E01.2 | Volumetrics for Nodule Assessment
Authors: Matthijs Oudkerk, Marjolein Heuvelmans
University of Groningen, University Medical Center Groningen, Center for Medical Imaging - North East Netherlands, Groningen/NETHERLANDS
Abstract:
Introduction
Lung cancer is a major health problem with no improvement in survival over the last decades. At time of diagnosis, lung cancer is often already in advanced stage, with 5-year survival of no more than 15%. Currently, several lung cancer screening trials investigating whether early detection of lung cancer in high-risk individuals will reduce lung cancer mortality are ongoing. In 2011, the National Lung Screening Trial (NLST), was the first and to date only reporting a 20% decrease in lung cancer mortality when three rounds of annual low-dose computed tomography (CT) were compared with three annual rounds of chest X-ray screening. A major challenge, however, is the high rate of positive test results reported by the NLST (24.2%). No less than 96.2% of these comprised false-positive test results, causing unnecessary patient anxiety, radiation exposure and cost.
The Dutch-Belgian lung cancer screening trial (Dutch acronym: NELSON study) was launched in September 2003. The NELSON study is an ongoing multicentre randomized controlled multi-detector low-dose CT lung cancer screening trial. The primary object is to investigate whether chest CT screening in year 1, 2, 4 and 6.5 will decrease lung cancer mortality by at least 25% in high-risk (ex-)smokers between 50 and 75 years of age compared to a control group receiving no screening. The NELSON study is the first lung cancer screening trial in which nodule management is based on nodule volume, instead of transverse cross-sectional nodule diameter for new nodules, and nodule growth in terms of volume doubling time (VDT) for existing ones. In this presentation, different aspects of nodule management in the NELSON study will be discussed.
Volume detection thresholds
Sensitive pulmonary nodule detection is crucial not to miss any lung cancer in a screening setting. The sensitivity of nodule detection was investigated by scanning a Lungman phantom according to the standard NELSON protocol. Nodules of five different volumes (range 14–905mm3) were randomly positioned in the phantom. A sensitivity of 100% was found for nodules with a volume equal to or larger than 65mm3 (5mm), and a sensitivity of 60–80% was found for solid nodules with a volume of 14mm3 (3mm). Since the lung cancer probability of lung nodules smaller than 50mm3 or 4mm is neglectable, the sensitivity of nodule detection using the NELSON protocol is sufficient for accurate detection of malignant lung nodules.
Measurement reproducibility
For accurate decision making in serial CT studies, nodule measurement reproducibility is essential. A sub-study of the NELSON trial showed a difference in repeatability among three reconstruction settings, demonstrating that the use of consistent reconstruction parameters is important. Volume measurements of pulmonary nodules obtained at 1mm section thickness combined with a soft kernel were found to be most repeatable. Another sub-study showed that variability on volume measurements is related to nodule size, morphology and location.
Besides image reproducibility, interobserver variability in performing semi-automated volume measurements is of major importance in the classification of lung nodules. Gietema et al. found that interobserver correlation was very high (r=0.99) in small-to-intermediate size (15-500mm3) nodules.
Volume criteria for nodule stratification
For solid nodules, and solid components of part-solid nodules, volume was calculated by 3-dimensional volumetric computer assessment, using LungCare software (version Somaris/5:VA70C-W; Siemens Medical Solutions). The final screen result was based on the nodule with largest volume or fasted growth. In the NELSON study, nodules were classified as negative if volume was <50mm3 (4.6mm diameter if the nodule would have been perfectly spherical), leading to an invitation for the regular next-round CT, as positive if nodule volume was >500mm3 (>9.8mm diameter), leading to direct referral to a pulmonologist for further workup, and as indeterminate in case of volume of 50-500mm3. Indeterminate nodules underwent a 6-week to 3-month follow-up low-dose CT for growth assessment.
Volumetric growth assessment of pulmonary nodules
After a nodule has been selected by a radiologist, the LungCare software automatically calculates nodule volume. Information is saved in the NELSON Management System (NMS), which calculates the growth in case of a pre-existing nodule. Growth is defined as a change in volume of ≥25% between two subsequent scans according to the formula:
Percentage volume change (%) = (V2-V1)/V1)*100
V2 = volume at last CT, and V1 = volume at previous examination.
Determination of the volume-doubling time
For solid nodules, or solid components of partial-solid nodules with PVC≥25%, the VDT is semi-automatically calculated by the NMS according to the formula:
VDT (days) = (ln(2)*Δt)/(ln(V2/V1))
Comparison between volumetric and diameter assessment of pulmonary nodules
For determining pulmonary nodule size, the use of volume measurements has been found to be more reliable than diameter measurements. In the previously mentioned phantom study, measurements of the manually measured maximal transverse diameter and semi-automated measurements of diameter and nodule volume were compared with actual properties. In both methods, diameter and volume of the spherical nodules were significantly underestimated. In diameter evaluation, the overall underestimation for solid nodules was about 10% using the manual method, compared with less than 4% using the semi-automated method. In volumetry, the overall underestimation for solid nodules was about 25% (translates into 8% diameter underestimation) using the manual method, compared with less than 8% (translates into 2.5% diameter underestimation) using the semi-automated method. It is important to keep in mind that a small change in diameter already corresponds to a considerably higher change in volume. Thus, in lung cancer screening we suggest nodule measurements by semi-automated volumetry should be used.
MO11.09 | CT Screening for Lung Cancer: Definition of Positive Test Result in the National Lung Screening Trial CT cohort compared with I-ELCAP
Authors: Rowena Yip1, David F. Yankelevitz2, Claudia I. Henschke1
1Icahn School of Medicine at Mount Sinai, New York City, NY/UNITED STATES OF AMERICA, 2Icahn School of Medicine at Mount Sinai, New YOrk City, NY/UNITED STATES OF AMERICA
Background:
Low-dose CT screening for lung cancer can reduce mortality among high-risk people but to reduce unnecessary evaluations with attendant risks, alternative thresholds for defining positive result and cancer diagnoses needs to be further understood. The purpose of the study is to assess the frequency of positive results and potential delays in diagnosis in the baseline round of screening using more restrictive thresholds.
Methods:
Among the participants who were randomly assigned to the CT arm of the National Lung Screening Trial (NLST) cohort, we identified the frequency of solid and part-solid pulmonary nodules and the rate of lung cancer diagnoses using a 5.0, 6.0, 7.0. 8.0 and 9.0 mm threshold for the largest noncalcified nodule identified in the baseline CT scan. we compared these results with those previously published for the I-ELCAP cohort.
Results:
The frequency of positive results in the baseline round, using the definition of positive result (any parenchymal, solid or part-solid, noncalcified nodule > 5.0 mm), was 15.9% (4,104/25,814). Using alternative threshold values of 6.0, 7.0, 8.0 and 9.0 mm, the frequencies (95% CI) of positive results were 10.5% (10.2, 10.9), 7.2% (6.9, 7.5), 5.3% (5.0, 5.6) , and 4.2% (3.9, 4.4), respectively. Use of these alternative definitions would have reduced the workup by 33.8%, 54.7%, 66.6%, and 73.8%, respectively. Concomitantly, proportion of lung cancer diagnoses made within first 12 months would be delayed for 0.9%, 2.6%, 6.1%, and 10.0% of the patients, respectively. These results are similar to those found in I-ELCAP.
Conclusion:
