Prediction of pulmonary function decline in fibrous interstitial lung abnormalities based on quantitative chest CT parameters
Abstract Background Interstitial lung abnormalities (ILA) are a proposed imaging concept. Fibrous ILA have a higher risk of progression and death. Clinically, computed tomography (CT) examination is a frequently used and convenient method compared with pulmonary function tests. This study aimed to c...
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2025-01-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01561-z |
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| author | Dechun Li Yingli Sun Zongjing Ma Bin Chen Liang Jin Ming Li |
| author_facet | Dechun Li Yingli Sun Zongjing Ma Bin Chen Liang Jin Ming Li |
| author_sort | Dechun Li |
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| description | Abstract Background Interstitial lung abnormalities (ILA) are a proposed imaging concept. Fibrous ILA have a higher risk of progression and death. Clinically, computed tomography (CT) examination is a frequently used and convenient method compared with pulmonary function tests. This study aimed to correlate quantitative CT airway parameters with pulmonary function parameters in patients with fibrous ILA, with the goal of establishing a prediction model for abnormal pulmonary function parameters in patients with fibrous ILA. Methods Ninety-five cases of fibrous ILA including CT images and 64 normal control cases were collected. All patients completed pulmonary function tests within one week. The airway parameters of the CT images of the two groups of cases were measured using a commercial software (Aview). Differences in airway parameters and lung function parameters between the two groups were analyzed by logistic multifactorial regression. The correlation between airway parameters and lung function parameters among 95 patients with fibrous ILA and a prediction model was determined for the decreased percentage forced vital capacity to predicted normal value (FVC%pred) in patients with fibrous ILA. Results Logistic multifactorial regression correlated FVC%pred and bronchial wall thickness (WT) were correlated with fibrous ILA. The 95 patients with fibrous ILA were divided into normal FVC%pred (n = 69) and decreased FVC%pred (n = 26) groups at the 80% cut-off. Logistic multifactorial regression revealed that FVC%pred decline in patients with fibrous ILA was effectively predicted by age (odds ratio [OR]: 1.11, 95% confidence interval [CI]: 1.02–1.21, p = 0.014), gender (OR: 4.16,95% CI: 1.27–13.71, p = 0.019), luminal area of the sixth generation brochi (LA6; OR: 0.87, 95%CI: 0.78–0.970,p = 0.015), and airway wall area (WA; OR: 1.12, 95%CI: 1.02–1.24, p = 0.020) were effective predictors of. The area under the curve of the prediction model based on the four parameters was 0.8428. Conclusion WT is a quantitative CT biomarker and FVC%pred is a valid lung function parameter in fibrous ILA patients. Age, gender, LA6, and WA are effective predictors of FVC%pred decline in fibrous ILA patients. The combined model has good predictive value. Clinical trial number 2024K249. |
| format | Article |
| id | doaj-art-d9ad21de571045f0b7298c5778ef1920 |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
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| series | BMC Medical Imaging |
| spelling | doaj-art-d9ad21de571045f0b7298c5778ef19202025-02-02T12:47:52ZengBMCBMC Medical Imaging1471-23422025-01-012511910.1186/s12880-025-01561-zPrediction of pulmonary function decline in fibrous interstitial lung abnormalities based on quantitative chest CT parametersDechun Li0Yingli Sun1Zongjing Ma2Bin Chen3Liang Jin4Ming Li5Department of Radiology, Huadong Hospital, Fudan UniversityDepartment of Radiology, Huadong Hospital, Fudan UniversityDepartment of Radiology, Huadong Hospital, Fudan UniversityDepartment of Radiology, Huadong Hospital, Fudan UniversityDepartment of Radiology, Huadong Hospital, Fudan UniversityDepartment of Radiology, Huadong Hospital, Fudan UniversityAbstract Background Interstitial lung abnormalities (ILA) are a proposed imaging concept. Fibrous ILA have a higher risk of progression and death. Clinically, computed tomography (CT) examination is a frequently used and convenient method compared with pulmonary function tests. This study aimed to correlate quantitative CT airway parameters with pulmonary function parameters in patients with fibrous ILA, with the goal of establishing a prediction model for abnormal pulmonary function parameters in patients with fibrous ILA. Methods Ninety-five cases of fibrous ILA including CT images and 64 normal control cases were collected. All patients completed pulmonary function tests within one week. The airway parameters of the CT images of the two groups of cases were measured using a commercial software (Aview). Differences in airway parameters and lung function parameters between the two groups were analyzed by logistic multifactorial regression. The correlation between airway parameters and lung function parameters among 95 patients with fibrous ILA and a prediction model was determined for the decreased percentage forced vital capacity to predicted normal value (FVC%pred) in patients with fibrous ILA. Results Logistic multifactorial regression correlated FVC%pred and bronchial wall thickness (WT) were correlated with fibrous ILA. The 95 patients with fibrous ILA were divided into normal FVC%pred (n = 69) and decreased FVC%pred (n = 26) groups at the 80% cut-off. Logistic multifactorial regression revealed that FVC%pred decline in patients with fibrous ILA was effectively predicted by age (odds ratio [OR]: 1.11, 95% confidence interval [CI]: 1.02–1.21, p = 0.014), gender (OR: 4.16,95% CI: 1.27–13.71, p = 0.019), luminal area of the sixth generation brochi (LA6; OR: 0.87, 95%CI: 0.78–0.970,p = 0.015), and airway wall area (WA; OR: 1.12, 95%CI: 1.02–1.24, p = 0.020) were effective predictors of. The area under the curve of the prediction model based on the four parameters was 0.8428. Conclusion WT is a quantitative CT biomarker and FVC%pred is a valid lung function parameter in fibrous ILA patients. Age, gender, LA6, and WA are effective predictors of FVC%pred decline in fibrous ILA patients. The combined model has good predictive value. Clinical trial number 2024K249.https://doi.org/10.1186/s12880-025-01561-zInterstitial lung abnormalitiesPulmonary function testQuantitative computed tomography |
| spellingShingle | Dechun Li Yingli Sun Zongjing Ma Bin Chen Liang Jin Ming Li Prediction of pulmonary function decline in fibrous interstitial lung abnormalities based on quantitative chest CT parameters BMC Medical Imaging Interstitial lung abnormalities Pulmonary function test Quantitative computed tomography |
| title | Prediction of pulmonary function decline in fibrous interstitial lung abnormalities based on quantitative chest CT parameters |
| title_full | Prediction of pulmonary function decline in fibrous interstitial lung abnormalities based on quantitative chest CT parameters |
| title_fullStr | Prediction of pulmonary function decline in fibrous interstitial lung abnormalities based on quantitative chest CT parameters |
| title_full_unstemmed | Prediction of pulmonary function decline in fibrous interstitial lung abnormalities based on quantitative chest CT parameters |
| title_short | Prediction of pulmonary function decline in fibrous interstitial lung abnormalities based on quantitative chest CT parameters |
| title_sort | prediction of pulmonary function decline in fibrous interstitial lung abnormalities based on quantitative chest ct parameters |
| topic | Interstitial lung abnormalities Pulmonary function test Quantitative computed tomography |
| url | https://doi.org/10.1186/s12880-025-01561-z |
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