on a screening population balancing the positive rate against what might be considered an acceptable “miss” rate. Using the I-ELCAP database, the frequency of positive results in the baseline round using the 5 mm size cutoff for positive result (any parenchymal, solid or part-solid, noncalcified nodule ≥5.0 mm) was 16% (3396/21 136). When alternative threshold values of 6.0, 7.0, 8.0 and 9.0 mm were used, the frequencies of positive results were 10.2% (95% CI, 9.8% to 10.6%), 7.1% (CI, 6.7% to 7.4%), 5.1% (CI, 4.8% to 5.4%), and 4.0% (CI, 3.7% to 4.2%), respectively. Use of these alternative definitions would have reduced the work-up by 36%, 56%, 68%, and 75%, respectively. Concomitantly, lung cancer diagnostics would have been delayed by at most 9 months for 0%, 5.0% (CI, 1.1% to 9.0%), 5.9% (CI, 1.7 to 10.1%), and 6.7% (CI, 2.2% to 11.2%) of the cases of cancer, respectively. This type of analysis was also performed on the NLST data which using their 4 mm size cutoff had reported a 26.6% positive rate on baseline. The frequency of positive results using the definition of a positive result of any parenchymal, solid or part-solid, noncalcified nodule of 5.0 mm or larger was 15.8%. Using alternative thresholds of 6.0, 7.0, 8.0, and 9.0 mm, the frequencies of positive results were 10.5% (2700 of 25 813, 7.2% , 5.3% , and 4.1% , respectively, and the corresponding proportional reduction in additional CT scans would have been 33.8% , 54.7% , 66.6% , and 73.8% , respectively. Concomitantly, the proportion of lung cancer diagnoses determined within the first 12 months would be delayed up to 9 months for 0.9% (two of 232), 2.6% (six of 232), 6.0% (14 of 232), and 9.9% (23 of 232) of the patients, respectively. The use of the 6 mm size threshold has now gained widespread acceptance in the context of screening having been endorsed by the NCCN, Lung-Rads and I-ELCAP. Nevertheless, it must still be recognized that the tradeoff is the delay in diagnosis of some small cancers for an additional nine months when the next annual screen would occur. While these cancers are unlikely to substantially change in size, the potential for progression is still present and this is the main consideration in balancing against the decrease in positive rate. While size does remain the dominant feature in defining a positive result in this high risk population, there are other approaches that consider additional features of the nodules that also have prognostic significance and may be useful in defining positive results.

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      MS24.02 - Computer Assisted Lung Cancer Screening: Automated CT Image Analysis (ID 1955)

      14:40 - 15:00  |  Author(s): A. Reeves, Y. Xie

      • Abstract
      • Presentation

      Abstract:
      With the advent of lung cancer screening (LCS) with low-dose chest CT images, the attention for computer aided tools advances from proof of concept and validation studies to clinical utility. Computer aided image-based diagnosis tools (CAD) for LCS on the initial CT have a primary objective of improved decision making for follow up actions. There are four roles for CAD tools in this context: nodule detection, nodule characterization, nodule growth-rate measurement for malignancy status, and companion diagnostics. The special low-dose CT scan acquired as the primary test in LCS is of lower quality than a traditional clinical CT scan and, consequently, presents a higher challenge to computer analysis methods. Computer aided nodule detection systems address the critical screening task of identifying pulmonary nodules in low-dose CT images. These systems typically identify the location of nodule candidates in the CT images. In general, they detect small sphere like high intensity image regions that correspond to the most common and important finding in LCS. Their performance is related to size and most evaluations are focused on nodules of 4-5 mm or larger. For smaller nodules the false positive rate is much higher. The first of such systems received FDA approval in 2004. There has been significant technology improvement since then with sensitivities in research systems higher than 90% reported in 2007 [1]. In 2012 Zhao et al [2] reported on a study using commercial software on 400 randomly selected cases from the NELSON study. They found that the CAD system could obtain 96.7% sensitivity on nodules greater than 50 mm[3] (4.6 mm) with only 1.9 false positives per scan. In contrast, the double reading achieved 78.1% sensitivity. While the benefit of using computer detection for LCS has been clearly demonstrated and good commercial products are available, there has been little adoption of these methods in recent LCS studies. The second area in which the computer may by useful is in analyzing the images of pulmonary nodule candidates especially with respect to the critical issue of malignant or benign. The classical approach here is to generate some diagnostic features from the appearance of the nodule images and to perform classification from these to determine malignancy. A number of research studies have shown encouraging results; however, these studies have either used non-screening nodules and images, which have a vastly larger size and higher quality or did not separate out the contribution of nodule size, which is highly predictive of malignancy in LCS populations, from the other image features. A recent study [3] has shown that after compensating for size, for LCS CT images, the other image features provide only a moderate amount of additional information. This information is insufficient for a diagnosis by itself but may be used to refine follow up decisions. The measurement of nodule growth rate from two or more CT scans has been shown to be highly predictive of nodule malignancy status [4]. Since at least a second scan is required this method should be considered as a follow up procedure among other clinical follow up methods. The main barrier to clinical implementation of this method is that it requires the computing of the difference of the two CT scans, which is highly dependent on the geometric image quality of each scan. Unfortunately, there exists no agency or process by which this quality is monitored or measured on current scanners and without any scanner calibration imprecise results may occur. Correct use of this method requires careful attention to details. CT scans acquired for LCS also image other critical organs that are at risk for the screening population. Companion diagnostics refers to computer analysis for conditions other than lung cancer from the periodic LCS CT images. Conceptually, this is similar to a blood test where additional conditions may be evaluated from a single patient interaction. Therefore, the automatic risk factor assessment of these additional regions provides additional benefit without requiring additional imaging for the LCS population. Work in this area is still at an early stage. Research targets for automated evaluation reported in the literature include: lung (emphysema and COPD), cardiac (coronary artery calcium, aorta profile and calcium), breast (density assessment), and bone (vertebral body density evaluation). Computer aided methods will inevitably make major contributions to increasing the efficiency and benefit of LCS as they transition from research prototypes to clinical practice. More sophisticated computer algorithms and modern machine learning techniques will greatly improve CAD performance; however, such methods require very large training image datasets. Research studies to date typically involve 100 images examples or less; future algorithm development can greatly benefit by the millions of images that will be acquired with LCS practice. References [1] Enquobahrie A A, Reeves A P, Yankelevitz D F and Henschke C I, “Automated Detection of Small Pulmonary Nodules in Whole Lung CT Scans”, Acad Radiol, 14(5): 579-593, 2007. [2] Zhao Y, de Bock G H, Vliegenthart R, van Klaveren R J, Wang Y, Bogoni L, de Jong P A, Mali W P, van Ooijen P M A and Oudkerk M, “Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume”, Eur Radiol, 22(10): 2076-2084, 2012. [3] Reeves A P, Xie Y and Jirapatnakul A, “Automated pulmonary nodule CT image characterization in lung cancer screening”, IJCARS, doi: 10.1007/s11548-015-1245-7, 2015. [4] Reeves A P, “Measurement of Change in Size of Lung Nodules”. In Li Q, Nishikawa R M (ed) Computer-Aided Detection and Diagnosis in Medical Imaging, Taylor & Francis, Chapter 11, 2015.

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      MS24.03 - Role of PET Scan in Workup of Nodules (ID 1956)

      15:00 - 15:20  |  Author(s): U. Pastorino

      • Abstract
      • Presentation

      Abstract:
      Effective screening programs should detect all cancers and reduce as much as possible the probability of false-positive results, not representing malignant disease. In lung cancer screening, false-positive low-dose computed tomography (LDCT) results are even more crucial than in other fields, because of the magnitude of risks and costs related to invasive diagnostic examinations, and the need of potentially harmful surgical procedures. Long-term follow-up of nodules ≤ 5 mm at baseline CT has proven that these nodules don’t require additional workup, but for non-calcified nodules between 5 and 10 mm, surveillance of growth is mandatory to identify the relatively few malignant lesions. With the NLST diagnostic algorithm, based on diameter measurement, 24% of subjects had a positive LDCT but 96% of them proved to be false positives, with a positive predictive value (PPV) of only 3.6% at baseline, 2.4 first repeat and 5.2% at second repeat [1,2]. On the contrary, the diagnostic algorithm of Nelson trial, based on the automated assessment of 3D volumetry and doubling time, obtained a 36% PPV and a 99.9% negative predictive value (NPV) [3]. However, in the Nelson trial, where positron emission tomography (PET) was not included in the diagnostic algorithm, the frequency of invasive procedures for benign disease proved to be quite high (27%), and similar to the one observed in NLST trial (24%) [4]. Large meta-analyses have demonstrated the clinical value of PET in the differential diagnosis of undetermined pulmonary nodules detected by spiral CT, with a sensitivity rate of 96-97%, a specificity of 78-82% [5], and accuracy rate reaching 92% with the CT/PET fusion machine [6]. In 2000, our pilot study in Milan was the first screening protocol to include selective use of PET in the diagnostic algorhitm, thus showing that PET may be helpful in the management of CT detected nodules ≥ 7 mm. In the first five years of screening, PET was applied to only 1.4% of spiral CTs, with an overall sensitivity rate of 94%, specificity of 82%, and an accuracy rate of 88% [7,8]. In the Milan pilot trial, the cumulative frequency of surgical procedures for benign disease at 5 years was 15%. The MILD randomized trial has obtained similar results, in terms of frequency and diagnostic accuracy. From 2005 to 2015, a total of 113 PET were applied to 2376 individuals and 12,314 LDCTs, representing 4.8% of all screened individuals in 10 years, and 0.93% of all LDCTs. Excluding lung cancer cases, where PET would have been applied later for staging purposes, the true excess of PET examinations for screening purposes only reached a total 33 exams (1.4% of subjects, 0.3% of LDCTs). The sensitivity rate was 85%, specificity 80%, accuracy 83%, PPV 89% and NPV 74%. Of interest, only 3 patients underwent pulmonary resection for benign disease, out of 66 surgical procedures (5%) performed in the MILD trial. Such a low benign resection rate, is not only due to selective use of PET, but also to the active surveillance programme applied to non-solid lesions in the MILD trial. Beyond differential diagnosis, PET may play a role in prediction of outcome, and identification of indolent lung cancer. We have demonstrated in a previous paper, based on 34 lung cancer patients from the first pilot trial, that PET-SUV value can accurately predict long term survival and identify individuals with 100% 5-year survival [9]. In the MILD trial we have confirmed the value of metabolic profile as a predictor of outcome. The following figure illustrates the 5-year survival of 95 patients, from pilot and MILD trials. Figure 1 The possibility to combine metabolic profile with other biomarkers, such as circulating miRNAs [10], to identify indolent disease will require future investigations, to improve performance and reduce over-diagnosis of LDCT screening. 1 Aberle DR, Adams AM, Berg CD, et al. The National Lung Screening Trial Research Team (2011). Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 365:395-409. 2 Aberle DR, DeMello S, Berg CD, et al. (2013) Results of the Two Incidence Screenings in the National Lung Screening Trial. N Engl J Med 369:920-31. 3 van Klaveren RJ, Oudkerk M, Prokop M, et al. (2009) Management of lung nodules detected by volume CT scanning. N Engl J Med 361:2221-9. 4 Kramer BS, Berg CD, Aberle DR, Prorok PC. Lung cancer screening with low-dose helical CT: results from the National Lung Screening Trial (NLST). J Med Screen. 2011;18:109-111. 5 M.K. Gould, C.C. Maclean, W.G. Kuschner, et a. l(2001). Owens, Accuracy of positron emission tomography for diagnosis of pulmonary nodules and mass lesions: a meta-analysis. JAMA 285: 914–924. 6 Kim SK, Allen-Auerbach M, Goldin J, et al. (2007) Accuracy of PET/CT in characterization of solitary pulmonary lesions. J Nucl Med 48:214–220. 7 Pastorino U (2010) Lung Cancer Screening. British Journal of Cancer 102: 1681–1686 8 Veronesi G, Bellomi M, Veronesi U, et al. (2007) Role of positron emission tomography scanning in the management of lung nodules detected at baseline computed tomography screening. Ann Thorac Surg 84:959-66 9 Pastorino U, Landoni C, Marchianò A, et al. (2009) Fluorodeoxyglucose (FDG) uptake measured by positron emission tomography (PET) and standardised uptake value (SUV) predicts long-term survival of CT screening-detected lung cancer in heavy smokers. J Thor Oncol 11:1352-6 10. Sozzi G, Boeri M, Rossi M, et al: Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: A correlative MILD trial study. J Clin Oncol 32:768-773, 2014



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      MS24.04 - Biomarkers in Selection for CT Screening/Management of Nodules (ID 1957)

      15:20 - 15:40  |  Author(s): H.I. Pass

      • Abstract
      • Presentation

      Abstract:
      The complexity of biomarker discovery is amplified by the multitude of platforms on which the biomarker is discovered (mutational sequencing, fluorescence in situ hybridization (FISH), single-nucleotide polymorphisms (SNPs), copy-number variation (CNV) of chromosomes, immunohistochemistry, epigenetics including methylation studies, or microRNA ), and by the material used (tissue, plasma, serum, urine, breath, sputum, effusion). The aim is to define these biomarkers in a way whereby their use is contingent on maximal accuracy, which depends on the ability of biomarker researchers to not only put forth markers with the greatest sensitivity and specificity, but also to be able to validate these biomarkers in a methodologic algorithm that will satisfy regulatory bodies including the Food and Drug Administration (FDA) in the United States as well as other agencies abroad. This lecture will concentrate on novel biomarkers for lung cancer being investigated by the Lung Group and industrial members of the Early Detection Research Network. These biomarkers include autoantibodies, MRM proteomics, micro and lncRNAs, SomaMers, and airway transcriptomics.

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Author of

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    MINI 19 - Surgical Topics in Localized NSCLC (ID 138)

    • Event: WCLC 2015
    • Type: Mini Oral
    • Track: Treatment of Localized Disease - NSCLC
    • Presentations: 1
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      MINI19.15 - Discussant for MINI19.11, MINI19.12, MINI19.13, MINI19.14 (ID 3475)

      18:05 - 18:15  |  Author(s): J.H. Pedersen

      • Abstract
      • Presentation

      Abstract not provided

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    MS 19 - Global Nursing Issues in Lung Cancer (ID 37)

    • Event: WCLC 2015
    • Type: Mini Symposium
    • Track: Nursing and Allied Professionals
    • Presentations: 1
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      MS19.04 - The Importance of Pre- and Early Postoperative Rehabilitation in NSCLC Patient (Design and Rationale for the PROLUCA Study) (ID 1934)

      14:59 - 15:12  |  Author(s): J.H. Pedersen

      • Abstract
      • Presentation
      • Slides

      Abstract:
      Background Exercise has been introduced to improve physical capacity (VO~2peak~ and 1RM) and quality of life and to reduce symptoms and side-effects of treatment in patients with cancer, mostly investigated in patients with breast cancer (1,2). Against this background a feasibility study was developed to investigate the safety and feasibility of a preoperative and postoperative exercise program in patients undergoing surgery for lung cancer (3). The study concluded that the preoperative exercise program was not feasible. However, initiation of exercise two weeks postoperatively for patients with NSCLC was safe and feasible. A randomized clinical trial (PROLUCA) was therefore developed to investigate the efficacy of a postoperative exercise intervention in a non-hospital setting. Objectives The objective of the presented study was to explore operable lung cancer patient experiences with the postoperative exercise intervention from a longitudinal perspective according to patient motivation and patient perceived benefits and barriers of exercise. Methods This qualitative component formed part of the randomized control trial (PROLUCA) comparing the efficacy of early initiated postoperative exercise (initiated two weeks after surgery) with the effect of exercise initiated 14 weeks after surgery (usual care). NSCLC patients referred for surgery at the Department of Thoracic Surgery, Copenhagen University Hospital, were recruited for the exercise intervention. More details on the RCT study can be found in the published protocol by Sommer et al. (3). Nineteen patients enrolled in the exercise intervention two weeks post-surgery participated in qualitative interviews at three time points; the day after surgery, 7 weeks post-surgery and 4 months post-surgery. An analysis based on Ricoeur’s theory of interpretation was conducted in a phenomenological hermeneutical approach (4). Results The patient sample’s mean age was 63 years (range 48–75). Patients underwent video-assisted thoracoscopic surgery (VATS) with intent to cure. The majority (79 %) had comorbidities, including chronic obstructive pulmonary disease (26 %), cardiovascular disease (26 %) and hypertension (26%). Pre-illness physical activity levels of the patients showed that 47 % had not met the national recommendation for physical activity. Patients started exercising 15 days following surgery (median) (range 14–41 days). Delay was due to postoperative complications (pain, pneumothorax, pneumonia, atrial fibrillation, general discomfort). Eight participants dropped out of the intervention. Reasons for non-adherence included chemotherapy side effects (nausea, fatigue) (n=3), other reasons (n=1), fractured arm (n=1), work (n=1), terminally ill husband (n=1) and death (n=1). The mean attendance rate for the 11 participants who completed the intervention was 82 % (range 58–100 %). No patients experienced severe adverse events (e.g. heart- or respiration stop) during or following exercise. The interview findings are organized into three themes reflecting the timespan related to the patients’ treatment trajectory: 1) Pre-intervention motivation for participation; 2) Benefits and barriers of the intervention; 3) Overall experiences with the intervention. Motivation for participation included patients’ expectations of physical benefits and the security of having professionals present. Patients experienced physical and emotional benefits and affirmed their social identity, including improved breathing and increased well-being and energy level. Group training had social benefits and the patients experienced a sense of belonging. Exercising with others in a similar circumstance was meaningful to the patients and created a sense of community. Barriers were primarily related to side-effects of chemotherapy. The intervention put the patients on track to a healthier lifestyle regarding physical activity and smoking, and regaining vitality and energy increased the participants’ faith in the future. The patients were satisfied with the exercise intensity level, contents and variation and felt that after 12 weeks of two 60-min weekly sessions they had regained a good amount of strength and energy. Two of the eight patients who were undertaking adjuvant chemotherapy started training on completion of their treatment, while three other patients exercised while being treated. Discussion To our knowledge, this is the first study that addresses operable NSCLC patient perspectives on participating in an exercise intervention during the immediate post-surgical period and subsequent chemotherapy. The sample of the 19 patients included in the interview study appears to be a select group of operable lung cancer patients. So far it has not been possible to compare the interview sample with the larger sample of the trial and therefore not possible to discuss representativeness of the selected sample. However, when comparing the interview study sample with other operable lung cancer patients, it appears that the study patients are a little younger, better educated, have early stage disease, good performance status and are used to physical activity. Although this sample is comparable with other lung cancer exercise samples (5), this suggests social inequality related to the intervention’s design. This might also explain why the operable lung cancer patients consented to participate in the intervention and changed their behaviour—a finding that is not comparable with the general lung cancer population reported to have particularly low levels of physical activity during the post-treatment period (6) and with patients engaged in light physical activity (7). Despite the fact that the studied sample might be a select group, the diagnosis appears to represent “a teachable moment” as discussed by Demark-Wahnefried and colleagues (8), and the intervention seem to assist the patients by increasing their physical function and energy, their well-being and improve their social capital. Eleven of the patients completed the intervention with a mean attendance rate of 82 %, which is comparable with other exercise intervention studies (9). This result underscores the patients’ desire and ability to complete the intervention. Reasons for dropping out of the intervention were due to external conditions unrelated to the intervention or due to chemotherapy side effects. Conclusion This study contributes to the literature by taking into account patient perspectives of exercise interventions. The supervised intervention was undertaken safely by operable lung cancer patients initiated 2 weeks after surgery. This select group of lung cancer patients experienced physical and emotional benefits and affirmed their social identity. This qualitative study indicates that exercise is beneficial for lung cancer patients in the postsurgical trajectory and especially for those who were physically active and motivated pre-illness. References 1. Adamsen L, Quist M,Andersen C, Moller T, Herrstedt J, Kronborg D et al (2009) Effect of a multimodal high intensity exercise intervention in cancer patients undergoing chemotherapy: randomised controlled trial. BMJ 339:b3410 2. Mishra SI, Scherer RW, Snyder C, Geigle PM, Berlanstein DR, Topaloglu O (2012) Exercise interventions on health-related quality of life for people with cancer during active treatment. Clin Otolaryngol 37(5):390–392 3. Sommer MS, Trier K, Vibe-Petersen J, Missel M, Christensen M, Larsen KR et al (2014) Perioperative rehabilitation in operation for lung cancer (PROLUCA)—rationale and design. BMC Cancer 14:404-2407–14-404 4. Ricoeur P (1976) Interpretation Theory. Discource and the surplus of meaning. TCU Press, Texas 5. Jones LW, Eves ND, Spasojevic I,Wang F, Il'yasova D (2011) Effects of aerobic training on oxidative status in postsurgical non-small cell lung cancer patients: a pilot study. Lung Cancer 72(1):45–51 6. Coups EJ, Park BJ, Feinstein MB, Steingart RM, Egleston BL, Wilson DJ et al (2009) Physical activity among lung cancer survivors: changes across the cancer trajectory and associations with quality of life. Cancer Epidemiol Biomarkers Prev 18(2):664–672 7. Lin YY,Wu YC, RauKM, Lin CC (2013) Effects of physical activity on the quality of life in Taiwanese lung cancer patients receiving active treatment or off treatment. Cancer Nurs 36(4):E35–E41 8. Demark-Wahnefried W, Aziz NM, Rowland JH, Pinto BM (2005) Riding the crest of the teachable moment: promoting long-term health after the diagnosis of cancer. J Clin Oncol 23(24):5814–5830 9. Kjaer TK, Johansen C, Ibfelt E, Christensen J, Rottmann N, Hoybye MT et al (2011) Impact of symptom burden on health related quality of life of cancer survivors in a Danish cancer rehabilitation program: a longitudinal study. Acta Oncol 50(2):223–232

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    ORAL 09 - CT Screening - New Data and Risk Assessment (ID 95)

    • Event: WCLC 2015
    • Type: Oral Session
    • Track: Screening and Early Detection
    • Presentations: 1
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      ORAL09.03 - The Danish Lung Cancer Screening Trial: Results 5 Years after Last CT Screening (ID 2384)

      11:07 - 11:18  |  Author(s): J.H. Pedersen

      • Abstract
      • Presentation
      • Slides

      Background:
      The Danish Lung Cancer Screening Trial (DLCST) is a European randomized controlled trial comparing annual CT screening with no screening. Inclusion ran from 2004 to 2006, and participants have now been followed for 5 years since last CT screening (approximately 10 years since randomization). The American NLST showed 20% decrease in lung cancer mortality in the screening group, and DLCST is the first European trial to present comparable results regarding effect of screening on mortality, causes of death, lung cancer findings and risk stratification after sufficient follow up.

      Methods:
      4,104 participants aged 50-70 at time of inclusion and a minimum of 20 pack-years of smoking history were randomized to five annual low-dose CT scans or clinical follow up without CT scanning; thus, participants were younger and had fewer pack-years than participants from NLST. Screening was concluded in 2010. Follow up information regarding date and cause of death as well as lung cancer diagnosis, stage and histology was obtained from national registries, latest follow up date was April 7, 2015. . The effects of age, amount of smoking and COPD on lung cancer mortality in the two randomized groups were explored to evaluate possible effects of risk stratification and selection of high-risk individuals on effect of screening.

      Results:
      More cancers (100 vs. 53, p<0.001) were found in the screening group, in particular adenocarcinomas (58 vs. 18, p<0.001). Significantly more low-staged cancers (stage I+II: 54 vs. 10, p<0.001) and stage IIIa cancers (15 vs. 3, p=0.009) were found in the screening group. However, stage IV cancers were more frequent in the control group (23 vs. 32, p=0.278), and this was statistically significant for the highest-stage cancers (T4N3M1: 8 vs. 21, p=0.025). No differences in lung cancer mortality or all-cause mortality were observed between the two groups (Log Rank tests: p=0.898 and p=0.885, respectively). However, sub-group analyses including participants with higher age, presence of COPD, and more than 35 pack-years of smoking history showed significantly increased risk of death from lung cancer; the highest-risk group (with COPD and >35 pack-years) showed a 20% reduction in lung cancer mortality when screened. Though this result is not statistically significant due to small numbers, it does show compliance with the results from NLST.

      Conclusion:
      Although no statistically significant effects of 5 annual CT screening rounds on lung cancer mortality were observed in this small study, results indicate that focus on selection of high-risk individuals may be essential for the effect of CT lung cancer screening. We suggest that balancing benefits with harms—such as false positive findings and overdiagnosis— should bring focus to high-risk profiling of screening participants. Thus, the effects of age, amount of smoking, and COPD on the occurrence and mortality of lung cancer in the two randomization groups seems to indicate that limiting lung cancer screening to a higher-risk group improves the outcome of screening.

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