Journal Thoracic Oncology

Abstracts Journal of Thoracic Oncology • Volume 12 Issue S1 January 2017
Background: LDCT screening for lung cancer often triggers follow-up scans for
indeterminate nodules. The non-invasive LuCED test for detection of early
stage lung cancer may resolve nodule findings and reduce LDCT false
positives. In LuCED, patient sputum is analyzed by the Cell-CT® which
computes 3D images of single cells allowing measurement of 3D structural
biomarkers to identify potential abnormal cells. Final case disposition is
determined through cytology review of these cells. Example images of
abnormal cells identified by LuCED are shown in the figure.
the NELSON program for lung cancer screening in different scenarios in
order to assess the robustness of the chosen approach. We are looking to
develop a model that allows for testing the imaging protocol performance
using various high-risk screening populations. Our Objective is to work out a
simulator adaptive to multiple screening scenarios. In a first step, we tested
a simulation of the NELSON triage algorithm by using published statistics as
input data: the distribution of nodule size, the precision of nodule volume
measurements and the distribution of nodules growth. Methods: We modeled
the baseline round of NELSON triage algorithm. We simulated 10,000,000
ground truth (GT) data where the axial diameter of nodules followed a chi2
(df=1) distribution between 3 mm and 20 mm. For each of the GT nodule, we
modeled also a chi2 (df=1) distribution of volume doubling time between 90
and 1000 days. We included into the model a Gaussian distribution of the time
between visits (average: 105 days, standard deviation: 5 days). We modeled
volume measurement of the nodules by adding a Gaussian random error as
documented by the Quantitative Imaging Biomarker Alliance (QIBA) screening
profile. We performed a by-nodule comparison between nodule classification
by the triage algorithm and the corresponding GT in the first round. At each
step of the triage algorithm, we evaluated Sensitivity (Se), Specificity (Sp),
Positive Predictive Value (PPV) and Negative Predictive Value (NPV). Results:
Sensitivity of the triage algorithm for classifying nodules into size categories
was for 96,6% for NODCAT2, 86.9% for NODCAT3 and 90.7% for NODCAT4.
Classification of GROWCAT C yielded Se=66.2% / Sp=21.2%. We found an
overall performance of the NELSON triage algorithm of Se/Sp 94.0%/80.3%.
PPV was 11.3%, and NPV was 99.8% Conclusion: Mathematical modeling
gives valuable insights into the performance of different components of
triage algorithms in lung cancer screening. We found a markedly different
test performance for size versus growth assessment of the NELSON triage
algorithm. Future work will extent the model to non-solid nodules and
multiple rounds of screening. Moreover, it may have the potential to optimize
triage algorithms in the design of screening programs.
Keywords: Lung Screening, Modelling, triage algorithm, volume of nodules
MA01: IMPROVEMENT AND IMPLEMENTATION OF LUNG CANCER SCREENING
MONDAY, DECEMBER 5, 2016 - 11:00-12:30
Methods: Sputum samples from 127 patients were processed by LuCED: 65
patients had biopsy-confirmed lung cancer; and 62 patients were normal
controls. Sensitivity was computed as the percentage of cancer cases where
abnormal cells were found by LuCED. Generally, abnormal cells found in a case
otherwise understood to be normal could constitute a diagnostic overcall and
counted as a false positive. However, a finding of abundant (>5) abnormal cells
in cases understood to be normal indicates discovery of a possible occult
cancer or dysplastic lesion. Accordingly, these cases were not included in
specificity calculations. Results: For cancer cases, the histology included
adenocarcinoma (29 cases), squamous cancer (24), small cell lung cancer (5)
and undifferentiated cancer (7); representing stages 1 (14), 2 (11), 3 (25), 4 (14),
and unknown (1). Abnormal cells were found in 61 of 65 cancer cases for
sensitivity of 93.8%. For stage 1 and 2 cancer, sensitivity was 88%. Ten cells
exhibiting changes consistent with atypical adenomatous hyperplasia were
found in one case. After removal, there remained two false positive cases,
leading to specificity of 96.7% (N = 61). Conclusion: The LuCED test
demonstrates accurate detection of early stage lung cancer with the potential
of detecting pre-cancerous conditions of the lung. Results suggest that
suspicious nodules may be efficiently reconciled by LuCED when used
adjunctively with LDCT.
Keywords: indeterminate nodules, LuCED, Non-Invasise, LDCT
MA01: IMPROVEMENT AND IMPLEMENTATION OF LUNG CANCER SCREENING
MONDAY, DECEMBER 5, 2016 - 11:00-12:30
MA01.05 PREDICTIVE PERFORMANCES OF NELSON SCREENING
PROGRAM BASED ON CLINICAL, METROLOGICAL AND
POPULATION STATISTICS
Hubert Beaumont 1 , Nathalie Faye 2 , Antoine Iannessi 3 , Dag Wormanns 4
1 Sciences, Median Technologies, Valbonne/France, 2 Medical, Median Technologies,
Valbonne/France, 3 Radiology, Centre Antoine Lacassagne, Nice/France, 4 Radiology,
Evangelische Lungenklinik Berlin, Berlin/Germany
Background: The balance of benefits and harms of screening programs
depends on multiple factors such as the scenario of patient selection, the
triage algorithm and the imaging methods. Because of the multifactorial
nature of the outcome of screening programs, it is important to evaluate
the performance of its components. We modeled the triage algorithm of
MA01.06 LONG-TERM FOLLOW-UP OF SMALL PULMONARY
GROUND-GLASS NODULES STABLE FOR 3 YEARS: PROPER FOLLOW-
UP PERIOD AND RISK FACTORS FOR SUBSEQUENT GROWTH
Jaeyoung Cho, Eun Sun Kim, Se Joong Kim, Yeon Joo Lee, Jong Sun Park, Young-
Jae Cho, Ho Il Yoon, Jae Ho Lee, Choon-Taek Lee
Internal Medicine, Seoul National University Bundang Hospital, Seongnam/Korea,
Republic of
Background: It is uncertain how long persistent and stable ground-glass
nodules (GGNs) should be followed although a minimum of 3 years is
suggested. Here, we aimed to evaluate the proportion of GGNs showing
subsequent growth after initial 3 years among GGNs that had been stable
during the initial 3 years, and to determine clinical and radiologic factors
associated with subsequent growth. Methods: We retrospectively analyzed
patients who underwent further computed tomography after the initial
3-year follow-up period showing a persistent and stable GGN (at least 5-year
follow-up from initial CT). Results: Between May 2003 and June 2015, 453
GGNs (438 pure GGNs and 15 part-solid GGNs) were found in 218 patients. Of
the 218 patients, 14 patients had 15 GGNs showing subsequent growth after
the initial 3 years during the median follow-up period of 6.4 years. For the
person-based analysis, frequency of subsequent growth of GGNs that had
been stable during initial 3 years was 6.7% (14/218). For the nodule-based
analysis, the frequency was 3.3% (15/453). In a multivariate analysis, age ≥
65 years (odds ratio [OR], 5.51; p = 0.012), history of lung cancer (OR, 6.44; p =
0.006), initial size ≥ 8 mm (OR, 5.74; p = 0.008), presence of a solid component
(OR, 16.58; p = 0.009), and an air bronchogram (OR, 5.83; p = 0.015) were
independent risk factors for subsequent GGN growth.Between May 2003 and
June 2015, 453 GGNs (438 pure GGNs and 15 part-solid GGNs) were found in 218
patients. Of the 218 patients, 14 patients had 15 GGNs showing subsequent
growth after the initial 3 years during the median follow-up period of 6.4
years. For the person-based analysis, frequency of subsequent growth of
GGNs that had been stable during initial 3 years was 6.7% (14/218). For the
nodule-based analysis, the frequency was 3.3% (15/453). In a multivariate
analysis, age ≥ 65 years (odds ratio [OR], 5.51; p = 0.012), history of lung cancer
(OR, 6.44; p = 0.006), initial size ≥ 8 mm (OR, 5.74; p = 0.008), presence of a solid
component (OR, 16.58; p = 0.009), and an air bronchogram (OR, 5.83; p = 0.015)
were independent risk factors for subsequent GGN growth. Conclusion: For
the individuals with GGNs having risk factors described above, the longer
follow-up period is required to confirm subsequent GGN growth.
Keywords: ground glass nodule, follow-up, growth, computed tomograpy
Copyright © 2016 by the International Association for the Study of Lung Cancer
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