Virtual Library

Start Your Search

Claudia I Henschke



Author of

  • +

    P2.13 - Radiology/Staging/Screening (ID 714)

    • Event: WCLC 2017
    • Type: Poster Session with Presenters Present
    • Track: Radiology/Staging/Screening
    • Presentations: 1
    • +

      P2.13-026 - Determining the Effect of Screening on Lung Cancer Mortality (ID 9553)

      09:30 - 09:30  |  Presenting Author(s): Claudia I Henschke

      • Abstract

      Background:
      The current lung cancer screening recommendation of the United States Preventive Services Task Force (USPSTF) is to perform annual low-dose computed tomography (CT) scans for high risk current smokers (at least 30 pack-years), or quitters in the past 15 years, age 55-80 years. Our study aims to assess if early detection of lung cancer by screening decreases the lung cancer mortality burden and, if so, how drastically for those considered at highest lung cancer risk.

      Method:
      Lung cancer screening prevalence was calculated from the 2010 to 2015 National Health Interview Surveys (NHIS). Probability of screening was derived from logistic regression models using race, age, gender, smoking and health insurance status as predictors. Beta values for these covariates were then used to estimate the probability of screening in the 1999-2004 National Health and Nutrition Examination (NHANES) cohort, for which lung cancer mortality information was available through linkage with the National Death Index. Using the predictor values generated in the NHIS dataset, probability of screening was estimated for the at risk NHANES participants, to make inferences about the effects of screening on lung cancer mortality.

      Result:
      Of the 60829 NHIS study participants, 2296 met the definition for being at high for lung cancer. The overall screening prevalence for this at-risk population was 10.4%; 7.7% had chest radiography while 5.7% had CT scans. Screening occurred more frequently in former smokers (p=0.0474), people who had health insurance coverage (p= 0.0017), and those older than 68 years (p = 0.0439). In the NHANES cohort, out of 31126 participants, 668 met the USPSTF recommendation for screening and 25 of them died of lung cancer. Lung cancer mortality was significantly higher in the high-risk group than in the low-risk group (HR~adj~ 8.59, 95% CI: 5.12-14.41). Based on the screening predictors obtained from NHIS data, 347 (51.95%) of the 688 high risk individuals would undergo a screening; 16 of them (4.6%) have died of lung cancer. If screening had occurred, overall lung cancer mortality would have potentially been reduced by 64%, provided that individuals had screening-detected early stage operable tumors.

      Conclusion:
      Increasing CT screening among those at high-risk for lung cancer should significantly reduce deaths from lung cancer in this population. Screening needs to be combined with continued smoking cessation efforts.

  • +

    P2.16 - Surgery (ID 717)

    • Event: WCLC 2017
    • Type: Poster Session with Presenters Present
    • Track: Surgery
    • Presentations: 5
    • Now Available
    • +

      P2.16-013 - Peripheral or Central Lung Nodules: How do Thoracic Surgeons Define it? (Now Available) (ID 9534)

      09:30 - 09:30  |  Author(s): Claudia I Henschke

      • Abstract
      • Slides

      Background:
      “Peripheral” is ubiquitously used in thoracic surgery literature, but definitions differ. Our purpose was to ascertain opinions of thoracic surgeons on CT images and assess the frequency of peripheral nodules according to their definitions.

      Method:
      We developed a survey and obtained an IRB exemption. Surgeons were asked to choose one of methods A-D to define the peripheral pleura: A=costal pleura, B=costal and mediastinal pleura (diaphragmatic pleura also on coronal and sagittal views), C=costal and fissural pleura, D=any pleural surfaces on: Question#1) axial images, Question#2) coronal images, Question#3) sagittal images. Question#4 asked whether the peripheral lung was: 1, 2, or 3 cm, outer 1/3, outer 1/2 or outer 2/3. Question#5 asked whether the measurement from the nodule to the pleura started at the inner edge, center, or outer edge of the nodule. By applying the possible choices to a database of 76 patients with documented lung cancer we determined the frequency of peripheral cancers for each combination.

      Result:
      Ten thoracic surgeons participated, all had different answers. The most frequent response to Question#1 was Method A (n=4), Question#2 Method A (n=5), and Question#3 Method B (n=4). The most frequent answer for Question#4 was the outer 1/3 of the lungs (n=6), and for Question#5, the outer border of the nodule, closest to the relevant pleura (n=5). The frequency of nodules classified as peripheral according to these answers ranged from 13% (10/78) to 91% (71/78).

      Conclusion:
      There was no consensus. Standardization and rationale for this would be highly useful.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

    • +

      P2.16-014 - Deconstructing Surgical Decision Making (Now Available) (ID 9543)

      09:30 - 09:30  |  Author(s): Claudia I Henschke

      • Abstract
      • Slides

      Background:
      With the increase in number of individuals undergoing CT screening, lung cancers are now being detected at an earlier stage. Curative treatment can thus be performed on these patients, resulting in better lung cancer survival. Effective surgical decision making depends upon the degree of knowledge and experience the treating surgeon has about the outcome of actions, ability to assess risk and its subsequent impact. Use of a gnostic expert system would increase cost-effectiveness and efficiency. Our objective is to garner experts’ tacit knowledge about surgical decision making in a form of probability function.

      Method:
      Nine surgeons with extensive experiences in thoracic surgery were presented with a set of hypothetical cases, specified by indicators for surgical treatment (lobectomy or limited resection). Their choice of surgery and probability of performing limited resection were recorded for each case. Probabilities were translated into a logistic probability function for limited resection by 1) taking logits of the probabilities: Y=log[P/(1-P)], then 2) applying a general linear model for the mean of Y, Ŷ=β~1~+ β~2~X~2~+ β~3~X~3~+ β~4~X~4~+ β~5~X~5~+ β~6~X~6~+ β~7~X~7~ + ε. Standardized coefficients were computed and ranked to determine the effect of each indicator on limited resection.

      Result:
      Across the 24 cases, the median probabilities of limited resection among experts ranged from 0.0% to 100.0%, their case-specific IQR had values from 5 to 90 (Q3-Q1) percentage points, and ranges had values from 10-100(max-min) percentage points. Considering the expert-specific median probabilities, five out of eight experts favored lobectomy (median probabilities of limited resection ≤12.5%). Two other experts had median probabilities of 42.5% and 49% while the remaining expert favored limited resection (median probability 65%). The effect of each indicator on preferring limited resection over lobectomy varied between surgeons. Overall, distance from relevant pleura and nodule size were important factors for considering limited resection.

      Conclusion:
      There was great inter-surgeons variability on surgical decision making. Garnering experts’tacit knowledge on surgical decision making will enhance efficiency of health care and potentially change surgical practice.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

    • +

      P2.16-022 - Initiative for Early Lung Cancer Research on Treatment: Pilot Implementation (Now Available) (ID 10165)

      09:30 - 09:30  |  Presenting Author(s): Claudia I Henschke

      • Abstract
      • Slides

      Background:
      We have initiated a new multi-center, international collaborative cohort study, the Initiative for Early Lung Cancer Research for Treatment (IELCART), which focuses on identifying optimal treatment for early stage lung cancer An issue under discussion is the extent of surgery (i.e., sublobar resection and no mediastinal lymph node resection) in order to decrease the length and morbidity of the surgical procedure, preserves pulmonary function, and increases the likelihood of resection of future new occurrences of lung cancers. The role of Stereotactic Body Radiation (SBRT), and for certain cases, Watchful Waiting (WW) also needs to be better delineated. Increasingly, the power of large prospective databases collected in the context of clinical care is being recognized as providing important information.

      Method:
      Based on an extensive literature review, scientific articles, and a series of focus sessions with patients and treating physicians, a common protocol has been developed. Relevant data forms were developed for both physicians and patients, both for pre- and post-surgery to account for potential confounders. These forms have been tested and entered into a web-based data collection system that also includes relevant imaging data. Initial enrollment focused on surgery.

      Result:
      Initial enrollment was limited to surgical clinics of 8 surgeons and a total of 174 patients (94 women, 80 men) agreed. Average age was 67.5 years and pack-years of smoking was 31.4. Patients stated that the internet was the most frequent source of information (35%), while family/friends, medical literature were used much less frequently (each <20%). Factors influencing the patient pre-treatment choice was that the physician thought it was best (93%) or what would provide the best outcome (87%); only 38% got a second opinion. The surgeon’s choice of procedure depended mainly on the location (75%), size of the nodule (64%), and the ability to have negative parenchymal margin (40%), with other considerations being much less likely (<26%). There was good agreement between patients’ and surgeons’ perceptions of the procedure, although the patients not fully prepared about the post-treatment consequences of surgery. Patients also thought that support groups were important in patients’ decisions on what was the best surgery.

      Conclusion:
      These results together with quality of life information and focus sessions suggest that more support in the post-operative phase of the treatment would be beneficial. Within the next 3 years, we anticipate to have statistically meaningful results to start to compare outcomes of alternative treatments.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

    • +

      P2.16-023 - Changes of the Pulmonary Artery After Resection of Stage I Lung Cancer (Now Available) (ID 10238)

      09:30 - 09:30  |  Author(s): Claudia I Henschke

      • Abstract
      • Slides

      Background:
      Radiologists focus on the anatomic changes in the lung itself when interpreting postoperative surveillance CT scans, but the anatomic and physiologic effects of lung resection on the other organs of the thorax, specifically the pulmonary artery (PA), have not been well studied. Potential variations in PA size over time have been recognized as predictors of post-surgical complications and the development of pulmonary hypertension.

      Method:
      The International Early Lung Cancer Action Program (I-ELCAP) database was queried for lung cancer patients who underwent lobectomy and had both preoperative and postoperative CT imaging. Case-specific details were previously recorded in the database as per I-ELCAP protocol. All surgeries were performed by general thoracic surgeons. All CT imaging for each patient was reviewed by a fellowship-trained chest radiologist. Figure 1



      Result:
      Among the 142 subjects who underwent lobectomy, the median follow-up time from the pre-surgical CT to the last reviewable CT was 53.2 months (IQR: 27.9-100.4 months). The average increase in the size of the main pulmonary artery (mPA) was 1.5 mm (19.9 mm to 21.4 mm, P < 0.0001). There was also a significant increase between the pre-surgical CT and the initial postoperative CT which was on average 12.6 months later from 19.9 mm to 20.7 mm (P = 0.0002). Considering patients with and without CT evidence of emphysema, the 82 with emphysema had a smaller average change of the main PA between the pre-surgical and the last reviewable CT than the 60 without emphysema (1.0 mm vs. 1.8 mm, P = 0.08).

      Conclusion:
      Patients undergoing lobectomy appear to be at increased risk for enlargement of their pulmonary artery diameters after surgery. These results show that a focus on all the organs in the thorax, not just the lungs themselves, is important when evaluating postoperative lung resection CTs.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

    • +

      P2.16-024 - Effect of Resection of Stage 1 Lung Cancer on Lung Volume (Now Available) (ID 10248)

      09:30 - 09:30  |  Author(s): Claudia I Henschke

      • Abstract
      • Slides

      Background:
      The anatomic and physiologic effects of lung resection for early stage lung cancer patients have not been extensively reported. We hypothesize that patients who have undergone lobectomy or wedge resection will have reduced lung volume on the affected side immediately after surgery while the lung volume on the opposing side may increase to compensate.

      Method:
      The Mount Sinai database was queried for stage 1 lung cancer patients who underwent lobectomy or wedge resection and had both pre-operative and postoperative CT imaging. Surgeries were performed by thoracic surgeons. The lung volumes on all CT scans were measured using previously published research software including actual volumes for each lung (left and right) at each time point as well as a set of volumes normalized to the overall chest volume in order to compensate for differences in inspiration.

      Result:
      In the cohort of 21 patients who met the above criteria, the median follow-up time from the date of surgery to the most recent CT was 44.6 months (IQR: 23.5-94.7 months). The median age was 63 and the median pack years was 40. There were 2 patients for which only one post-op scan was successfully analyzed; the remaining cases all had two postop scans. In 20 of the 21 patients, the lung volume on the side where the surgery occurred was reduced in the first postop CT scan (average reduction in volume of 5.6%). The change in volume of the contralateral side (not undergoing surgery), was highly variable, with 11 cases showing an increase in volume on both post-op scans, 2 cases showing a decrease, and 8 cases showing an increase in volume at the first postop scan followed by a decrease in volume on the second post-op scan.

      Conclusion:
      Stage 1 lung cancer patients undergoing resection have reduced lung volume on the side of surgery, however there was marked variability in the contralateral lung suggesting that the extent to which patients compensate post operatively is complex and dependent on many factors.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

  • +

    P3.13 - Radiology/Staging/Screening (ID 729)

    • Event: WCLC 2017
    • Type: Poster Session with Presenters Present
    • Track: Radiology/Staging/Screening
    • Presentations: 2
    • Now Available
    • +

      P3.13-028 - Controversies on Lung Cancers Manifesting as Part-Solid Nodules (Now Available) (ID 10074)

      09:30 - 09:30  |  Author(s): Claudia I Henschke

      • Abstract
      • Slides

      Background:
      Questions have been raised about the appropriate treatment of lung cancers manifesting as subsolid nodules (nonsolid nodules (NSNs) and part-solid nodules (PSNs)), as these have very high reported survival rates and have been observed in up to 10% of screening participants. Our goal in this report is to summarize the publications on survival of patients with resected lung cancers manifesting as PSNs and to further the development of consensus definitions of the CT appearance and the workup of such nodules.

      Method:
      PubMed/MEDLINE and EMBASE databases were searched for all studies/ clinical trials on CT-detected lung cancer in English before Dec 21, 2015 to identify surgically-resected lung cancers manifesting as PSNs. Outcome measures were lung cancer-specific survival (LCS), overall survival (OS), or disease free survival (DFS). All PSNs were classified by the percentage of solid component to the entire nodule diameter into: Category PSNs < 80% or Category PSNs ≥ 80%.

      Result:
      Twenty studies reported on PSNs < 80%: 7 reported DFS and 2 OS of 100%, 6 DFS 96.3-98.7%, and 11 OS 94.7-98.9% (median DFS 100% and OS 97.5%). Twenty-seven studies reported on PSNs ≥ 80%: 1 DFS and 2 OS of 100%, 19 DFS 48.0%-98.0% (median 82.6%), and 16 reported OS 43.0%-98.0% (median DFS 82.6%, OS 85.5%). Both DFS and OS were always higher for PSNs<80%.

      Conclusion:
      A clear definition of the upper limit of solid component of a PSN is needed to avoid misclassification because cell-types and outcomes are different for PSN and solid nodules. The workup should be based on the size of the solid component.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

    • +

      P3.13-035 - Automatic Estimation of Measurement Error on CT Imaging (ID 10333)

      09:30 - 09:30  |  Author(s): Claudia I Henschke

      • Abstract

      Background:
      There has been increasing recognition that lung nodule measurement on CT scans is imprecise and that an understanding of the extent of this imprecision is necessary when trying to determine whether actual change in volume has occurred. The various factors that influence this are numerous with two of the most prominent being the overall quality of the CT scan (including all of the adjustable parameters) and the size of the nodule.

      Method:
      We have developed an automated system whereby a calibration device is scanned on a given scanner with a given protocol and then the system can automatically predict the extent of measurement error for a given size solid nodule. We compared this approach to empirically derived results obtained from a database of 117 screen-detected stable nodule ranging in size from 2.2 to 18.7 mm that were scanned twice on the same CT scanner using the same protocol. Automated volumetric analysis was performed using commercial software. This allowed us to determine the relationship between standard deviation of the measurements versus nodule size. We then scanned our calibration device using the same scanning protocol as was used on those nodules to automatically calculate the size and standard deviation relationship.

      Result:
      Predicted solid nodule volume standard deviation compared with empirically derived values across a range of nodule sizes was within 20% (see figure)Figure 1



      Conclusion:
      Results from our automated approach were highly correlated with results obtained from scans obtained in actual clinical practice. The ability to predict extent of error specific to a given scanner and scanning protocol is an essential step in understanding whether change has occurred and has implications for both diagnosis and therapy assessment, including predicting when a follow up scan should be obtained. This type of information will ultimately become a necessary component of all quantitative imaging programs.

  • +

    PL 01 - Prevention, Screening, and Management of Screen-Detected Lung Cancer (ID 586)

    • Event: WCLC 2017
    • Type: Plenary Session
    • Track: Radiology/Staging/Screening
    • Presentations: 1
    • Now Available
    • +

      PL 01.02 - Major Advances in CT Screening: A Radiologist's Perspective (Now Available) (ID 7838)

      08:35 - 08:55  |  Presenting Author(s): Claudia I Henschke

      • Abstract
      • Presentation
      • Slides

      Abstract:
      Advances in CT scanners. CT screening was first introduced when helical CT scanners became available in the early 1990’s (1-4). Since then, there have been remarkable advances in CT scanner technology with concurrent increase in the number of CT examinations per year by approximately 10% annually. More powerful hardware and image reconstruction algorithms have allowed faster scanning at lower radiation doses in today’s multidetector CT (MDCT) scanners. Ultra low-dose techniques are gaining acceptance. With respect to lung cancer screening, thinner collimation now possible has led to the detection of many more small pulmonary nodules. Also, there have been evolutions in diagnostic techniques such as percutaneous biopsies, navigational bronchoscopy, and PET scans and these advances have been integrated into the regimen of screening with a resulting decrease in the frequency of surgical resection of benign nodules (5). Definition of Positive Results. Updates in the definition of positive results have continued to be developed that allow for improvements in the efficiency of workups. One of the major changes has been to update the size thresholds for positive results from 4 to 6 mm and also to avoid rounding errors (6, 7). The NELSON trial introduced the concept that a positive result should be based on the initial CT scan and a follow-up CT scan for small nodules, rather than solely on the initial CT scan and this has been adopted by I-ELCAP (6). The I-ELCAP and NLST databases have been used to provide follow-up strategies for nonsolid and part-solid nodules (6). Considerations as to screening frequency may substantially reduce costs for lower risk individuals. There is increasing recognition that different approaches are needed for baseline and repeat scans where even when nodules might have the same characteristics as they should be managed differently. The management of both nonsolid and part-solid nodules has dramatically changed. For the first time, imaging as a biomarker for aggressiveness has been used to monitor whether a cancer is progressing. Growing nonsolid nodules can be followed on an annual basis and only the emergence of a solid component triggers more aggressive intervention. For the part-solid nodule it has now been recognized that the important component from a prognostic perspective is the solid portion not the overall size. Quantitative assessments. Quantitative assessment of many findings on chest CT scans have been developed (6). In particular, assessment of nodule size and growth as to the probability of malignancy and lung cancer aggressiveness has progressed. Most guideline organizations have moved from a single measurement of length to an average diameter (average of length and width) (6) and to three measurements of volume (7). The errors involved in any of these measurements are influenced by multiple factors including the intrinsic properties of the nodule and the software used to make the measurement (8, 9). Additionally, they are impacted by the variability of CT scanners and their adjustable scan parameters. Advances in incorporating measurement errors into growth assessment by RSNA’s Quantitative Imaging Biomarkers Alliance (QIBA) has led to a web-based calculator. The American College of Radiology (ACR) specifies that growth for a nodule of any size requires “an increase of 1.5 mm or more.” Both approaches allow for large measurement errors for the wide range of CT scanners and the protocols. The I-ELCAP guidelines for solid and the solid component of part-solid nodules is given explicitly in I-ELCAP protocol (6). Each of these approaches has specific technical requirements as measurement error is influenced by both the scanner itself, the choice of various adjustable parameters on the scanner (slice thickness, slice spacing, dose, FOV, pitch, recon kernel etc.) as well as characteristics of the nodule itself. Additional considerations for computer-assisted volume change assessment requires: 1) inspecting the computer scans and the segmentation for image quality (e.g. motion artifacts) and for the quality of the segmentation; 2) the radiologist visually inspecting both nodule image sets side-by-side to verify the quality of the computer segmentation for each image that contains a portion of the nodule; 3) examination of the segmentations for errors such as when a vessel is segmented as part of a nodule in one scan but not in the other; 4) that the scan slice thickness for the purpose of volumetric analysis should be 1.25 mm or less. When using any computer-assisted software, the radiologist must be satisfied with the CT image quality and the computer segmentation results, further substantiating the notion that the decision of whether growth has occurred is ultimately based on clinical judgment. Innovations in use of imaging and genetic information. Radiomics is an emerging field of study on the quantitative processing and analysis of radiologic images and metadata to extract information on tumor behavior and patient survival (10). The hypothesis is that data analysis through automated or semi-automated software can provide more information than that of a physician. Its use has shown improved diagnostic accuracy in discriminating lung cancer from benign nodules. It has been used successfully in breast imaging, with 2017 FDA approval of a computer-aided diagnosis tool which utilizes advanced machine learning analytics. Furthermore, radiomics has been linked with the field of genomics, inferring that imaging features are closely linked to gene signatures such as EGFR expression, a known therapeutic target. In the future, as larger data sets emerge and inter-institutional sharing of images becomes more commonplace, radiomics will become more tightly integrated with lung cancer diagnosis, treatment planning, and patient survival prognostication. References 1. Henschke C, McCauley D, Yankelevitz D, Naidich D, McGuinness G, Miettinen O, Libby D, Pasmantier M, Koizumi J, Altorki N, and Smith J. Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 1999; 354:99-105. 2. The International Early Lung Cancer Action Program Investigators. Survival of Patients with Stage I lung cancer detected on CT screening. NEJM 2006; 355:1763-71 3. Kaneko M, Eguchi K, Ohmatsu H, Kakinuma R, Naruke T, Suemasu K, and Moriyama N. Peripheral lung cancer: screening and detection with low-dose spiral CT versus radiography. Radiology 1996; 201: 798-802. 4. Sone S, Nakayama T, Honda T, Tsushima K, Li F, Haniuda M, et al. Long-term follow-up study of a population-based 1996-1998 mass screening programme for lung cancer using mobile low-dose spiral computed tomography. Lung Cancer. 2007; 58:329-41. 5. Linek HC, Flores RM, Yip R, Hu M, Yankelevitz DF, Powell CA. Non-malignant resection rate is lower in patients who undergo pre-operative fine needle aspiration for diagnosis of suspected early-stage lung cancer. Am J Respir and Crit Care Med 2015; 191: A3561 6. International Early Lung Cancer Action Program protocol. http://www.ielcap.org/sites/default/files/I-ELCAP%20protocol-v21-3-1-14.pdf Accessed March 27, 2015 7. Van Klaveren RJ et al. Management of Lung Nodules Detected by Volume CT Scanning. N Engl J of Medicine 2009; 361: 2221-9 8. Henschke CI, Yankelevitz DF, Yip R, Archer V, Zahlmann G, Krishnan K, Helba B, Avila R. Tumor volume measurement error using computed tomography (CT) imaging in a Phase II clinical trial in lung cancer. Journal of Medical Imaging 2016; 3:035505 9. Avila RS, Jirapatnakul A, Subramaniam R, Yankelevitz D. A new method for predicting CT lung nodule volume measurement performance. SPIE Medical Imaging 2017: 101343Y 10. Lee G, Lee HY, Park H, et al. Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art. Eur J Radiol. 2017; 86:297-307.

      Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

  • +

    WS 01 - IASLC Supporting the Implementation of Quality Assured Global CT Screening Workshop (By Invitation Only) (ID 632)

    • Event: WCLC 2017
    • Type: Workshop
    • Track: Radiology/Staging/Screening
    • Presentations: 1
    • +

      WS 01.12 - Planning for USA Registries for CT Screened Images – What Are Their Objectives? (ID 10650)

      10:20 - 10:35  |  Author(s): Claudia I Henschke

      • Abstract
      • Slides

      Abstract:
      The reimbursement of low dose CT lung cancer screening for high risk populations in the United States by the Centers for Medicare and Medicaid Services (CMS) [1] has been implemented with a requirement to participate in a nationwide registry run by the American College of Radiology (ACR) [2]. This registry’s main purpose is to enable the collection of basic information on lung cancer screening including patients’ demographic information, medical history and risk factors, procedure indications, and follow-up information. Owing in part to the large data sizes of low dose CT lung cancer screening studies, which can exceed 500 MB for each 3D CT scan acquisition, this important US lung cancer screening registry is not collecting CT image data. However, the I-ELCAP study has been collecting international lung cancer screening data, including CT scan images, for over two decades [3]. There are several important benefits to collecting CT lung cancer screening image datasets in addition to basic lung cancer screening information. CT image data provides important information on the quality of actual scans and findings in the field, which can help identify areas of improvement for national screening efforts as well as for the local lung cancer screening site. One of the most important benefits is that expert review of these scans and findings can help train local radiologists on how to improve delivery of lung cancer screening. In addition, many image acquisition characteristics can be automatically evaluated that influence lung cancer screening performance. Determining whether patients are being over scanned (outside the lung region), whether the CT table was properly positioned, and whether the CT reconstruction field of view was properly set can be evaluated are some of the areas that can be evaluated using automated analysis methods provided that the CT scan datasets are available for processing. Also, new image quality standards for CT lung cancer screening data acquisition are becoming available and these requirements can potentially be evaluated against actual scans acquired. Another important benefit that is enabled by CT lung cancer screening image data registries is the potential to identify new imaging biomarkers as well as help improve existing imaging biomarkers. A persistent challenge for lung cancer imaging research groups is to continuously collect lung cancer screening image data obtained from current day patients and using modern CT scanners. Given that CT scanner technology and methods are changing rapidly it is particularly important to have a large continuous source of imaging data, which a large image-based registry can provide. In addition to informed consent to conduct research and patient privacy protections, studies based on registry data can support the lung cancer imaging research community by further collecting additional quantitative metadata with each CT scan. The collection of images allows for retrospective reviews of imaging findings that were not known to be important for the different diseases that may occur in the lungs. One such example is recognition of early interstitial lung disease which can be as deadly as lung cancer [4]. Having the prior images for review once a diagnosis is made allows for future early recognition and for development of follow-up recommendations. Growing recognition of subtypes of nodules (subsolid and solid), both solitary and multiple ones, and review of prior imaging has been important in limiting invasive procedures for certain subtypes [5, 6]. Automated methods can potentially be used by image-based registries to calculate and store the location, surface geometry, and volume of the lungs, suspicious nodules, cancer tumors, and relevant anatomy and pathology. If data transmission bandwidth is a roadblock to collecting image data, automated methods can be employed to at least collect images of identified lung cancers and other targeted areas (e.g. suspicious lung nodule regions). Another opportunity is to document the fundamental image quality characteristics of CT scans, as is becoming available using automated methods. Documenting image quality information within large lung cancer screening image datasets will enable the research community to better understand the relationship between image quality and measures of lung cancer screening success, such as the ability to detect and measure small lung nodules. This data will be critical to help inform the establishment of new minimum imaging standards that are being developed for lung cancer screening studies. Over the next few years several new lung cancer screening initiatives will launch in the United States including an effort to deploy lung cancer screening services at US Department of Veterans Affairs Medical Centers. These lung cancer screening studies will offer a fresh opportunity to collect lung cancer screening image data with modern tools, research targets, and methods. References 1. CMS recommendation to support reimbursement for lung cancer screening, , February 5, 2015. 2. Pederson JH, Ashraf H, Implementation and organization of lung cancer screening, Ann Transl Med. 2016 Apr; 4(8): 152. 3. Yankelevitz DF, Henschke CI, Advancing and sharing the knowledge base of CT screening for lung cancer, Ann Transl Med. 2016 Apr; 4(8): 154. 4. Salvatore M, Henschke CI, Yip R, Jacobi A, Eber C, Padilla M, Koll A, Yankelevitz D. Journal Club: Evidence of Interstitial Lung Disease on Low-Dose Chest CT: Prevalence, Patterns and Progression. AJR AM J Roentgenol 2016: 206:487-94 5. Yankelevitz DF, Yip R, Smith JP, Liang M, Liu Y, Xu DM, Salvatore M, Wolf A, Flores R, Henschke CI. CT screening for lung cancer: nonsolid nodules in baseline and annual repeat rounds. Radiology 2015; 277: 555-64 6. Henschke CI, Yip R, Wolf A, Flores R, Liang M, Salvatore M, Liu Y, Xu DM, Smith JP, Yankelevitz DF. CT screening for lung cancer: part-solid nodules in baseline and annual repeat rounds. AJR Am J Roentgenol 2016; 11:1-9

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

  • +

    WS 02 - IASLC Symposium on the Advances in Lung Cancer CT Screening (Ticketed Session SOLD OUT) (ID 631)

    • Event: WCLC 2017
    • Type: Symposium
    • Track: Radiology/Staging/Screening
    • Presentations: 4
    • Now Available
    • +

      WS 02.01 - Welcome to the Special Symposium (Now Available) (ID 10583)

      09:00 - 09:10  |  Presenting Author(s): Claudia I Henschke

      • Abstract
      • Presentation

      Abstract not provided

      Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.

    • +

      WS 02.03 - Lung Cancer Screening – IELCAP Contribution to CT Screening Implementation (Now Available) (ID 10620)

      09:10 - 10:10  |  Presenting Author(s): Claudia I Henschke

      • Abstract
      • Presentation

      Abstract:
      1. Introduction of CT screening and showing its value. First to introduce CT screening in a novel cohort design comparing CT with chest radiography, providing a workup strategy for screen-detected nodules. Predicted outcome of well-designed and correctly powered RCT studies Henschke C, McCauley D, Yankelevitz D, Naidich D, McGuinness G, Miettinen O, Libby D, Pasmantier M, Koizumi J, Altorki N, and Smith J. Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 1999; 354:99-105. 2. Long-term survival rates of patients diagnosed with lung cancer in a program of CT screening. First to provide estimated cure rates under screening by measuring long-term survivial. The International Early Lung Cancer Action Program Investigators. Survival of Patients with Stage I lung cancer detected on CT screening. NEJM 2006; 355:1763-71 3. First to provide information on the value of CT scans in delivering smoking cessation advice. Ostroff J, Buckshee N, Mancuso C, Yankelevitz D, and Henschke C. Smoking cessation following CT screening for early detection of lung cancer. Prev Med 2001; 33:613-21. Anderson CM, Yip R, Henschke CI, Yankelevitz DF, Ostroff JS, and Burns DM. Smoking cessation and relapse during a lung cancer screening program. Cancer Epidemiol Biomarkers Prev 2009; 18:3476-83. 4. First to introduce computer-assisted CT determined growth rates into the workup of pulmonary nodules. Yankelevitz DF, Gupta R, Zhao B, and Henschke CI. Small pulmonary nodules: evaluation with repeat CT--preliminary experience. Radiology 1999; 212:561-6. Yankelevitz DF, Reeves AP, Kostis WJ, Zhao B, and Henschke CI. Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology 2000; 217:251-6. Kostis WJ, Yankelevitz DF, Reeves AP, Fluture SC, Henschke CI. Small pulmonary nodules: reproducibility of three-dimensional volumetric measurement and estimation of time to follow-up CT. Radiology 2004; 231:446-52. Henschke C, Yankelevitz D, Yip R, Reeves A, Farooqi A, Xu D, Smith J, Libby D, Pasmantier M, and Miettinen O. Lung cancers diagnosed at annual CT screening: volume doubling times. Radiology 2012; 263:578-83. 5. Development of size threshold values and short-term followup and importance of a regimen of screening. Henschke C, Yankelevitz D, Naidich D, McCauley D, McGuinness G, Libby D, Smith J, Pasmantier M, and Miettinen O. CT screening for lung cancer: suspiciousness of nodules according to size on baseline scans. Radiology 2004; 231:164-8. Libby DM, Wu N, Lee IJ, Farooqi A, Smith JP, Pasmantier MW, McCauley D, Yankelevitz DF, and Henschke CI. CT screening for lung cancer: the value of short-term CT follow-up. Chest 2006; 129:1039-42. Henschke C, Yip R, Yankelevitz D, and Smith J. Definition of a positive test result in computed tomography screening for lung cancer: a cohort study. Ann Intern Med 2013; 158:246- 52. Yip R, Henschke CI, Yankelevitz DF, and Smith JP. CT screening for lung cancer: alternative definitions of positive test result based on the national lung screening trial and international early lung cancer action program databases. Radiology 2014; 273:591-6. Yip R, Henschke C, Yankelevitz D, Boffetta P, Smith J, The International Early Lung Cancer Investigators. The impact of the regimen of screening on lung cancer cure: a comparison of I-ELCAP and NLST. Eur J Cancer Prev. 2015;24(3):201-8. 6. Nomenclature and management protocols for nonsolid and part-solid nodules. Henschke C, Yankelevitz D, Mirtcheva R, McGuinness G, McCauley D, and Miettinen O. CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. AJR Am J Roentgenol 2002; 178:1053-7. Yankelevitz DF, Yip R, Smith JP, Liang M, Liu Y, Xu DM, Salvatore MM, Wolf AS, Flores RM, Henschke CI, and International Early Lung Cancer Action Program Investigators Group. CT Screening for Lung Cancer: Nonsolid Nodules in Baseline and Annual Repeat Rounds. Radiology 2015; 277:555-64. Henschke CI, Yip R, Wolf A, Flores R, Liang M, Salvatore M, Liu Y, Xu DM, Smith JP, Yankelevitz DF. CT screening for lung cancer: part-solid nodules in baseline and annual repeat rounds. AJR Am J Roentgenol 2016; 11:1-9. 7. Differences in management of nodules found in baseline and annual repeat rounds of screening. International Early Lung Cancer Investigators. Baseline and annual repeat rounds of screening: implications for optimal regimens of screening. Eur Radiol 2017. In press. 8. Assessment of risk of lung cancer among women and never smokers. International Early Lung Cancer Action Program Investigators. Women’s susceptibility to tobacco carcinogens and survival after diagnosis of lung cancer. JAMA 2006; 296:180-4. Yankelevitz DF, Henschke CI, Yip R, Boffetta P, Shemesh J, Cham MD, Narula J, Hecht HS, FAMRI-IELCAP Investigators. Second-hand tobacco smoke in never smokers is a significant risk factor for coronary artery calcification. JACC Cardiovasc Imaging 2013; 6:651-7. Henschke CI, Yip R, Boffetta P, Markowitz S, Miller A, Hanaoka T, Zulueta J, Yankelevitz D. CT screening for lung cancer: importance of emphysema for never smokers and smokers. Lung Cancer 2015; 88:42-7 PMID:25698134. Yankelevitz DF, Cham MD, Hecht HS, Yip R, Shemesh S, Narula J, Henschke CI. The Association of Secondhand Tobacco Smoke and CT angiography-verified coronary atherosclerosis. JACC Imaging. 2016. 9. Determination of cardiac risk on nongated, low-dose CT scans and development of an ordinal scale. Shemesh J, Henschke CI, Farooqi A, Yip R, Yankelevitz DF, Shaham D, and Miettinen OS. Frequency of coronary artery calcification on low-dose computed tomography screening for lung cancer. Clin Imaging 2006; 30:181-5. Shemesh J, Henschke CI, Shaham D, Yip R, Farooqi AO, Cham MD, McCauley DI, Chen M, Smith JP, Libby DM, Pasmantier MW, and Yankelevitz DF. Ordinal scoring of coronary artery calcifications on low-dose CT scans of the chest is predictive of death from cardiovascular disease. Radiology 2010; 257:541-8. 10. Recommendations for reporting findings of emphysema, coronary arteries, breast, and abdomen on low-dose CT scans. Zulueta JJ, Wisnivesky JP, Henschke CI, Yip R, Farooqi AO, McCauley DI, Chen M, Libby DM, Smith JP, Pasmantier MW, and Yankelevitz DF. Emphysema scores predict death from COPD and lung cancer. Chest 2012. Henschke CI, Lee IJ, Wu N, Farooqi A, Khan A, Yankelevitz D, and Altorki NK. CT screening for lung cancer: prevalence and incidence of mediastinal masses. Radiology 2006; 239:586-90. Salvatore M, Margolies L, Kale M, Wisnivesky J, Kotkin S, Henschke CI, and Yankelevitz DF. Breast density: comparison of chest CT with mammography. Radiology 2014; 270:67-73. Hu M, Yip R, Yankelevitz D, and Henschke C. CT screening for lung cancer: frequency of enlarged adrenal glands identified in baseline and annual repeat rounds. Eur Radiol 2016. Chen X, Li K, Yip R, Perumalswami P, Branch AD, Lewis S, Del Bello D, Becker BJ, Yankelevitz DF, and Henschke CI. Hepatic steatosis in participants in a program of low-dose CT screening for lung cancer. European Journal of Radiology 2017. In Press. 11. Quantatative assessment of the vascular system on low-dose CT scans. Fully automated evaluation of quantitaive image biomarkers for multple organs and anatomic regions including: pulmoanry nodules, lungs (emphysema, ILD, major airways), coronary arteries, aorta, pulmoanry artery, breast, and vertebra. Kostis, W. J., Reeves, A. P., Yankelevitz, D. F., and Henschke, C. I. Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Transactions on Medical Imaging 2003; 22: 1259-1274. Enquobahrie, A., Reeves, A. P., Yankelevitz, D. F., and Henschke, C. I. Automated detection of small solid pulmonary nodules in whole lung CT scans from a lung cancer screening study. Academic Radiology 2003; 14, 5: 579-593. Keller, B. M., Reeves, A. P., Henschke, C. I., and Yankelevitz, D. F. Multivariate Compensation of Quantitative Pulmonary Emphysema Metric Variation from Low-Dose, Whole-Lung CT Scans. AJR 2011; 197, 3: W495-W502. Xie Y. Htwe YM, Padgett J, Henschke CI, Yankelevitz DF, Reeves AP. Automated aortic calcification detection in low-dose chest CT images. SPIE Medical Imaging 2014; 9035:90250P. Xie Y, Cham M, Henschke CI, Yankelevitz DF, Reeves AP. Automated coronary artery calcification detection on low-dose chest CT images. SPIE Medical Imaging 2014; 9035:90250F.

      Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.

    • +

      WS 02.08 - Session 2: Current Lung Cancer Screening Guidelines (Panel Discussion) (ID 10586)

      12:00 - 12:00  |  Presenting Author(s): Claudia I Henschke

      • Abstract

      Abstract not provided

    • +

      WS 02.15 - Quality Control for Lung Imaging Biomarkers (Now Available) (ID 10628)

      15:30 - 16:00  |  Author(s): Claudia I Henschke

      • Abstract
      • Presentation

      Abstract:
      Computed Tomography (CT) imaging of the lung has been routinely used over the last few decades to detect and treat early lung cancer and other related diseases. As CT image acquisition technology has improved, the use of CT for quantitative and precise lung imaging clinical applications has greatly expanded. High resolution CT studies, which now easily obtain sub-millimeter resolution of the entire chest within a breath-hold, are now widely used to detect and measure changes in early lung cancer lesions and COPD. Traditionally, several concurrent methods have been used to ensure that the quality of acquired CT images is adequate for general clinical use. This includes regular scanning and analysis of CT quality control phantoms from ACR (as well as from individual CT scanner manufacturers) and visual inspection of acquired images by radiologists for significant image artifacts. While these methods have served the field of radiology well for identifying and correcting major image quality issues, there has not been standard image quality assessment methods available for specific clinical applications that require precise image-based measurements. To improve global quality control of lung imaging studies, several clinical societies and organizations have provided image acquisition and measurement guidance documents intended to be followed by clinical sites [1, 2, 3]. We are entering a new era of quantitative imaging where easy to use tools are available that ensure that precise quantitative image measurements can be routinely and reliably obtained. To achieve this goal, a new set of task-based image quality control measures is being developed by research groups and radiology societies such as the RSNA’s Quantitative Imaging Biomarkers Alliance [4]. Each major quantitative imaging-based clinical task is being extensively studied to determine the fundamental image quality properties needed (e.g. resolution, sampling rate, noise, intensity linearity, spatial warping) to achieve a minimum level of measurement performance. In addition, new low-cost phantoms are being developed that can be quickly scanned and automatically analyzed to estimate these fundamental properties throughout the full three-dimensional CT scanner field of view. Deploying these low-cost phantoms and automated phantom analysis software on the cloud further enables global clinical sites to quickly and easily verify the quality of a CT scanner and acquisition protocol for a specific quantitative clinical task. In addition to providing a fast method for verifying conformance with minimum quantitative imaging performance standards, the reports generated can provide guidance as to the best protocols observed for a particular CT scanner model, thereby allowing a clinical site to optimize image acquisition protocols with the best evidence obtained through crowd-sourcing task-specific image quality information. The QIBA CT lung nodule task force is now preparing to launch a pilot project to evaluate the utility of these new image quality control measures for the quantitative measurement of the change in volume of solid lung nodules (6mm to 10mm diameter) [5]. Over the coming months this new “active” and cloud-based analysis approach will be deployed at international lung cancer screening institutions and use statistics will be assembled. The data collected has the potential not only to inform the lung cancer screening community on the global quality of lung cancer screening imaging, but also to establish early data on whether these new methods can one day serve as a more effective approach to providing quality control for quantitative imaging methods. References 1. Kauczor HU, Bonomo L, Gaga M, Nackaerts K, Peled N, Prokop M, Remy-Jardin M, von Stackelberg O, Sculier JP; European Society of Radiology (ESR); European Respiratory Society (ERS), ESR/ERS white paper on lung cancer screening, ESR/ERS white paper on lung cancer screening. 2. IELCAP, IELCAP Protocol Document, http://www.ielcap.org/sites/default/files/I-ELCAP-protocol.pdf Accessed May 31, 2017. 3. Fintelmann FJ, Bernheim A, Digumarthy SR, Lennes IT, Kalra MK, Gilman MD, Sharma A, Flores EJ, Muse VV, Shepard JA, The 10 Pillars of Lung Cancer Screening: Rationale and Logistics of a Lung Cancer Screening Program, Radiographics. 2015 Nov-Dec;35(7):1893-908. 4. https://www.rsna.org/QIBA/ 5. RSNA QIBA, Draft QIBA Profile: Lung Nodule Volume Assessment and Monitoring in Low Dose CT Screening, http://qibawiki.rsna.org/images/e/e6/QIBA_CT_Vol_LungNoduleAssessmentInCTScreening_2017.05.15.docx, May 15, 2017.

      Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.