PHP48 COST SENSITIVENESS AND PHYSICIAN TREATMENT CHOICES

Objectives To explore the relationship between nodule count and lung cancer probability in baseline low-dose CT lung cancer screening. Materials and Methods Included were participants from the NELSON trial with at least one baseline nodule (3392 participants [45% of screen-group], 7258 nodules). We determined nodule count per participant. Malignancy was confirmed by histology. Nodules not diagnosed as screen-detected or interval cancer until the end of the fourth screening round were regarded as benign. We compared lung cancer probability per nodule count category. Results 1746 (51.5%) participants had one nodule, 800 (23.6%) had two nodules, 354 (10.4%) had three nodules, 191 (5.6%) had four nodules, and 301 (8.9%) had > 4 nodules. Lung cancer in a baseline nodule was diagnosed in 134 participants (139 cancers; 4.0%). Median nodule count in participants with only benign nodules was 1 (Inter-quartile range [IQR]: 1–2), and 2 (IQR 1–3) in participants with lung cancer (p = NS). At baseline, malignancy was detected mostly in the largest nodule (64/66 cancers). Lung cancer probability was 62/1746 (3.6%) in case a participant had one nodule, 33/800 (4.1%) for two nodules, 17/354 (4.8%) for three nodules, 12/191 (6.3%) for four nodules and 10/301 (3.3%) for > 4 nodules (p = NS). Conclusion In baseline lung cancer CT screening, half of participants with lung nodules have more than one nodule. Lung cancer probability does not significantly change with the number of nodules. Baseline nodule count will not help to differentiate between benign and malignant nodules. Each nodule found in lung cancer screening should be assessed separately independent of the presence of other nodules

Relationship between the number of new nodules and lung cancer probability in incidence screening rounds of CT lung cancer screening: The NELSON study

Background: New nodules are regularly found after the baseline round of low-dose computed tomography (LDCT) lung cancer screening. The relationship between a participant's number of new nodules and lung cancer probability is unknown. Methods: Participants of the ongoing Dutch-Belgian Randomized Lung Cancer Screening (NELSON) Trial with (sub)solid nodules detected after baseline and registered as new by the NELSON radiologists were included. The correlation between a participant's new nodule count and the largest new nodule size was assessed using Spearman's rank correlation. To evaluate the new nodule count as predictor for new nodule lung cancer together with largest new nodule size, a multivariable logistic regression analysis was performed. Results: In total, 705 participants with 964 new nodules were included. In 48% (336/705) of participants no nodule had been found previously during baseline screening and in 22% (154/705) of participants >1 new nodule was detected (range 1–12 new nodules). Eventually, 9% (65/705) of the participants had lung cancer in a new nodule. In 100% (65/65) of participants with new nodule lung cancer, the lung cancer was the largest or only new nodule at initial detection. The new nodule lung cancer probability did not differ significantly between participants with 1 (10% [56/551], 95%CI 8–13%) or >1 new nodule (6% [9/154], 95%CI 3–11%, P =.116). An increased number of new nodules positively correlated with a participant's largest nodule size (P < 0.001, Spearman's rho 0.177). When adjusted for largest new nodule size, the new nodule count had a significant negative association with lung cancer (odds ratio 0.59, 0.37–0.95, P =.03). Conclusion: A participant's new nodule count alone only has limited association with lung cancer. However, a higher new nodule count correlates with an increased largest new nodule size, while the lung cancer probability remains equivalent, and may improve lung cancer risk prediction by size only

Comparison of three software systems for semi-automatic volumetry of pulmonary nodules on baseline and follow-up CT examinations

Background: Early diagnosis of lung cancer in a treatable stage is the main purpose of lung cancer screening by computed tomography (CT). Accurate three-dimensional size and growth measurements are essential to assess the risk of malignancy. Nodule volumes can be calculated by using semi-automated volumetric software. Systematic differences in volume measurements between packages could influence nodule categorization and management decisions. Purpose: To compare volumetric measurements of solid pulmonary nodules on baseline and follow-up CT scans as well as the volume doubling time (VDT) for three software packages. Material and Methods: From a Lung Cancer Screening study (NELSON), 50 participants were randomly selected from the baseline round. The study population comprised participants with at least one pulmonary nodule at the baseline and consecutive CT examination. The volume of each nodule was determined for both time points using three semi-automated software packages (P-1, P-2, and P-3). Manual modification was performed when automated assessment was visually inaccurate. VDT was calculated to evaluate nodule growth. Volume, VDT, and nodule management were compared for the three software packages, using P-1 as the reference standard. Results: In 25 participants, 147 nodules were present on both examinations (volume: 12.0-436.6 mm(3)). Initial segmentation at baseline was evaluated to be satisfactory in 93.9% of nodules for P-1, 84.4 % for P-2, and 88.4% for P-3. Significant difference was found in measured volume between P-1 and the other two packages (P <0.001). P-2 overestimated the volume by 38 +/- 24%, and P-3 by 50 +/- 22%. At baseline, there was consensus on nodule size categorization in 80% for P-1& P-2 and 74% for P-1& P-3. At follow-up, consensus on VDT categorization was present in 47% for P-1& P-2 and 44% for P-1& P-3. Conclusion: Software packages for lung nodule evaluation yield significant differences in volumetric measurements and VDT. This variation affects the classification of lung nodules, especially in follow-up examinations

Quantification of growth patterns of screen-detected lung cancers: The NELSON study

Objectives Although exponential growth is assumed for lung cancer, this has never been quantified in vivo. Aim of this study was to evaluate and quantify growth patterns of lung cancers detected in the Du