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D. Midthun
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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
- Moderators:D. Midthun, J.H. Pedersen
- Coordinates: 9/09/2015, 14:15 - 15:45, Four Seasons Ballroom F3+F4
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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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- 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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MTE 26 - Multidisciplinary Approach to a Comprehensive CT Screening Program (Ticketed Session) (ID 78)
- Event: WCLC 2015
- Type: Meet the Expert (Ticketed Session)
- Track: Community Practice
- Presentations: 1
- Moderators:
- Coordinates: 9/09/2015, 07:00 - 08:00, 109
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MTE26.01 - Multidisciplinary Approach to a Comprehensive CT Screening Program (ID 2013)
07:00 - 08:00 | Author(s): D. Midthun
- Abstract
- Presentation
Abstract:
The National Lung Screening Trial results provided convincing evidence to the US Preventive services Task Force (USPSTF) to give a B recommendation (same as mammography) to screening high-risk individuals with low-dose CT (LDCT) in 2013.[1,2] In response in 2015 the Center for Medicare and Medicaid services (CMS) determined payment for screening those age 55 to 77 with the same smoking requirements.[3] So how is the medical community supposed to implement screening? At this point it appears easier to come up with the questions than the answers and it is unclear if there will be additional large randomized studies to guide the process. Programs will have similar elements with yet with features reflective of local needs and resources. In the US much of this process is being dictated by CMS and the American College of Radiology (ACR) which will maintain the registry through which reimbursement by CMS has been approved. As of July, this registry has not been set up and reimbursement by CMS is promised but not being done. As the NLST is the only randomized control trial to show benefit, whether international adoption occurs remains to be seen. Policy statements are available to help identify the key components and implementation strategies for a CT screening program.[4,5] A multidisciplinary committee that meets regularly consisting of pulmonology, radiology, primary care, thoracic surgery, interventional radiology, and medical and radiation oncology is important to facilitate LDCT screening, and evaluation and treatment of screening results. The inclusion of each of these disciplines helps to assure the patient has a complete complement of options regarding diagnosis and treatment and also limits the implementation of screening to systems with the needed expertise available. Having dedicated secretarial and administrative support is as important to a program’s success. Who to screen? The simplest answer is to screen those for whom benefit has been shown, namely those fitting the NLST criteria: age 55-74 with a 30 pk-year history of smoking and either current smokers or those who have quit within 15 years.[1] But it should be more complicated that as there are many patients at equivalent or higher risk who don’t fit the NLST criteria. There are several guidelines published and they all differ. USPSTF recommends ages 55 to 80 (and is so mandated within the Affordable Care Act), CMS is reimbursing for those age 55 to 77 (in medicare or medicaid), the National Comprehensive Care Network (NCCN) additionally recommends screening for those age > 50 with a 20 pack-year history and one additional risk factor such as COPD, family history of lung cancer, occupational exposure to carcinogens, and significant radon exposure.[6] Similarly the American Association of Thoracic Surgery recommends screening for those ages 55-79 within the NSLT smoking criteria as well as those > age 50 with a > 20 pk-year history and a cumulative risk of > 5% over 5 years.[7] At present the American Academy of Family Physicians recommends against LDCT screening.[8] Our program is recommending screening based on risk rather than reimbursement and, as a consequence, in additional to those who meet USPSTF criteria, recommends screening to those who have equivalent or higher risk using the PLCO~2012~ model.[9] Exclusion criteria should be similar between programs and based on the NLST.[1] Shared Decision Making. The USPSTF recommendation includes a shared decision making process (not recommended for breast cancer screening); this is also mandated by CMS as an identifiable visit with specific components: eligibility, absence of signs or symptoms of lung cancer, benefits and harms of screening, follow-up diagnostic testing, over-diagnosis, false positive rate, radiation exposure; importance of adherence to annual screening, impact of comorbidities, willingness to undergo treatment; and the importance cigarette smoking abstinence or cessation.[3] We mandate tobacco cessation counseling for current smokers prior to screening in an attempt to make clear that cessation is more lifesaving than screening. In the NLST there were 16 deaths within 60 days of an invasive procedure and only 10 of those had cancer; patients need to know process of screening can be fatal.[1] Radiology, results and Data collection. The ACR and Society of Thoracic Radiology have identified specifications for a LDCT and the registry requires that those technical parameters be met.[10] A structured reporting system is desired yet unfortunately the ACR registry requires that LungRADS be used which is not consistent with evidence based guidelines, is ambiguous, and is not aimed at patient communication. Nodule Evaluation. An optimal nodule evaluation algorhythm is yet to be determined, and since patient preference is to be weighed, no one fit will size all. Doing the CT scan is the easy part; follow-up is imperative - a dedicated registry is mandatory in this regard. Many raise concerns about the false positives of CT screening; within the NLST (positive defined as > 4mm) false positives were 96%. The 4 mm nodule has a likelihood of lung cancer of less than 1%. Should we call it a positive with that probability? The reality is that the vast majority of nodules found by CT screening need no additional evaluation other than CT follow-up – most with the next annual scan. We are recommending PET or biopsy (depending on the circumstances) only for nodules 1cm or greater and that eliminates immediate evaluation for 95% of the participants. But we don’t call a 6 mm nodule negative; it exists and needs follow-up – a key is to provide accurate information to the patient and their provider as to the likelihood of malignancy. Our program is responsible for the evaluation and followup of findings in a desire to favorably the tip the balance of benefit versus harms. People don’t die from false positives, but they can die from their evaluation. Nodule evaluation should be done by those who do it every day; we don’t feel this is appropriate for the primary provider and perhaps why the AAFP rejects LDCT screening. A multidisciplinary tumor/nodule board can help share the decision making, cross fertilize, and facilitate care for those who need treatment. 1. Aberle DR, Adams AM, Berg CD, et al. National Lung Screening Trial Research Team. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011;365: 395–409. 2. Moyer VA. Screening for lung cancer: U.S. Preventive services task force recommendation statement. Ann Intern Med 2014;160:330-338. 3. Centers for Medicare & Medicaid Services. Decision memo for screening for lung cancer with low dose computed tomography (ldct) (cag-00439n) 2015. Available from: http://www.cms.gov/medicare-coverage-database/details/nca-decision-memo.aspx?NCAId=274&NcaName=Screening+for+Lung+Cancer+with+Low+Dose+Computed+Tomography+(LDCT)&MEDCACId=68&IsPopup=y&bc=AAAAAAAAAgAAAA%3d%3d&. 4. Mazzone P, Powell CA, Arenberg D et al. Components necessary for high-quality lung cancer screening: American College of Chest Physicians and American Thoracic Society Policy Statement. Chest. 2015;147:295-303. 5. Wiener RS, Gould MK, Arenberg D, et al. Official American Thoracic Society / American College of Chest Physicians Policy Statement: Implementation of Lung Cancer Screening Programs with Low-Dose Computed Tomography in Clinical Practice. Am J Respir Crit Care Med. 2015; in press. 6. National Comprehensive Cancer Network. Nccn clinical practice guidelines in oncology (nccn guidelines) - lung cancer screening, version 1.2015. 2014. Available from: http://www.nccn.org/professionals/physician_gls/pdf/lung_screening.pdf. 7. Jaklitsch MT, Jacobson FL, Austin JH, et al. The american association for thoracic surgery guidelines for lung cancer screening using low-dose computed tomography scans for lung cancer survivors and other high-risk groups. J Thorac Cardiovasc Surg 2012;144:33-38. 8. American Academy of Family Physicians Policy Statement available from: http://www.aafp.org/patient-care/clinical-recommendations/all/lung-cancer.html 9. Kazerooni EA, Austin JH, Black WC, et al. Acr-str practice parameter for the performance and reporting of lung cancer screening thoracic computed tomography (CT): 2014 (resolution 4). J Thorac Imaging 2014;29:310-316. 10. Tammemagi MC, Katki HA, Hocking WG, et al. Selection criteria for lung-cancer screening. N Engl J Med 2013;368:728-736.
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ORAL 23 - Prevention and Cancer Risk (ID 121)
- Event: WCLC 2015
- Type: Oral Session
- Track: Prevention and Tobacco Control
- Presentations: 1
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ORAL23.01 - A Randomized Phase IIb Trial of Myo-Inositol in Smokers with Bronchial Dysplasia (ID 856)
10:45 - 10:56 | Author(s): D. Midthun
- Abstract
- Presentation
Background:
Previous preclinical studies and a phase I clinical trial suggested myo-inositol may be a safe and effective lung cancer chemopreventive agent. We conducted a randomized, double blind, placebo-controlled, phase IIb study to determine the chemopreventive effects of myo-inositol in smokers with bronchial dysplasia.
Methods:
Smokers with ≥1 site of dysplasia identified by autofluorescence bronchoscopy-directed biopsy were randomly assigned to receive oral placebo or myo-inositol, 9 g once/day for two weeks, and then twice/day for 6 months. The primary endpoint was change in dysplasia rate after six months of intervention on a per participant basis. Other trial endpoints reported herein include Ki-67 labeling index and pro-inflammatory, oxidant/anti-oxidant biomarker levels in blood and bronchoalveolar lavage fluid (BAL).
Results:
Seventy four (n=38 myo-inositol, n=36 placebo) participants with a baseline and 6-month bronchoscopy were included in all efficacy analyses. The complete response and the progressive disease rates were 26.3% versus 13.9% and 47.4% versus 33.3%, respectively, in the myo-inositol and placebo arms (p=0.76). The mean percent change in Ki67 labeling index in bronchial biopsies with dysplasia was -22.8% and -6.2%, respectively, in the myo-inositol and placebo arms (p=0.34). Compared with placebo, myo-inositol intervention significantly reduced IL-6 levels in BAL over 6 months (p=0.03) and had borderline significant effects on BAL myeloperoxidase (p= 0.06) level.
Conclusion:
The heterogeneous response to myo-inositol suggests a targeted therapy approach based on molecular alterations is needed in future clinical trials to determine the efficacy of myo-inositol as a chemopreventive agent.
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