Lung cancer screening with low-dose CT:Simulating the effect of starting screening at a younger age in women

BACKGROUND: The US has recently lowered the entry age for lung cancer screening with low-dose computed tomography (LDCT) from 55 to 50 years. The effect of the younger age for starting screening on the rates of screen-detected and radiation-induced lung cancers in women remains unclear.METHODS: A modeling study was conducted. A static cohort of 100,000 heavy female smokers was simulated to undergo annual lung cancer screening with LDCT. The number of screen-detected lung cancers (benefit) and radiation-induced lung cancers (harm) per 1000 screenees were calculated for scenarios with two starting ages (55-50 years) and fixed stopping age (75 years). The benefit-harm ratio and incremental benefit-harm ratio (IBHR) were calculated for each scenario.RESULTS: For annual screening from 55 to 75 years, the number of screen-detected and radiation-induced lung cancers was 112.4 and 2.2, respectively. For annual screening from 50 to 75 years, those numbers were 117.0 and 3.4, respectively. The benefit-harm ratio decreased from 51 to 35 and the IBHR decreased from 6.3 to 4.0 when lowering the screening starting age from 55 to 50 years.CONCLUSIONS: The risk of radiation induced lung cancers increased by 50% when lowering the screening starting age by 5 years in women. However, the benefits of LDCT lung cancer screening still outweigh the assumed radiation harm.</p

Supplementary data for a model-based health economic evaluation on lung cancer screening with low-dose computed tomography in a high-risk population

This supplementary data is supportive to the research article entitled ‘Cost-effectiveness of lung cancer screening with low-dose computed tomography (LDCT) in heavy smokers: A micro-simulation modelling study’ (Yihui Du et al. 2020). This supplementary contains a description of the model input and the related model output data that were not included in the research article. The input data used for the tumour growth model and the self-detected tumour size model are provided. The output data of this article include the data used for cost-effectiveness analysis of lung cancer LDCT screening with the Dutch and international discount rates, the data of the sensitivity analysis, and the data of the model validation

Influenza season influence on outcome of new nodules in the NELSON study

We evaluated the impact of the influenza season on outcome of new lung nodules in a LDCT lung cancer screening trial population. NELSON-trial participants with ≥ 1 new nodule detected in screening rounds two and three were included. Outcome (resolution or persistence) of new nodules detected per season was calculated and compared. Winter (influenza season) was defined as 1st October to 31st March, and compared to the summer (hay-fever season), 1st April to 30th September. Overall, 820 new nodules were reported in 529 participants. Of the total new nodules, 482 (59%) were reported during winter. When considering the outcome of all new nodules, there was no statistically significant association between summer and resolving nodules (OR 1.07 [CI 1.00-1.15], p = 0.066), also when looking at the largest nodule per participant (OR 1.37 [CI 0.95-1.98], p = 0.094). Similarly, there was no statistically significant association between season and screen detected cancers (OR 0.47 [CI 0.18-1.23], p = 0.123). To conclude, in this lung cancer screening population, there was no statistically significant association between influenza season and outcome of new lung nodules. Hence, we recommend new nodule management strategy is not influenced by the season in which the nodule is detected.</p

Lung cancer occurrence attributable to passive smoking among never smokers in China:a systematic review and meta-analysis

Background: Quantifying the occurrence of lung cancer due to passive smoking is a necessary step when forming public health policy. In this study, we estimated the proportion of lung cancer cases attributable to passive smoking among never smokers in China. Methods: Six databases were searched up to July 2019 for original observational studies reporting relative risks (RRs) or odds ratios (ORs) for the occurrence of lung cancer associated with passive smoking in Chinese never smokers. The population attributable fraction (PAF) was then calculated using the combined proportion of lung cancer cases exposed to passive smoking and the pooled ORs from meta-analysis. Data are reported with their 95% confidence intervals. Results: We identified 31 case-control studies of never smokers and no cohort studies. These comprised 9,614 lung cancer cases and 13,093 controls. The overall percentages of lung cancers attributable to passive smoking among never smokers were 15.5% (9.0-21.4%) for 9 population-based studies and 22.7% (16.6-28.3%) for 22 hospital-based studies. The PAFs for women were 17.9% (11.4-24.0%) for the population-based studies and 20.9% (14.7-26.7%) for the hospital-based studies. The PAF for men was only calculable for hospital-based studies, which was 29.0% (95% CI: 8.0-45.2%). Among women, the percentage of lung cancer cases attributable to household exposure (19.5%) was much higher than that due to workplace exposure (7.2%). Conclusions: We conclude that approximately 16% of lung cancer cases among never smokers in China are potentially attributable to passive smoking. This is slightly higher among women (around 18%), with most cases occurring due to household exposure

Predicted versus CT-derived total lung volume in a general population:The ImaLife study

Predicted lung volumes based on the Global Lung Function Initiative (GLI) model are used in pulmonary disease detection and monitoring. It is unknown how well the predicted lung volume corresponds with computed tomography (CT) derived total lung volume (TLV). The aim of this study was to compare the GLI-2021 model predictions of total lung capacity (TLC) with CT-derived TLV. 151 female and 139 male healthy participants (age 45-65 years) were consecutively selected from a Dutch general population cohort, the Imaging in Lifelines (ImaLife) cohort. In ImaLife, all participants underwent low-dose, inspiratory chest CT. TLV was measured by an automated analysis, and compared to predicted TLC based on the GLI-2021 model. Bland-Altman analysis was performed for analysis of systematic bias and range between limits of agreement. To further mimic the GLI-cohort all analyses were repeated in a subset of never-smokers (51% of the cohort). Mean±SD of TLV was 4.7 ±0.9 L in women and 6.2±1.2 L in men. TLC overestimated TLV, with systematic bias of 1.0 L in women and 1.6 L in men. Range between limits of agreement was 3.2 L for women and 4.2 L for men, indicating high variability. Performing the analysis with never-smokers yielded similar results. In conclusion, in a healthy cohort, predicted TLC substantially overestimates CT-derived TLV, with low precision and accuracy. In a clinical context where an accurate or precise lung volume is required, measurement of lung volume should be considered.</p

A contrast-enhanced-CT-based classification tree model for classifying malignancy of solid lung tumors in a Chinese clinical population

Background: To develop and validate a contrast-enhanced CT based classification tree model for classifying solid lung tumors in clinical patients into malignant or benign. Methods: Between January 2015 and October 2017, 827 pathologically confirmed solid lung tumors (487 malignant, 340 benign; median size, 27.0 mm, IQR 18.0-39.0 mm) from 827 patients from a dedicated Chinese cancer hospital were identified. Nodules were divided randomly into two groups, a training group (575 cases) and a testing group (252 cases). CT characteristics were collected by two radiologists, and analyzed using a classification and regression tree (CART) model. For validation, we used the decision analysis threshold to evaluate the classification performance of the CART model and radiologist's diagnosis (benign; malignant) in the testing group. Results: Three out of 19 characteristics [margin (smooth; slightly lobulated/lobulated/spiculated), and shape (round/oval; irregular), subjective enhancement (no/uniform enhancement; heterogeneous enhancement)] were automatically generated by the CART model for classifying solid lung tumors. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the CART model is 98.5%, 58.1%, 80.6%, 98.6%, 79.8%, and 90.4%, 54.7%, 82.4% 98.5%, 74.2% for the radiologist's diagnosis by using three-threshold decision analysis. Conclusions: Tumor margin and shape, and subjective tumor enhancement were the most important CT characteristics in the CART model for classifying solid lung tumors as malignant. The CART model had higher discriminatory power than radiologist's diagnosis. The CART model could help radiologists making recommendations regarding follow-up or surgery in clinical patients with a solid lung tumor

Seasonal prevalence and characteristics of low-dose CT detected lung nodules in a general Dutch population

We investigated whether presence and characteristics of lung nodules in the general population using low-dose computed tomography (LDCT) varied by season. Imaging in Lifelines (ImaLife) study participants who underwent chest LDCT-scanning between October 2018 and October 2019 were included in this sub-study. Hay fever season (summer) was defined as 1st April to 30th September and Influenza season (winter) as 1st October to 31st March. All lung nodules with volume of ≥ 30 mm3 (approximately 3 mm in diameter) were registered. In total, 2496 lung nodules were found in 1312 (38%) of the 3456 included participants (nodules per participant ranging from 1 to 21, median 1). In summer, 711 (54%) participants had 1 or more lung nodule(s) compared to 601 (46%) participants in winter (p = 0.002). Of the spherical, perifissural and left-upper-lobe nodules, relatively more were detected in winter, whereas of the polygonal-, irregular-shaped and centrally-calcified nodules, relatively more were detected in summer. Various seasonal diseases with inflammation as underlying pathophysiology may influence presence and characteristics of lung nodules. Further investigation into underlying pathophysiology using short-term LDCT follow-up could help optimize the management strategy for CT-detected lung nodules in clinical practice

Clinical characteristics and work-up of small to intermediate-sized pulmonary nodules in a Chinese dedicated cancer hospital

Objectives: To evaluate the characteristics and work-up of small to intermediate-sized pulmonary nodules in a Chinese dedicated cancer hospital. Methods: Patients with pulmonary nodules 4-25 mm in diameter detected via computed tomography (CT) in 2013 were consecutively included. The analysis was restricted to patients with a histological nodule diagnosis or a 2-year follow-up period without nodule growth confirming benign disease. Patient information was collected from hospital records. Results: Among the 314 nodules examined in 299 patients, 212 (67.5%) nodules in 206 (68.9%) patients were malignant. Compared to benign nodules, malignant nodules were larger (18.0 mm vs. 12.5 mm, P < 0.001), more often partly solid (16.0% vs. 4.7%, P < 0.001) and more often spiculated (72.2% vs. 41.2%, P < 0.001), with higher density in contrast-enhanced CT (67.0 HU vs. 57.5 HU, P = 0.015). Final diagnosis was based on surgery in 232 out of 314 (73.9%) nodules, 166 of which were identified as malignant [30 (18.1%) stage III or IV] and 66 as benign. In 36 nodules (11.5%), diagnosis was confirmed by biopsy and the remainder verified based on stability of nodule size at follow-up imaging (n = 46, 14.6%). Among 65 nodules subjected to gene (EGFR) mutation analyses, 28 (43.1%) cases (EGFR19 n = 13; EGFR21 n = 15) were identified as EGFR mutant and 37 (56.9%) as EGFR wild-type. Prior to surgery, the majority of patients [n = 194 (83.6%)] received a contrast-enhanced CT scan for staging of both malignant [n = 140 (84.3%)] and benign [n = 54 (81.8%)] nodules. Usage of positron emission tomography (PET)-CT was relatively uncommon [n = 38 (16.4%)]. Conclusions: CT-derived nodule assessment assists in diagnosis of small to intermediate- sized malignant pulmonary nodules. Currently, contrast-enhanced CT is commonly used as the sole diagnostic confirmation technique for pre-surgical staging, often resulting in surgery for late-stage disease and unnecessary surgery in cases of benign nodules

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

textabstractBackground: 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

Influenza season influence on outcome of new nodules in the NELSON study

We evaluated the impact of the influenza season on outcome of new lung nodules in a LDCT lung cancer screening trial population. NELSON-trial participants with ≥ 1 new nodule detected in screening rounds two and three were included. Outcome (resolution or persistence) of new nodules detected per season was calculated and compared. Winter (influenza season) was defined as 1st October to 31st March, and compared to the summer (hay-fever season), 1st April to 30th September. Overall, 820 new nodules were reported in 529 participants. Of the total new nodules, 482 (59%) were reported during winter. When considering the outcome of all new nodules, there was no statistically significant association between summer and resolving nodules (OR 1.07 [CI 1.00-1.15], p = 0.066), also when looking at the largest nodule per participant (OR 1.37 [CI 0.95-1.98], p = 0.094). Similarly, there was no statistically significant association between season and screen detected cancers (OR 0.47 [CI 0.18-1.23], p = 0.123). To conclude, in this lung cancer screening population, there was no statistically significant association between influenza season and outcome of new lung nodules. Hence, we recommend new nodule management strategy is not influenced by the season in which the nodule is detected