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A Model for Predicting Future FEV1 Decline in Smokers Using Hyperpolarized3 He Magnetic Resonance Imaging

Posted Feb 28, 2019 Updated Apr 10, 2023
By Ryan J. Baron BS , Hooman Hamedani MS , Stephen J. Kadlecek PhD , Ian F. Duncan PhD , Yi Xin MS , Sarmad Siddiqui BS , Mehrdad Pourfathi MS , Maurizio Cereda MD , Rahim R. Rizi PhD
14 min read

Rationale and Objectives

The purpose of this study was to assess the effectiveness of hyperpolarized helium-3 magnetic resonance (MR)-based imaging markers in predicting future forced expiratory volume in one second decline/chronic obstructive pulmonary disorder progression in smokers compared to current diagnostic techniques.

Materials and Methods

Total 60 subjects (15 nonsmokers and 45 smokers) participated in both baseline and follow-up visits (∼1.4 years apart). At both visits, subjects completed pulmonary function testing, a six-minute walk test , and the St. George Respiratory Questionnaire. Using helium-3 MR imaging, means (M) and standard deviations (H) of oxygen tension (P A O 2 ), fractional ventilation, and apparent diffusion coefficient were calculated across 12 regions of interest in the lungs. Subjects who experienced FEV1 decline >100 mL/year were deemed “decliners,” while those who did not were deemed “sustainers.” Nonimaging and imaging prediction models were generated through a logistic regression model, which utilized measurements from sustainers and decliners.

Results

The nonimaging prediction model included the St. George Respiratory Questionnaire total score, diffusing capacity of carbon monoxide by the alveolar volume (DLCO/VA), and distance walked in a six-minute walk test. A receiving operating character curve for this model yielded a sensitivity of 75% and specificity of 68% with an overall area under the curve of 65%. The imaging prediction model generated following the same methodology included ADC H , FV H , and P A O 2 H . The resulting receiving operating character curve yielded a sensitivity of 87.5%, specificity of 82.8%, and an area under the curve of 89.7%.

Conclusion

The imaging predication model generated from measurements obtained during 3 He MR imaging is better able to predict future FEV1 decline compared to one based on current clinical tests and demographics. The imaging model’s superiority appears to arise from its ability to distinguish well-circumscribed, severe disease from a more uniform distribution of moderately altered lung function, which is more closely associated with subsequent FEV1 decline.

INTRODUCTION

Chronic obstructive pulmonary disorder (COPD) is a progressive disease of the lungs in which obstructed airflow and destruction of the lung parenchyma cause disrupted and deteriorating lung function often accompanied by sputum production, dyspnea, and cough ( ). In 2015, 3.17 million deaths were attributed to COPD, accounting for nearly 5% of total global deaths ( ). COPD is typically diagnosed by assessing forced expiratory volume in one second (FEV 1 ) together with the presentation of associated symptoms; however, while tracking annual FEV 1 decline has been considered the most effective way to monitor COPD progression since the 1970s ( ), it has proven ineffective for predicting future lung function decline. In recent years, the hyperpolarized noble gases have been developed as MRI contrast agents capable of delivering images of gas distribution throughout all regions of the lung, which can be used to quantify lung function during ventilation and diffusion ( ). As a result, assessing gas distribution and heterogeneity in signal intensity throughout the lung now offers an alternative method for monitoring COPD progression with better prognostic potential. In this paper, we present a model for using 3 He MR imaging markers to predict functional decline in the lungs of smokers with high sensitivity and specificity.

To diagnose symptomatic COPD, physicians utilize GOLD criteria based on the ratio of a patient’s FEV 1 to their forced vital capacity (FVC) after bronchodilator use ( ). After initial diagnosis (FEV 1 /FVC < 70%), disease progression is tracked via changes in percent predicted FEV 1 (%FEV 1 ), and is staged from GOLD 1 (≥80% predicted) to GOLD 4 (<30% predicted). Depending on the annual rate of FEV 1 decline, clinicians can then determine whether a smoker’s lung function is declining rapidly (“decliner”) or at a rate consistent with normal aging (“sustainer”) ( ). However, because spirometric measurements cannot differentiate between decliners and sustainers until after a significant functional decline has already occurred, they represent a much better tool for confirming disease progression than for predicting future functional decline ( ).

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METHODS AND MATERIALS

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Subject Groups and Demographics

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Clinical Tests

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3 He Imaging Markers

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Gas Delivery System and Imaging Session

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Figure 1, Study design and data analysis flow chart depicting the steps from data collection to statistical analysis in this study. Data collection occurred during the MRI session, after which images were registered (adjusted so that all were uniform in size and alignment). Once the images were adjusted, the analysis of ADC, P A O 2 , and FV began with the binning of images into the 12 ROI's (binned voxels) of 500 cm 3 each. Statistical analyses could then be successfully run on this data. ADC, apparent diffusion coefficient; FV, fractional ventilation; ROI, regions of interest.

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3 He MR Imaging

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Gas Dynamic Models and Image Analysis

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Statistical Analysis

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RESULTS

Demographics and Clinical (Nonimaging) Observations

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Table 1

Demographic Information for Nonsmokers, Smokers, and Subjects in the Smokers Group with COPD

Nonsmokers (n = 15) Smokers (n = 33) Smokers with COPD (n = 12)Demographics Age 52.9 ± 5.9 49.9 ± 6.9 52.4 ± 8.2 Height (in) 67.8 ± 3.4 69.0 ± 3.1 68.6 ± 3.5 Weight (lb) 174.6 ± 29.5 183.9 ± 34.9 181.65 ± 36.9 Body mass index (kg/m 2 ) 26.7 ± 4.2 27.2 ± 4.04 27.0 ± 5.3 Smoking (Pack years) 0 ± 0 29.1 ± 10.3 38.1 ± 16.4PFTs FVC (L) 4.09 ± 0.96 4.17 ± 1.1 3.55 ± 1.0 FEV1 (L) 3.35 ± 0.78 3.20 ± 0.72 2.03 ± 0.62 FEV1/FVC (%) 81.7 ± 2.7 77.5 ± 4.5 57.3 ± 5.3 RV/TLC (%) 30.6 ± 4.5 31.1 ± 7.5 40.5 ± 8.1 DLCO (mL/min/mm Hg) 27.4 ± 5.1 25.3 ± 6.3 18.4 ± 5.3 Percentage predicted FEV1 106.1 ± 11.1 98.1 ± 15.1 65.7 ± 12.5 Percentage predicted FVC 101.7 ± 9.2 101.4 ± 18.8 90.2 ± 14.5 Percentage predicted DLCO 107.4 ± 12.5 98.2 ± 22.9 81.1 ± 21.9Clinical tests Distance walked in 6MWT (m) 551.8 ± 120.8 485.1 ± 70.0 506.3 ± 72.6 SGRQ overall score 1.91 ± 2.7 11.8 ± 12.7 27.6 ± 22.10

Values are presented as mean ± standard deviations.

COPD, chronic obstructive pulmonary disorder; FEV1, forced expiratory volume in one second; FVC, forced vital capacity; PFT, pulmonary function testing; RV, residual volume, SGRQ, St. George Respiratory Questionnaires; TLC, total lung capacity; 6MWT, 6-minute walk test.

Figure 2, Box plot representations of nonimaging markers comparing (A) smokers vs nonsmokers and (B) sustainers vs decliners. The line within each box represents the median, while the circle in the middle represents the mean. Values for FEV 1FVC, %FEV 1 , DL COVA, and RV/TLC were determined via PFT. SGRQ overall represents the total overall scores from the St. George Respiratory Questionnaires. FEV1, forced expiratory volume in one second; FVC, forced vital capacity; PFT, pulmonary function testing; RV, residual volume; TLC, total lung capacity; 6MWT, 6-minute walk test.

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Figure 3, FEV 1 drop between baseline and follow-up imaging session. The bolded black lines indicate a substantial FEV 1 decline of at least 100 mL/yr. In total, 8 subjects experienced significant FEV 1 decline between baseline and follow-up visits. FEV1, forced expiratory volume in one second.

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Table 2

( A) All Nonimaging Markers Were Assessed During Clinical Tests Prior to MR Imaging. %FEV1, DLCO/VA, RV/TLC, and FEV1/FVC were Determined via PFT. (B) Nonimaging Markers Selected for the Multivariate Logistic Regression Were Those Values That had a p Value < 0.25 From the Univariate Logistic Regression

(A) Univariate Logistic Regression of Nonimaging Markers Sustainers vs Decliners Variable_Coeff._ SE z_p_ AICAge 0.034 0.043 –1.299 0.194 68.6Height 0.197 0.112 1.764 0.078 65.6Weight –0.002 0.009 –0.234 0.815 69.1Pack years 0.064 0.023 2.793 0.005 58.76MWT –0.001 0.001 –1.148 0.251 60.3SGRQ 0.049 0.020 2.397 0.017 62.2%FEV1 –0.012 0.015 –0.779 0.436 68.6DLCO/VA –2.011 0.590 –3.410 0.001 45.9RV/TLC 0.051 0.039 1.304 0.192 67.5FEV1/FVC –0.102 0.033 –3.070 0.002 58.2

(B) Multivariate Logistic Regression of Nonimaging Markers Sustainers vs Decliners Variable_Coeff._ SE z_p_ AICSGRQ 0.076 0.037 2.055 0.039DLCO/VA –2.039 0.705 –2.893 0.004 40.86MWT 0.002 0.002 1.118 0.263

FEV1, forced expiratory volume in one second; FVC, forced vital capacity; PFT, pulmonary function testing; RV, residual volume, SGRQ, St. George Respiratory Questionnaires; TLC, total lung capacity; 6MWT, 6-minute walk test.

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Imaging Studies

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Table 3

( A) All Imaging Markers Were Generated During HP 3 He MR Imaging. (B ) The Values that Entered the Multivariate Logistic Regression were Those Values that had a p Value < 0.25 From the Univariate Logistic Regression

(A) Univariate Logistic Regression of Imaging Markers Sustainers vs Decliners Variable_Coeff._ SE z_p_ AICADC M 0.832 0.865 1.181 0.236 75.0ADC H –1.964 0.724 –2.711 0.007 63.6P A O 2 M –0.915 0.554 –1.652 0.099 73.3P A O 2 H –2.903 1.237 –2.348 0.019 61.2FV M –1.576 0.757 –2.083 0.037 71.4FV H 0.131 0.411 0.320 0.749 76.6

(B) Multivariate Logistic Regression of Imaging Markers Sustainers vs Decliners Variable_Coeff._ SE z_p_ AICADC H –3.668 2.290 –1.602 0.109P A O 2 H –3.113 1.864 –1.671 0.095 55.0FV H 1.446 1.348 1.072 0.284

ADC, apparent diffusion coefficient; FV, fractional ventilation; MR, magnetic resonance; ROI, regions of interest.

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Figure 4, ROC curve generated for the results of the multivariate logistic regression, which entered the nonimaging prediction model for future FEV1 decline between sustainers and decliners. The three nonimaging markers that entered this model were SGRQ total score, DLCO/VA, and 6MWT. At the operating point in this model sensitivity = 75.0% and specificity = 68.3%. FEV1, forced expiratory volume in one second; ROC, receiving operating character; SGRQ, St. George Respiratory Questionnaires; 6MWT, 6-minute walk test.

Figure 5, Complete regional lung function assessment consisting of ventilation (FV), alveolar oxygen tension (P A O 2 ), and ADC in a representative subject. Images were acquired from one asymptomatic smoker (FEV 1FVC = 72; DL CO = 30.79, %FEV 1 = 93). ADC, apparent diffusion coefficient; FV, fractional ventilation; FEV1, forced expiratory volume in one second; FVC, forced vital capacity. (Color version of figure is available online.)

Figure 6, Boxplots of the imaging markers comparing (A) smokers vs nonsmokers and (B) sustainers vs decliners. The line within each box represents the median, while the circle in the middle represents the mean. Each point on the boxplots represents the specified value calculated from one of the 12 ROI's from each slice of MR lung images. All imaging markers were generated during HP 3 He MR imaging sessions. H, heterogeneity (standard deviation); HP, hyperpolarized; M, mean; MR, magnetic resonance; ROI, regions of interest.

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DISCUSSION

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Figure 7, ROC curves of the regional measures (A) ADC H (Sensitivity = 82.4, Specificity = 39.6), (B) P A O 2 H (Sensitivity = 64.0, Specificity = 96.9), (C) FV H (Sensitivity = 60.7, Specificity = 90.8). These three parameters entered into the imaging prediction model of future FEV1 decline used to differentiate sustainers from decliners. (D) ROC curve of a multifaceted measure of ADC H , P A O 2 H , and FV H in the imaging prediction model (Sensitivity = 82.9, Specificity = 80.8). ADC, apparent diffusion coefficient; FV, fractional ventilation; FEV1, forced expiratory volume in one second; ROC, receiving operating character.

Figure 8, ROC curve of the predictive imaging model, with the optimal cut-off for differentiating sustainers from decliners (solid block line), and the non-imaging model (red line). At its operating point, the imaging prediction model has a sensitivity of 87.5% and a specificity of 82.8%. This model also possesses an AUC of 89.7%. AUC, area under the curve; ROC, receiving operating character. (Color version of figure is available online.)

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Acknowledgment

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Academic Radiology, Volume 26, Issue 3
Journals General Radiology
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