Toward automatic regional analysis of pulmonary function using inspiration and expiration thoracic CT

Murphy, Keelin; Pluim, Josien P. W.; Rikxoort, Eva M. van; Jong, Pim A. de; Hoop, Bartjan de; Gietema, Hester A.; Mets, Onno M.; Bruijne, Marleen de; Lo, Pechin; Prokop, Mathias; Ginneken, Bram van

doi:10.1118/1.3687891

Cited by 47 publications

(63 citation statements)

References 34 publications

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“…(15) is that they analyzed COPD patients while our study and the work by Yamamoto et al reports on lung cancer patients who may have airway occlusion due to the tumor as well as COPD. Another possibility to explain the lower correlation values for our study compared to Murphy et al (15) is that the relationship between 4DCT-ventilation and PFT metrics may not be linear (a saturation effects appears in Figure 2) and other mathematical relationships need to be investigated in future work. Our data can be also be compared to other validation work comparing 4DCT-ventilation imaging with other forms of ventilation imaging modalities (6,(16)(17)(18)(19)(20)(21).…”

Section: Discussionmentioning

confidence: 45%

“…In future work, our goal is to attempt to isolate the effects of the tumor from other thoracic conditions to improve the confidence in the ability of 4DCT-ventilation to predict specific thoracic conditions. Our study builds on prior work (15,18) and present several important advancements in the validation of 4DCT-ventilation. Our study of 98 lung cancer patients presents the largest study set to date to validate 4DCT-ventilation with PFT data or any other imaging modality.…”

Section: Discussionmentioning

confidence: 99%

“…One of the challenges of the proposed work was to convert 3D ventilation images into single metrics. We chose 4DCT-ventilation metrics that we deemed clinically meaningful and metrics that were previously proposed (15,18). However, the used metrics can have their shortcomings.…”

Section: Discussionmentioning

confidence: 99%

“…We derived single metrics from the 4DCT-ventilation that were intended to reflect the degree of ventilation obstruction and heterogeneity of the ventilation image. We computed the coefficient of variation (CoV) defined as the ratio of the standard deviation and the mean (20), the percentage of lung with 20% ventilation or less (V20) (15,18), and an observer based binary metric of whether a ventilation defect was present or not. Larger CoV and V20 values indicated a more heterogeneous ventilation image and consequently worsening lung function.…”

Section: Dct-ventilationmentioning

confidence: 99%

“…4DCT-ventilation has been used to assess changes in lung function throughout (8) and after (13) radiotherapy. Outside of the scope of oncology, several authors have also shown the ability of 4DCT-ventilation to detect non-oncologic lung conditions such as emphysema and chronic obstructive pulmonary disease (COPD) (12,15).…”

Section: Introductionmentioning

confidence: 99%

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Clinical Validation Of 4DCT-Ventilation With Pulmonary Function Test Data

Vinogradskiy

Schubert

Brennan

et al. 2014

International Journal of Radiation Oncology*Biology*Physics

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Section: Discussionmentioning

confidence: 45%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Dct-ventilationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Clinical Validation Of 4DCT-Ventilation With Pulmonary Function Test Data

Vinogradskiy

Schubert

Brennan

et al. 2014

International Journal of Radiation Oncology*Biology*Physics

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Clinical evaluation of pulmonary quantitative computed tomography parameters for diagnosing eosinophilic chronic obstructive pulmonary disease: Characteristics and diagnostic performance

Liu,

Lu,

Chen

et al. 2024

Health Science Reports

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AimsTo investigate the characteristics and diagnostic performance of quantitative computed tomography (QCT) parameters in eosinophilic chronic obstructive pulmonary disease (COPD) patients.MethodsHigh‐resolution CT scans of COPD patients were retrospectively analyzed, and various emphysematous parenchyma measurements, including lung volume (LC), lung mean density (LMD), lung standard deviation (LSD), full‐width half maximum (FWHM), and lung relative voxel number (LRVN) were performed. The QCT parameters were compared between eosinophilic and noneosinophilic COPD patients, using a definition of eosinophilic COPD as blood eosinophil values ≥ 300 cells·µL−1 on at least three times. Receiver operating characteristic curves and area under the curve (ROC‐AUC) and python were used to evaluate discriminative efficacy of QCT.ResultsNoneosinophilic COPD patients had a significantly lower TLMD (−846.3 ± 47.9 Hounsfield Unit [HU]) and TFWHM(162.5 ± 30.6 HU) compared to eosinophilic COPD patients (−817.8 ± 54.4, 177.3 ± 33.1 HU, respectively) (p = 0.018, 0.03, respectively). Moreover, the total LC (TLC) and TLSD were significantly lower in eosinophilic COPD group (3234.4 ± 1145.8, 183.8 ± 33.9 HU, respectively) than the noneosinophilic COPD group (5600.2 ± 1248.4, 203.5 ± 20.4 HU, respectively) (p = 0.009, 0.002, respectively). The ROC‐AUC values for TLC, TLMD, TLSD, and TFWHM were 0.91 (95% confidence interval [CI], 0.828–0.936), 0.66 (95% CI, 0.546–0.761), 0.64 (95% CI, 0.524–0.742), and 0.63 (95% CI, 0.511–0.731), respectively. When the TLC value was 4110 mL, the sensitivity was 90.7% (95% CI, 79.7–96.9), specificity was 77.8% (95% CI, 57.7–91.4) and accuracy was 86.4%. Notably, TLC demonstrated the highest discriminative efficiency with an F1 Score of 0.79, diagnostic Odds Ratio of 34.3 and Matthews Correlation Coefficient of 0.69, surpassing TLMD (0.55, 3.66, 0.25), TLSD (0.56, 3.95, 0.26), and TFWHM (0.56, 4.16, 0.33).ConclusionEosinophilic COPD patients exhibit lower levels of emphysema and a more uniform density distribution throughout the lungs compared to noneosinophilic COPD patients. Furthermore, TLC demonstrated the highest diagnostic efficiency and may serve as a valuable diagnostic marker for distinguishing between the two groups.

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Computed tomography quantification of tracheal abnormalities in COPD and their influence on airflow limitation

et al. 2017

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Purpose To present a method to automatically quantify tracheal morphology changes during breathing and investigate its contribution to airflow impairment when adding CT measures of emphysema, airway wall thickness, air trapping and ventilation. Methods Because tracheal abnormalities often occur localized, a method is presented that automatically determines the most abnormal trachea section based on automatically computed sagittal and coronal lengths. In this most abnormal section, trachea morphology is encoded using four equiangular rays from the center of the trachea and the normalized lengths of these rays are used as features in a classification scheme. Consequently, trachea measurements are used as input for classification into GOLD stages in addition to emphysema, air trapping and ventilation. A database of 200 subjects distributed across all GOLD stages is used to evaluate the classification with a k nearest neighbour algorithm. Performance is assessed in two experimental settings: a) when only inspiratory scans are taken; b) when both inspiratory and expiratory scans are available. Results Given only an inspiratory CT scan, measuring tracheal shape provides complementary information only to emphysema measurements. The best performing set in the inspiratory setting was a combination of emphysema and bronchial measurements. The best performing feature set in the inspiratory-expiratory setting includes measurements of emphysema, ventilation, air trapping, and trachea. Inspiratory and inspiratory-expiratory settings showed similar performance. Conclusions The fully automated system presented in this study provides information on trachea shape at inspiratory and expiratory CT. Addition of tracheal morphology features improves the ability of emphysema and air trapping CT-derived measurements to classify COPD patients into GOLD stages and may be relevant when investigating different aspects of COPD.

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Toward automatic regional analysis of pulmonary function using inspiration and expiration thoracic CT

Cited by 47 publications

References 34 publications

Clinical Validation Of 4DCT-Ventilation With Pulmonary Function Test Data

Clinical Validation Of 4DCT-Ventilation With Pulmonary Function Test Data

Clinical evaluation of pulmonary quantitative computed tomography parameters for diagnosing eosinophilic chronic obstructive pulmonary disease: Characteristics and diagnostic performance

Computed tomography quantification of tracheal abnormalities in COPD and their influence on airflow limitation

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