radiologic Features of small Pulmonary nodules and lung cancer risk ...

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Purpose:

To extract radiologic features from small pulmonary nodules (SPNs) that did not meet the original criteria for a positive screening test and identify features associated with lung cancer risk by using data and images from the National Lung Screening Trial (NLST).

Materials and Methods:

Radiologic features in SPNs in baseline low-dose computed tomography (CT) screening studies that did not meet NLST criteria to be considered a positive screening examination were extracted. SPNs were identified for 73 incident case patients who were given a diagnosis of lung cancer at either the first or second follow-up screening study and for 157 control subjects who had undergone three consecutive negative screening studies. Multivariable logistic regression was used to assess the association between radiologic features and lung cancer risk. All statistical tests were two sided.

From the Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; and Tianjin’s Clinical Research Center for Cancer, Tianjin, China (Y.L., H.W., Q.L., Z.Y.); and Departments of Cancer Imaging and Metabolism (Y.L., Q.L., Y.B., A.L.G., R.J.G.), Diagnostic Imaging and Interventional Radiology (M.J.M.), Biostatistics and Bioinformatics (Z.J.T.), Cancer Epidemiology (J.J.H., M.B.S.), and Thoracic Oncology (M.B.S.), H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, MRC-CANCONT, Tampa, FL 33612. Received July 6, 2016; revision requested September 6; revision received March 21, 2017; accepted April 3; final version accepted June 8. Address correspondence to M.B.S. (e-mail: [email protected]).

Results:

Nine features were significantly different between case patients and control subjects. Backward elimination followed by bootstrap resampling identified a reduced model of highly informative radiologic features with an area under the receiver operating characteristic curve of 0.932 (95% confidence interval [CI]: 0.88, 0.96), a specificity of 92.38% (95% CI: 52.22%, 84.91%), and a sensitivity of 76.55% (95% CI: 87.50%, 95.35%) that included total emphysema score (odds ratio [OR] = 1.71; 95% CI: 1.39, 2.01), attachment to vessel (OR = 2.41; 95% CI: 0.99, 5.81), nodule location (OR = 3.25; 95% CI: 1.09, 8.55), border definition (OR = 7.56; 95% CI: 1.89, 30.8), and concavity (OR = 2.58; 95% CI: 0.89, 5.64).

Funding support came from a National Cancer Institute (NCI) Quantitative Imagine Network Grant (U01-CA143062 to R.J.G. and M.B.S.), from an NCI Early Detection Research Network Grant (U01-CA200464 to J.J.H., R.J.G., and M.B.S.), and an NCI Grant (U01-CA186145 subcontract to M.B.S. and R.J.G.). This work has also been supported in part by a Cancer Center Support Grant at the H. Lee Moffitt Cancer Center and Research Institute, an NCI-designated Comprehensive Cancer Center (P30-CA76292). R.J.G. and M.B.S. supported by James and Esther King Biomedical Research Program (2KT01).

Conclusion:

A set of clinically relevant radiologic features were identified that that can be easily scored in the clinical setting and may be of use to determine lung cancer risk among participants with SPNs.

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Online supplemental material is available for this article.

The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by the NCI. RSNA, 2017

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Radiology: Volume 000: Number 0—   2018 n radiology.rsna.org

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Imaging

Ying Liu, MD Hua Wang, MD Qian Li, MD Melissa J. McGettigan, MD Yoganand Balagurunathan, PhD Alberto L. Garcia, BS Zachary J. Thompson, PhD John J. Heine, PhD Zhaoxiang Ye, MD Robert J. Gillies, PhD Matthew B. Schabath, PhD

Original Research n Thoracic

Radiologic Features of Small Pulmonary Nodules and Lung Cancer Risk in the National Lung Screening Trial: A Nested CaseControl Study1

THORACIC IMAGING: Radiologic Features of Small Pulmonary Nodules and Lung Cancer Risk

O

n the basis of the National Lung Screening Trial (NLST) protocol, lung cancer screening results were dichotomous as a positive screening or a negative screening. A positive screening examination was defined as one that showed a noncalcified nodule 4 mm or larger in the axial plane. Recently, the National Comprehensive Cancer Network guidelines on lung cancer screening increased the recommended nodule size threshold for a positive screening to 6 mm in diameter (1). Increasing the size threshold for a positive screening reduces false-positive results and diagnostic work-up but may result in increasing the proportion of lung cancer diagnoses being delayed or missed (2), especially for smaller pulmonary nodules. Conversely, a negative screening examination was defined as one that showed no abnormalities or only small pulmonary nodules (SPNs) that were not suspicious for lung cancer. Although SPNs are often benign (3) and have a low incidence of lung cancer development, they still present an important clinical problem because they are less amenable to characterization with positron emission tomography or percutaneous biopsy. Thus, SPNs are commonly managed with surveillance and follow-up imaging (4,5). However, prior analyses have shown that NLST participants who had baseline negative screening examinations that included SPNs and who were given a diagnosis of incident lung cancer in follow-up

Advances in Knowledge nn The final model of the five most informative radiologic features, including total emphysema score, attachment to vessel, nodule location, border definition, and concavity, yielded an area under the receiver operating characteristic curve (AUC) of 0.928, a specificity of 92.36%, and a sensitivity of 75.34%. nn When we adjusted the model for demographic covariates, the final model yielded an AUC of 0.930, a specificity of 92.36%, and a sensitivity of 73.97%. 2

Liu et al

screening have poorer survival and higher lung cancer mortality rates compared with NLST participants who had baseline positive screenings and then were diagnosed with incident lung cancer (6,7). The observation that incident lung cancers arising from negative screenings are associated with poorer outcomes could be attributed to faster-growing, more aggressive cancers that arose from a lung environment previously lacking focal abnormalities. Hence, lung cancer screening participants with SPNs may represent a more vulnerable population because of the possibility of developing a highly aggressive tumor, and imaging-based biomarkers could assist in identifying high-risk participants and biologically aggressive nodules. Using data and images from the NLST, we extracted radiologic features from SPNs that did not meet the original criteria for the screening examination to be considered a positive screening, and we identified features associated with lung cancer risk.

Materials and Methods NLST Study Population and Screening Results This research was approved by the University of South Florida Institutional Review Board. Details of the NLST study design and methods for classifying lung nodules have been published previously (8–10). Briefly, participants were randomly assigned to undergo three annual screening examinations with either low-dose computed tomography (CT) (n = 26 722) or chest radiography (n = 26 732) (8–10). Because it is not feasible to perform radiologic feature Implications for Patient Care nn The features we identified can be easily scored in the clinical setting to determine which patients with small pulmonary nodules are at an increased risk for lung cancer. nn These features also enable potential diagnostic discrimination.

extraction on the entire CT arm of the NLST, we focused on a subset of 125 patients with screening-detected lung cancer who had baseline negative screenings and 250 control subjects who had three consecutive negative screenings. The nested study design with frequency matching minimized the influence of confounders between the case patients and the control subjects. Hence, any radiologic features that differentiate case patients and control subjects were not likely to be attributed to external risk factors. Although the NLST parent study was performed to determine whether screening with low-dose CT could reduce mortality from lung cancer, in this article, we report on whether imagingbased biomarkers could identify highrisk participants and biologically aggressive SPNs. There were no statistically significant differences between case patients and control subjects according to age, sex, race, smoking status, number of pack-years smoked, and family history of lung cancer (Table 1). Additionally, using the Student t test, we found no statistically significant difference in mean age between case

https://doi.org/10.1148/radiol.2017161458 Content codes: Radiology 2018; 000:1–9 Abbreviations: AUC = area under the receiver operating characteristic curve CI = confidence interval COPD = chronic obstructive pulmonary disease NLST = National Lung Screening Trial OR = odds ratio SPN = small pulmonary nodule Author contributions: Guarantors of integrity of entire study, J.J.H., R.J.G., M.B.S.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; manuscript final version approval, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, Y.L., J.J.H., Z.Y., M.B.S.; clinical studies, Y.L., H.W., Q.L., J.J.H., R.J.G.; experimental studies, Y.B., J.J.H.; statistical analysis, Y.B., A.L.G., Z.J.T., J.J.H., R.J.G., M.B.S.; and manuscript editing, Y.L., H.W., Q.L., M.J.M., Y.B., Z.J.T., J.J.H., Z.Y., R.J.G., M.B.S. Conflicts of interest are listed at the end of this article.

radiology.rsna.org n Radiology: Volume 000: Number 0—   2018


Table 1 Demographic Characteristics and Baseline Radiologic Features according to CaseControl Status Characteristic Demographic characteristics Age (y)† By sex Female Male Sex Female Male Race White Black or other Smoking status Former Current No. of pack-years smoked‡ Self-reported history of COPD No Yes Family history of lung cancer No Yes Stage I II III IV Histologic finding Adenocarcinoma Squamous cell carcinoma Other and NOS NSCLC Small-cell carcinoma Radiologic features Nodule location RUL RML RLL LUL LLL Longest axial diameter (mm) Mean‡ Categoric ,4.5 mm 4.5 mm Longest perpendicular diameter (mm) Mean‡ Categoric ,2.9 mm 2.9 mm

Control Subjects with Negative Screening Results (n = 157)

Patients with Lung Cancer (n = 73)

62.9 (55–74)

63.9 (55–74)

.16

62.3 (55–69) 63.3 (55–74)

64.1 (56–72) 63.9 (55–74)

.08 .52

52 (33.1) 105 (66.9)

27 (37.0) 46 (63.0)

.66

143 (91.1) 14 (8.9)

66 (90.4) 7 (9.6)

.99

62 (39.5) 95 (60.5) 66.6 6 22.9

30 (41.1) 43 (58.9) 66.5 6 25.8

.89 .79

154 (98.1) 3 (1.9)

62 (84.9) 11 (15.1)

,.001

124 (79.0) 33 (21.0)

55 (75.3) 18 (24.7)

.61

… … … …

49 (67.1) 9 (12.3) 10 (13.7) 5 (6.8)

… … … …

… … …

33 (45.2) 21 (28.8) 16 (21.9) 3 (4.1)

… … …

P Value*

47 (29.9) 24 (15.3) 38 (24.2) 22 (14.0) 26 (16.6)

35 (47.9) 3 (4.1) 10 (13.7) 16 (21.9) 9 (12.3)

.004

4.84 6 2.15

5.98 6 4.50

.009

78 (49.7) 79 (50.3)

33 (45.2) 40 (54.8)

.57

3.33 6 1.42

3.78 6 2.78

.11

74 (47.1) 83 (52.9)

39 (53.4) 34 (46.6)

.39

(Table 1 continues)


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patients and control subjects when stratifying them among men (P = .52) and among women (P = .08). Selfreported COPD was significantly higher among case patients than among control subjects (15.1% vs 1.9%, P , .001). The methods for classifiying and reporting positive lung nodules identified in the NLST have been described in detail elsewhere (8–10). Briefly, eligible participants were between 55 and 74 years of age at the time of randomization, had a history of cigarette smoking of at least 30 pack-years, and were former smokers who had quit within the previous 15 years. Persons who had previously received a diagnosis of lung cancer, who had undergone chest CT within 18 months before enrollment, who had hemoptysis, or who had an unexplained weight loss of more than 6.8 kg (15 lb) in the preceding year were excluded. The NLST protocol defined a negative screening as a low-dose CT study showing no abnormalities, minor abnormalities not suspicious for lung cancer, or clinically significant abnormalities not suspicious for lung cancer. Hence, SPNs measuring less than 4 mm were considered to result in a negative screening according to the NLST protocol. These SPNs identified at the baseline screening were the basis of analysis in this study. We performed a re-review of baseline negative low-dose CT screening studies to extract radiologic features from SPNs that did not meet the original criteria to result in a positive screening. The NLST protocol defined a positive screening result as one or more noncalcified nodules or masses measuring 4 mm or larger in axial diameter or, less commonly, other abnormalities such as adenopathy or pleural effusion. Positive screenings were defined in the setting of abnormalities on baseline screenings or abnormalities on incidence screenings that were new, stable, or that evolved, the latter demonstrated by an increase in nodule size, consistency, or other characteristic potentially related to lung cancer. Participants with positive screening results received follow-up 3


Table 1 (continued) Demographic Characteristics and Baseline Radiologic Features according to CaseControl Status Characteristic Fissure attachment 0 1 Pleural attachment 0 1 Shape 1 2 Lobulation 0 1 Concavity 0 1 Border definition 1 2 Spiculation 0 1 Attenuation pattern 1 (GGO) 2 (part solid) 3 (solid) Air bronchogram 0 1 Bubblelike lucency/cavitation 0 1 Calcification 0 1 Attachment to vessel 0 1 Pleural retraction 0 1 Perinodule fibrosis 1 (none/slight) 2 (moderate) 3 (severe) Perinodule emphysema 0 (normal) 1 (,25% affected) 2 (,50% affected) 3 (,75% affected) 4 (75% affected)



P Value*

129 (82.2) 28 (17.8)

70 (95.9) 3 (4.1)

.003

140 (89.2) 17 (10.8)

62 (84.9) 11 (15.1)

.39

98 (62.4) 59 (37.6)

40 (54.8) 33 (45.2)

.31

142 (90.5) 15 (9.5)

59 (80.8) 14 (19.2)

.054

135 (85.9) 22 (14.0)

43 (59.9) 30 (41.1)

,.001

72 (45.9) 85 (54.1)

11 (15.1) 62 (84.9)

,.001

151 (96.2) 6 (3.8)

55 (75.3) 18 (24.7)

,.001

19 (12.1) 89 (56.7) 49 (31.2)

6 (8.2) 47 (64.4) 20 (27.4)

.52

156 (99.4) 1 (0.6)

73 (100.0) 0

.99

155 (98.7) 2 (1.3)

71 (97.3) 2 (2.7)

.59

155 (98.7) 2 (1.3)

71 (97.3) 2 (2.7)

.59

114 (72.6) 43 (27.4)

35 (47.9) 38 (52.1)

,.001

153 (97.5) 4 (2.6)

67 (91.8) 6 (8.2)

.057

57 (36.3) 64 (40.8) 36 (22.9)

34 (46.6) 20 (27.4) 19 (26.0)

.14

38 (24.2) 102 (64.9) 12 (7.6) 4 (2.6) 1 (0.6)

2 (2.7) 27 (40.0) 29 (39.7) 9 (12.3) 6 (8.2)

,.001

(Table 1 continues)

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recommendations; trial-wide guidelines for the management of positive screenings were developed, but were not mandated by protocol.

Nested Case-Control Study Design Figure 1 shows the schema used for our nested case-control study design. All participants had a baseline negative screening, and radiologic features were extracted from SPNs at the baseline screening. Incident case patients were given a diagnosis of lung cancer at either the first or second follow-up screening examination and were combined to form the lung cancer case patients. Because there were 14 716 participants who had three consecutive negative screenings, we used a nested study design to identify 250 control subjects who were frequency matched (2:1) to case patients in terms of age (65 years), sex, race, and smoking status. We found SPNs on the baseline screening study in 73 case patients and in 157 control subjects. Radiologic Features The radiologic features were extracted by a clinical radiologist (Y.L., with . 6 years of experience) who was blinded to case-control status. Table E1 (online) contains the radiologic features that were measured in the baseline studies of case patients and control subjects. On the basis of the publicly available data from the NLST (11), radiologic information for SPNs was obtained for anatomic location (lobe), longest axial and perpendicular diameters, margin characteristics, and attenuation. For the case patients with lung cancer, the positive nodule that was found to be cancer was not specified; however, the lung lobe in which the cancer was found was annotated. It was therefore presumed that the cancer manifested in the largest documented SPN from the same lobe as in the baseline study (12). For those screening studies that showed multiple abnormalities in the same lobe, the largest abnormality was chosen for analysis. Thin-section CT images were displayed by using both mediastinal (width, 350 HU; level, 40 HU) and lung (width, radiology.rsna.org n Radiology: Volume 000: Number 0—   2018


Table 1 (continued) Demographic Characteristics and Baseline Radiologic Features according to CaseControl Status Characteristic


Total emphysema score (range, from 0 to 24) Mean‡ Categoric 1 2–3 4–5  6


P Value*

3.4 6 2.9

9.7 6 4.4

,.001

43 (27.4) 54 (34.4) 33 (21.0) 27 (17.2)

0 1 (1.4) 7 (6.6) 65 (89.0)

,.001

Note.—Unless otherwise specified, data are numbers of patients, with percentages in parentheses. COPD = chronic obstructive pulmonary disease, GGO = ground-glass opacity, LLL = left lower lung, LUL = left upper lung, NSCLC = non–small-cell lung cancer, NOS = not otherwise specified, RLL = right lower lung, RML = right middle lung, RUL = right upper lung. * P values were calculated with the Fisher exact test for categoric variables and the Student t test for continuous variables. †

Data are means, with ranges in parentheses.

‡

Data are means 6 standard deviations.

1500 HU; level, 2600 HU) window settings. Bidimensional measurements (to the nearest millimeter) of the SPNs were recorded. In terms of morphologic characteristics, the presence or absence of fissure attachment (defined as a tumor that attaches to the fissure; the tumor’s margin is obscured by the fissure), pleural attachment (defined as a tumor that attaches to the pleura other than the fissure; the tumor’s margin is obscured by the pleura), lobulation, concavity, air bronchogram, calcification, attachment to vessel, and pleural retraction were assessed. We also evaluated the following features, which have been defined elsewhere: location of the abnormality, dominant attenuation pattern, shape, border definition, perinodule emphysema, total emphysema, perinodule fibrosis (13), spiculation (14,15), bubblelike lucency (14), cavitation (16), and presence of emphysema (17,18). For each patient, CT images were evaluated at three levels (the top of the aortic arch, the tracheal carina, and 2 cm above the highest hemidiaphragm); right and left images were assessed separately. Each image was classified as normal (score, 0), as less than 25% affected (score, 1), as less than 50% affected (score, 2), as less than 75% affected (score, 3), or as 75% or

more affected (score, 4) on the basis of the percentage of emphysematous area (19,20). Therefore, there were six images for each patient, and the total score ranged from 0 to 24. Also, we evaluated perinodule emphysema on the section that depicted the largest diameter of the nodule. The presence of pulmonary fibrosis at low-dose CT was defined as reticular opacities with peripheral and basal predominance, honeycombing, architectural distortion, and/or traction bronchiectasis or bronchiolectasis; focal ground-glass opacities and/or areas of alveolar condensation may be associated, but should not be prominent (17,18). Several radiologic features were significantly different (P , .01) between case patients and control subjects (Table 1), including nodule location, longest axial diameter, fissure attachment, concavity, border definition, spiculation, attachment to vessel, perinodule emphysema, and total emphysema. Pleural retraction (P = .057) and lobulation (P = .054) were borderline significantly different. We performed two interobserver studies to determine if radiologic features in the study could be consistently scored across different radiologists. Table E2 (online) contains the results from one of two interobserver


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agreement studies (Y.L., with > 6 years of experience, and Q.L., with > 3 years of experience) in which two radiologists performed “overreads” for 25 features in 40 participants from the NLST (20 patients with lung cancer and 20 control subjects with nodules). The patients with lung cancer and the nodulepositive control subjects had positive screening results from T0 to T2 (longest axial diameter length, 2–37 mm), but the case patients were given a diagnosis of lung cancer at T2, while the control subjects had three consecutive positive screening results but were not given a diagnosis of lung cancer. In the second interobserver agreement study, a different radiologist (M.J.M., with > 4 years of experience) performed an overread of data in a randomly selected subset of 20 patients with lung cancer and 20 control subjects from this study (Table E3 [online]). Unfortunately, the k statistic could be not reliably calculated, given the low prevalence for the features in the subset. For example, in the subset, only four participants had lobulation, two had pleural retraction, and none had calcification. Hence, we report observed agreement.

Statistical Analyses Statistical analyses were performed by using Intercooled Stata/MP 14.1 (Stata, College Station, Tex) and R Project for Statistical Computing, version 2.13.1 (http://www.r-project. org/). The Fisher exact test was used to test the differences between the case patients and control subjects for categoric covariates, and the Student t test was used to test differences for continuous covariates. Logistic regression was used to generate odds ratios (ORs) and 95% confidence intervals (CIs), and post-estimation was used to compute performance statistics. To generate a parsimonious multivariable model, a backward-elimination process was utilized, with .05 as the prespecified P value for removal from the model. Bootstrap resampling of the two final models (ie, without demographic covariates and with demographic covariates) was performed at 50003 for internal validation (21,22). 5


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Figure 1

Figure 1: Schema of the nested case-control study. All participants in this analysis had a baseline negative screening based on the NLST protocol. Incident case patients (red boxes) were given a diagnosis of lung cancer at either the first follow-up screening examination (n = 62) or the second follow-up screening examination (n = 63) and were combined to form the case group (n = 125). Control subjects were defined as participants who had three consecutive negative screenings (green box). A 2:1 nested study design was used to identify 250 control subjects who were frequency matched to the case patients for age, sex, race, and smoking status. In the 125 patients with incident lung cancers and 250 control subjects, radiologic SPNs were detected on the baseline screen for 73 case patients and 157 control subjects, which was the final sample size for analysis.

We also explored identifying an optimal cut point that maximized the threshold for sensitivity or specificity (23). All statistical tests were two sided, and P , .05 was considered to indicate a statistically significant difference.

Results Backward elimination was used to reduce the number of radiologic features to a parsimonious model containing the most informative features associated with lung cancer risk (Table 2 ). Radiologic features that were significantly different in Table 1 were considered for inclusion, as well as pleural retraction and lobulation because they approached significance. The final model (Table 2, Fig 2) yielded an AUC of 0.928 (95% CI: 0.893, 0.963), a specificity of 92.36% (95% CI: 87.03%, 95.99%), and a sensitivity of 75.34% (95% CI: 63.86%, 84.68%) and included total emphysema score (OR = 1.65; 95% CI: 1.42, 1.95), attachment to vessel (OR = 2.35; 95% CI: 1.04, 5.35), nodule location (OR = 3.09; 95% CI: 1.11, 8.54), border 6

definition (OR = 6.59; 95% CI: 2.19, 19.79), and concavity (OR = 2.44; 95% CI: 1.01, 5.89). After bootstrap resampling for internal validation, the ORs and performance statistics did not substantially change. To account for potential residual confounding, we included a model with these features adjusting for age, sex, race, smoking status, number of pack-years smoked, and family history of lung cancer. Selfreported COPD was not included because the total emphysema imaging feature was included. The point estimates for the radiologic features and performance statistics were appreciably similar to the model with features only (Table 2).

Discussion In this article we focused on a unique subset of case patients and control subjects who had baseline negative screenings in the low-dose CT arm of the NLST and assessed the association between radiologic features of SPNs and lung cancer risk. Using this approach, we identified an internally

validated final model of five informative radiologic features yielding an elevated AUC, suggesting a high probability of discriminating screening participants with SPNs who are at an increased risk of lung cancer. Although the participants in our analysis were originally given a negative screening result in the NLST, we did find that 50.3% of the control subjects and 54.8% of the case patients had nodules 4.5 mm or larger in the longest axial diameter. However, in the NLST, negative screenings did include minor or significant abnormalities not suspicious for lung cancer (9,10). We focused on NLST participants who had baseline negative screenings because we and others (6,7) have shown that screening-detected incident cancers that had baseline (prevalence) negative screenings conferred significantly poorer overall survival and progressionfree survival and higher death rates compared with screening-detected incident cancers that had baseline positive screenings. Specifically, we found that NLST participants who had baseline negative screenings that included SPNs



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Table 2 Multivariable Models for the Association between Radiologic Features and Lung Cancer Risk Radiologic Features Only Covariate Total emphysema score Attachment to vessel 0 1 Nodule location RUL, RML, RLL, LLL LUL Border definition 0 1 Concavity 0 1 Performance statistics based on 50% cut point AUC Specificity (%) Sensitivity (%) Positive predictive value (%) Performance statistics based on optimal cut point† Optimal cut point (%) AUC Specificity (%) Sensitivity (%) Positive predictive value (%)

mOR

Bootstrap mOR

Radiologic Features Adjusted for Demographic Factors* Bootstrap P Value

mOR

Bootstrap mOR

Bootstrap P Value

1.65 (1.42, 1.95)

1.71 (1.39, 2.01)

.001

1.66 (1.42, 1.94)

1.79 (1.38, 1.92)

.003

1.00 (Reference) 2.35 (1.04, 5.35)

1.00 (Reference) 2.41 (0.99, 5.81)

.056

1.00 (Reference) 2.57 (1.09, 6.03)

1.00 (Reference) 2.88 (0.86, 6.60)

.071

1.00 (Reference) 3.09 (1.11, 8.54)

1.00 (Reference) 3.25 (1.09, 8.55)

.033

1.00 (Reference) 3.35 (1.14, 9.77)

1.00 (Reference) 3.94 (0.84, 10.38)

.061

1.00 (Reference) 6.59 (2.19, 19.79)

1.00 (Reference) 7.56 (1.89, 30.8)

.018

1.00 (Reference) 8.49 (2.66, 27.19)

1.00 (Reference) 11.35 (1.57, 44.2)

.027

1.00 (Reference) 2.44 (1.01, 5.89)

1.00 (Reference) 2.58 (0.89, 5.64)

.057

1.00 (Reference) 2.56 (1.01, 6.57)

1.00 (Reference) 2.82 (0.79, 6.54)

.077

0.928 (0.893, 0.963) 92.36 (87.03, 95.99) 75.34 (63.86, 84.68) 82.09

0.932 (0.88, 0.96) 92.38 (55.22, 84.91) 76.55 (87.50, 95.35) 82.38

0.930 (0.895, 0.965) 92.36% (87.03, 95.98) 73.97% (62.37, 83.55) 81.41

0.940 (0.870, 0.950) 92.7 (86.76, 95.51) 78.0 (52.14, 82.05) 83.28

36.5 0.928 (0.893, 0.963) 89.17 (82.97, 93.38) 84.93 (74.21, 91.88) 78.48

34.6 (27.0, 53.0) 0.932 (0.880, 0.960) 86.89 (80.52, 96.42) 90.13 (70.27, 89.16) 76.62

29.4 0.930 (0.895, 0.965) 84.71 (77.91, 89.77) 91.78 (82.35, 96.61) 73.62

34.7 (14.0, 37.0) 0.940 (0.870, 0.950) 88.41 (72.90, 88.27) 90.16 (85.53, 98.36) 78.53

Note.—Data in parentheses are 95% CIs. AUC = area under the receiver operating characteristic curve, LLL = left lower lung, LUL = left upper lung, mOR = multivariable OR, RLL = right lower lung, RML = right middle lung, RUL = right upper lung. * The patient covariates included age, sex, race, smoking status, number of pack-years smoked, and family history of lung cancer. Self-reported COPD was not included because the total emphysema imaging feature was included. †

We identified the optimal cut point that maximized the threshold for sensitivity or specificity.

and who were given a diagnosis of incident lung cancer at follow-up screening exhibited a 5-year survival rate of 47.6% and a lung cancer–specific death rate of 136.6 per 1000 person-years (95% CI: 102.6, 175.4), compared with a 5-year survival rate of 65.7% and a lung cancer–specific death rate of 71.3 per 1000 person-years (95% CI: 53.4, 91.8) for participants who had baseline positive screenigns and then were given a diagnosis of incident lung cancer (7). As with interval cancers diagnosed following a negative screening, lung tumors that arise in a lung environment ostensibly free of lung nodules are likely more rapidly growing and aggressive, which results in the observed

significantly poorer outcomes (6,7). Although similar findings were also reported by Patz et al (6), the authors suggested that annual screening after a negative screening might be unnecessary because lung cancer incidence is lower among NLST participants with a baseline negative screening than among those with a baseline positive screening. Ideally, though, noninvasive imaging-based biomarkers, as presented in this analysis, should be used to assist in identifying high-risk participants and biologically aggressive nodules (24,25). The features in the final model that were significantly associated with lung cancer risk are biologically and clinically relevant. Pulmonary emphysema is the


morphologic enlargement of airspaces distal to the terminal bronchiole from dilatation or destruction of alveolar walls. In results consistent with our findings, a systematic review and meta-analysis (26) revealed that emphysema detected visually at CT is associated with significantly increased odds of lung cancer, independent of smoking history and airflow obstruction. At present, to our knowledge, there are no published data assessing vessel-attached nodules for lung cancer risk; however, vessel-attached nodules may be more vascular and angiogenic, which are key factors in cancer growth by supplying a nodule with required nutrients. Also, extent of vascularity differs between benign and malignant pulmonary nodules 7


Liu et al

Figure 2

Figure 2: Graph shows AUCs for multivariable models containing demographic variables only (red line), imaging features only (green line), and imaging features and demographic variables together (blue line). The model containing only demographic variables included age, sex, race, smoking status, number of packyears smoked, and family history of lung cancer. The model containing only imaging features included total emphysema, attachment to vessel, nodule location, border definition, and concavity. The model containing both demographics and imaging features included all covariates from the demographic variables–only model and the imaging features–only model.

(27). Our finding for nodule location is consistent with the natural history of lung cancer, as primary malignant nodules are commonly located in the upper lobes (28,29). Our analysis revealed that 69.8% of the case patients had SPNs in the upper lobes at the baseline screening, compared with 43.9% of the control subjects. A poorly defined nodule border conferred a 6.6-fold increased risk of lung cancer, and previous data have shown that malignant nodules often manifest with irregular, spiculated, and ill-defined margins, while benign nodules have a well-defined smooth edge (30). Finally, because tumor nodules appear concave and mimic benign nodules when the surrounding parenchyma is emphysematous (23,31,32), SPNs likely have a greater probability of malignancy in the presence of emphysema. This could be especially important because COPD phenotypes are highly prevalent in lung cancer screening populations (33). 8

The NLST demonstrated a clear benefit of low-dose CT screening in reducing lung cancer and all-cause mortality (9); this post-hoc analysis revealed novel and valuable knowledge about the characteristics of radiologic features associated with SPNs and lung cancer risk. We acknowledge that there were some limitations in this study. The generalizability of our results to screening populations outside the NLST eligibility criteria is indeterminate (10). Certainly, CT scanning parameters were not consistent for all NLST participants (eg, peak kilovoltage, tube current–time product, scanner manufacturer, section thickness), which can be problematic for some radiomic quantitative features; however, this is less of a concern for radiologic features such as those described herein (24,34). Because the nodule that was found to be cancer was not specified in the publicly available NLST data, we utilized two research

radiologists to come to a consensus and identify the potential nodule of interest. Moreover, the radiologists were blinded to the case-control status so as not to introduce potential differential biases. We noted rather high agreement in the first interobserver study, which included NLST participants who had a baseline positive screening; however, the second interobserver study, which included data in a subset of case patients and control subjects from this study, yielded wide variability in interobserver agreement, which is likely attributable to the difficulty in scoring smaller radiologic nodules. Hence, a detailed radiology feature atlas and crosstraining are recommended to assure accurate feature extraction within and across institutions. Next, although we had a limited number of case patients, the 2:1 study design increases statistical power (35). Although we performed bootstrapping for internal validation of our final models, we acknowledge that the bootstrap point estimates could be biased because bootstrapping of the backward-elimination approach was not performed. Despite these limitations, we have identified a set of informative and relevant radiologic features that can be easily scored in the clinical setting to determine lung cancer risk among participants with SPNs. Given the relatively short duration between the baseline and follow-up screenings, these features have potential diagnostic discrimination as well. The clinical utility of our findings needs to be replicated in other screening cohorts and prospectively. Acknowledgments: The authors thank the National Cancer Institute (NCI) for access to the NCI data collected by the NLST. Disclosures of Conflicts of Interest: Y.L. disclosed no relevant relationships. H.W. disclosed no relevant relationships. Q.L. disclosed no relevant relationships. M.J.M. disclosed no relevant relationships. Y.B. disclosed no relevant relationships. A.L.G. disclosed no relevant relationships. Z.J.T. disclosed no relevant relationships. J.J.H. disclosed no relevant relationships. Z.Y. disclosed no relevant relationships. R.J.G. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: is a member of the Advisory Board of HealthMyne. Other relationships: disclosed no relevant relationships. M.B.S. disclosed no relevant relationships.



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