利用定量计算机断层扫描纤维化和肺气肿特征预测特发性肺纤维化患者临床结局

doi:10.3877/cma.j.issn.1674-6902.2025.05.009

目的

分析定量计算机断层扫描(computed tomography, CT)的肺纤维化和肺气肿评估预测特发性肺纤维化(idiopathic pulmonary fibrosis, IPF)患者临床结局的性能。

方法

回顾性纳入2021年1月至2024年4月我院确诊的IPF患者92例，行高分辨率CT(high-resolution CT, HRCT)、定量CT以及肺功能检查，肺气肿的程度为低于－950 HU的像素分割为低衰减区(low attenuation areas, LAA)，计算每个切片的%LAA。纤维化病变的程度为所有大于－700 HU的像素分割为高衰减区(highly attenuation areas, HAA)，计算%HAA，将各胸段的%LAA和%HAA相加得到异常面积百分比(percentage of abnormal area, %AA)。临床不良结局为随访观察期间患者发生临床事件(住院、急性加重和死亡)。

结果

根据定量CT、肺功能检查以及临床特征进行聚类分析，聚类1中，用力肺活量预计值百分比(percent predicted of forced vital capacity, FVC%pred)高，CT视觉评分较低，%LAA及%AA低。聚类2中，FVC%pred最低，CT视觉评分高。聚类3中，FVC%pred较高，但CT视觉评分高，%LAA及%AA也最高。35例(38.04%)IPF患者发生临床事件，其中聚类1发生9例(22.50%)，中位时间663.5 d；聚类2发生12例(54.55%)，中位时间616.5 d；聚类3发生14例，中位时间581.0 d；聚类1无临床事件生存时间相较于聚类2、聚类3患者长(log rank＝6.257，P＝0.044；聚类1 vs.聚类2：log rank＝4.921，P＝0.027；聚类1 vs.聚类3：log rank＝4.622，P＝0.032)；聚类2与聚类3临床结局相比差异无统计学意义(log rank＝0.002，P＝0.966)。通过Spearman秩相关分析，%LAA、%HAA、%AA与肺一氧化碳弥散量占预计值的百分比(diffusing capacity of the lung for carbon monoxide, DL_CO%pred)呈负相关(rho＝－0.407，－0.536，－0.737，P<0.001)。时间依赖性受试者工作特征曲线分析结果为%AA对IPF患者1年、2年、3年的不良结局预测性能[曲线下面积分别为0.729(95%CI：0.681~0.770)、0.852(95%CI：0.794~0.922)、0.748(95%CI：0.693~0.796)]。聚类1生存者36例，死亡者4例，聚类2生存者17例死亡者5例、聚类3生存者24例，死亡者6例，3个聚类的总生存时间无统计学差异(log rank＝2.833，P＝0.243)。

结论

定量CT检测与肺功能检查结果及IPF患者的临床结局相关。CT定量测量可作为判断IPF患者临床病程及预后的有效方法。

关键词: 特发性肺纤维化, 定量计算机断层扫描, 肺纤维化, 肺气肿, 临床结局

Objective

To analyze the efficacy of quantitative computed tomography (CT) in evaluating and predicting the clinical outcomes of patients with idiopathic pulmonary fibrosis (IPF) for pulmonary fibrosis and emphysema.

Methods

Patients diagnosed with IPF in our hospital from January 2021 to April 2024 were retrospectively included. All patients underwent high-resolution CT (HRCT), quantitative CT and pulmonary function tests. Pixels with a degree of emphysema lower than -950 HU were segmented into low attenuation areas (LAA), and the %LAA of each slice was calculated. The degree of fibrotic lesions is that all pixels greater than -700 HU are segmented into highly attenuation areas (HAA), the %HAA is calculated, and the %LAA and %HAA of each thoracic segment are added together to obtain the percentage of abnormal area (%AA). The adverse clinical outcomes were clinical events (hospitalization, acute exacerbation, and death) in patients during the follow-up observation period.

Results

Cluster analysis was conducted based on quantitative CT, pulmonary function tests, and clinical characteristics. Patients in cluster 1 had a higher percent predicted of forced vital capacity (FVC%pred), a lower CT visual score, and lower %LAA and %AA. Among the patients in Cluster 2, the FVC%pred was the lowest and the CT visual score was high. Patients in Cluster 3 had a relatively high FVC%pred, but a high CT visual score, and the %LAA and %AA were also the highest. A total of 35 cases (38.04%) of IPF patients had clinical events. The event-free survival time of patients in cluster 1 was longer than that of patients in clusters 2 and 3 (log rank=6.257, P=0.044; Cluster 1 vs. Cluster 2: log rank=4.921, P=0.027; Cluster 1 vs. Cluster 3: log rank=4.622, P=0.032). However, there was no statistically significant difference in the clinical outcomes between cluster 2 and Cluster 3 (log rank=0.002, P=0.966). Through Spearman rank correlation analysis, %LAA, %HAA, and %AA were all negatively correlated with the percent predicted of diffusing capacity of the lung for carbon monoxide (DL_CO%pred) (rho=-0.407, -0.536, -0.737, P<0.001). The results of the time-dependent receiver operating characteristic curve analysis showed that %AA had a better predictive efficacy for adverse outcomes in IPF patients at 1 year, 2 years, and 3 years [areas under the curves were 0.729(95%CI: 0.681~0.770), 0.852(95%CI: 0.794~0.922), 0.748(95%CI: 0.693~0.796), respectively].

Conclusion

Quantitative CT detection is significantly correlated with the results of pulmonary function tests and the clinical outcomes of patients with IPF. CT quantitative measurement can be used as an effective method to determine the clinical course and prognosis of patients with IPF.

Key words: Idiopathic pulmonary fibrosis, Quantitative computed tomography, Pulmonary fibrosis Emphysema, Clinical outcome

图1 PF患者典型CT影像图。男，69岁，图A为上肺野病变；图B为中肺野病变；图C为右下肺野病变；图D为左下肺野病变

表1 不同聚类患者定量CT、肺功能检查以及临床特征

临床资料	聚类1(n＝40)	聚类2(n＝22)	聚类3(n＝30)	H/F/χ²	P值
年龄[岁，M₅₀(P₂₅，P₇₅)]	67.00(61.50，70.00)	68.50(63.25，70.00)	66.00(63.25，70.00)	0.723	0.697
男/女(n)	30/10	17/5	22/8	0.105	0.949
BMI[kg/m²，(±s)	24.00±4.08	24.26±3.98	23.73±3.05	0.128	0.880
吸烟史[n(%)]	39(97.50)	21(95.45)	30(100.00)	1.268	0.530
肺功能检查(%)
FEV₁%pred[M₅₀(P₂₅，P₇₅)]	73.10(65.03，82.90)	72.85(63.60，81.40)	73.05(65.48，85.93)	0.195	0.907
FVC%pred[M₅₀(P₂₅，P₇₅)]	72.32(62.50，84.45)	62.91(59.95，77.27)^a	69.18(61.09，85.93)^b	6.730	0.035
DL_CO%pred[M₅₀(P₂₅，P₇₅)]	68.15(59.40，86.90)	62.95(44.83，86.88)	67.40(53.93，78.65)	1.196	0.550
FEV₁/FVC%pred(±s)	102.35±8.26	100.19±8.90	101.91±10.18	0.415	0.662
定量CT检查[M₅₀(P₂₅，P₇₅)]
GGO(mm³)	444 564.97(334 203.94，629 623.98)	985 986.91(799 205.81，1 238 970.81)^a	552 376.40(370 813.49，771 198.24)^ab	22.484	0.000
网状影(mm³)	87 773.38(34 559.79，177 256.70)	250 337.29(101 876.42，410 079.09)^a	284 600.25(169 639.61，376 249.98)^a	10.176	0.006
蜂窝影(mm³)	5 202.17(554.06，15 022.66)	28 666.82(465.65，83 625.38)	14 319.84(2 361.52，116 959.61)	3.840	0.147
FS(mm³)	745 197.02(664 167.27，852 693.99)	1 394 556.65(1 272 798.52，1 552 427.32)^a	623 869.85(473 825.70，790 405.14)^b	34.967	0.000
%LAA(%)	4.18(2.62，10.92)	7.88(3.76，14.73)^a	12.77(8.53，17.79)^ab	17.491	0.000
%HAA(%)	19.44(15.46，22.47)	23.03(15.71，25.77)	20.67(16.75，25.87)	4.884	0.090
%AA(%)	24.79(19.47，33.90)	29.82(23.94，37.69)	35.39(27.84，41.73)^a	14.984	0.000

表1 不同聚类患者定量CT、肺功能检查以及临床特征

临床资料	聚类1(n＝40)	聚类2(n＝22)	聚类3(n＝30)	H/F/χ²	P值
年龄[岁，M₅₀(P₂₅，P₇₅)]	67.00(61.50，70.00)	68.50(63.25，70.00)	66.00(63.25，70.00)	0.723	0.697
男/女(n)	30/10	17/5	22/8	0.105	0.949
BMI[kg/m²，(±s)	24.00±4.08	24.26±3.98	23.73±3.05	0.128	0.880
吸烟史[n(%)]	39(97.50)	21(95.45)	30(100.00)	1.268	0.530
肺功能检查(%)
FEV₁%pred[M₅₀(P₂₅，P₇₅)]	73.10(65.03，82.90)	72.85(63.60，81.40)	73.05(65.48，85.93)	0.195	0.907
FVC%pred[M₅₀(P₂₅，P₇₅)]	72.32(62.50，84.45)	62.91(59.95，77.27)^a	69.18(61.09，85.93)^b	6.730	0.035
DL_CO%pred[M₅₀(P₂₅，P₇₅)]	68.15(59.40，86.90)	62.95(44.83，86.88)	67.40(53.93，78.65)	1.196	0.550
FEV₁/FVC%pred(±s)	102.35±8.26	100.19±8.90	101.91±10.18	0.415	0.662
定量CT检查[M₅₀(P₂₅，P₇₅)]
GGO(mm³)	444 564.97(334 203.94，629 623.98)	985 986.91(799 205.81，1 238 970.81)^a	552 376.40(370 813.49，771 198.24)^ab	22.484	0.000
网状影(mm³)	87 773.38(34 559.79，177 256.70)	250 337.29(101 876.42，410 079.09)^a	284 600.25(169 639.61，376 249.98)^a	10.176	0.006
蜂窝影(mm³)	5 202.17(554.06，15 022.66)	28 666.82(465.65，83 625.38)	14 319.84(2 361.52，116 959.61)	3.840	0.147
FS(mm³)	745 197.02(664 167.27，852 693.99)	1 394 556.65(1 272 798.52，1 552 427.32)^a	623 869.85(473 825.70，790 405.14)^b	34.967	0.000
%LAA(%)	4.18(2.62，10.92)	7.88(3.76，14.73)^a	12.77(8.53，17.79)^ab	17.491	0.000
%HAA(%)	19.44(15.46，22.47)	23.03(15.71，25.77)	20.67(16.75，25.87)	4.884	0.090
%AA(%)	24.79(19.47，33.90)	29.82(23.94，37.69)	35.39(27.84，41.73)^a	14.984	0.000

图1 定量CT与肺功能检查的Spearman秩相关散点矩阵图注：FEV₁%pred为第一秒用力呼气容积占预计值百分比；DL_CO%pred肺一氧化碳弥散量占预计值的百分比；FEV₁/FVC为第一秒用力呼气容积占用力肺活量的百分比；GGO为磨玻璃样影；FS为纤维化评分；%LAA为低衰减区；%HAA为高衰减区；%AA为异常面积百分比

表2 COX回归分析IPF患者不良结局的危险因素

临床资料及指标	β	SE	Walds值	HR(95%CI)	P值
单因素
BMI(连续型变量)	－0.018	0.041	0.198	0.982(0.906~1.064)	0.656
FEV₁%pred(连续型变量)	－0.006	0.013	0.189	0.994(0.970~1.020)	0.664
FVC%pred(连续型变量)	－0.008	0.010	0.694	0.992(0.973~1.011)	0.405
DL_CO%pred(连续型变量)	－0.076	0.018	17.816	0.927(0.894~0.960)	0.000
FEV₁/FVC(连续型变量)	0.021	0.022	0.951	1.021(0.979~1.066)	0.330
GGO(连续型变量)	0.259	0.120	4.690	1.295(1.025~1.637)	0.030
网状影(连续型变量)	0.161	0.064	6.269	1.175(1.036~1.333)	0.012
FS(连续型变量)	0.138	0.055	6.315	1.148(1.031~1.278)	0.012
%LAA(连续型变量)	0.060	0.020	9.033	1.061(1.021~1.104)	0.003
%HAA(连续型变量)	0.049	0.023	4.636	1.050(1.004~1.098)	0.031
%AA(连续型变量)	0.062	0.016	15.734	1.064(1.032~1.097)	0.000
多因素
DL_CO%pred(连续型变量)	0.011	0.008	1.764	1.011(0.995~1.027)	0.184
GGO(连续型变量)	0.311	0.949	0.108	1.365(0.213~1.760)	0.743
网状影(连续型变量)	0.041	0.050	0.681	1.042(0.945~1.148)	0.409
FS(连续型变量)	0.189	0.098	3.682	1.208(0.996~1.465)	0.055
%LAA(连续型变量)	0.020	0.024	0.670	1.020(0.972~1.070)	0.413
%HAA(连续型变量)	0.028	0.024	1.396	1.029(0.982~1.078)	0.237
%AA(连续型变量)	0.060	0.022	7.268	1.061(1.016~1.108)	0.007

表2 COX回归分析IPF患者不良结局的危险因素

临床资料及指标	β	SE	Walds值	HR(95%CI)	P值
单因素
BMI(连续型变量)	－0.018	0.041	0.198	0.982(0.906~1.064)	0.656
FEV₁%pred(连续型变量)	－0.006	0.013	0.189	0.994(0.970~1.020)	0.664
FVC%pred(连续型变量)	－0.008	0.010	0.694	0.992(0.973~1.011)	0.405
DL_CO%pred(连续型变量)	－0.076	0.018	17.816	0.927(0.894~0.960)	0.000
FEV₁/FVC(连续型变量)	0.021	0.022	0.951	1.021(0.979~1.066)	0.330
GGO(连续型变量)	0.259	0.120	4.690	1.295(1.025~1.637)	0.030
网状影(连续型变量)	0.161	0.064	6.269	1.175(1.036~1.333)	0.012
FS(连续型变量)	0.138	0.055	6.315	1.148(1.031~1.278)	0.012
%LAA(连续型变量)	0.060	0.020	9.033	1.061(1.021~1.104)	0.003
%HAA(连续型变量)	0.049	0.023	4.636	1.050(1.004~1.098)	0.031
%AA(连续型变量)	0.062	0.016	15.734	1.064(1.032~1.097)	0.000
多因素
DL_CO%pred(连续型变量)	0.011	0.008	1.764	1.011(0.995~1.027)	0.184
GGO(连续型变量)	0.311	0.949	0.108	1.365(0.213~1.760)	0.743
网状影(连续型变量)	0.041	0.050	0.681	1.042(0.945~1.148)	0.409
FS(连续型变量)	0.189	0.098	3.682	1.208(0.996~1.465)	0.055
%LAA(连续型变量)	0.020	0.024	0.670	1.020(0.972~1.070)	0.413
%HAA(连续型变量)	0.028	0.024	1.396	1.029(0.982~1.078)	0.237
%AA(连续型变量)	0.060	0.022	7.268	1.061(1.016~1.108)	0.007

1	Bonella F, Spagnolo P, Ryerson C. Current and future treatment landscape for idiopathic pulmonary fibrosis[J]. Drugs, 2023, 83(17): 1581-1593.
2	孙锦涛，徐兴祥. 特发性肺纤维化发病机制及药物治疗的研究进展[J/OL]. 中华肺部疾病杂志(电子版), 2015, 8(2): 228-233.
3	Glass DS, Grossfeld D, Renna HA, et al. Idiopathic pulmonary fibrosis: Current and future treatment[J]. Clin Respir J, 2022, 16(2): 84-96.
4	Marcoux V, Lok SD, Mondal P, et al. Impact of surgical lung biopsy on lung function and survival in patients with idiopathic pulmonary fibrosis in a multi-centre registry cohort[J]. Respirology, 2024, 29(7): 596-604.
5	Nolan CM, Schofield SJ, Maddocks M, et al. Change in gait speed and adverse outcomes in patients with idiopathic pulmonary fibrosis: A prospective cohort study[J]. Respirology, 2023, 28(7): 649-658.
6	Wells AU, Jacob J, Sverzellati N, et al. A formula for predicting emphysema extent in combined idiopathic pulmonary fibrosis and emphysema[J]. Respir Res, 2024, 25(1): 33-43.
7	Bak SH, Park HY, Nam JH, et al. Predicting clinical outcome with phenotypic clusters using quantitative CT fibrosis and emphysema features in patients with idiopathic pulmonary fibrosis[J]. PLoS One, 2019, 14(4): e0215303-e0215316.
8	Thillai M, Oldham JM, Ruggiero A, et al. Deep learning-based segmentation of computed tomography scans predicts disease progression and mortality in idiopathic pulmonary fibrosis[J]. Am J Respir Crit Care Med, 2024, 210(4): 465-472.
9	Sun H, Liu M, Kang H, et al. Idiopathic pulmonary fibrosis disease progression: a dynamic quantitative chest computed tomography follow-up analysis[J]. Quant Imaging Med Surg, 2023, 13(3): 1488-1498.
10	Raghu G, Remy-Jardin M, Myers JL, et al. Diagnosis of idiopathic pulmonary fibrosis. An official ATS/ERS/JRS/ALAT clinical practice guideline[J]. Am J Respir Crit Care Med, 2018, 198(5): e44-e68.
11	Matsuoka S, Yamashiro T, Matsushita S, et al. Quantitative CT evaluation in patients with combined pulmonary fibrosis and emphysema: correlation with pulmonary function[J]. Acad Radiol, 2015, 22(5): 626-657.
12	Matsuoka S, Yamashiro T, Matsushita S, et al. Morphological disease progression of combined pulmonary fibrosis and emphysema: comparison with emphysema alone and pulmonary fibrosis alone[J]. J Comput Assist Tomogr, 2015, 39(2): 153-162.
13	Laveneziana P, Albuquerque A, Aliverti A, et al. ERS statement on respiratory muscle testing at rest and during exercise[J]. Eur Respir J, 2019, 53(6): 1801214-1801429.
14	Collard HR, Ryerson CJ, Corte TJ, et al. Acute Exacerbation of idiopathic pulmonary fibrosis. An international working group report[J]. Am J Respir Crit Care Med, 2016, 194(3): 265-340.
15	Muri J, Durcová B, Slivka R, et al. Idiopathic pulmonary fibrosis: Review of current knowledge[J]. Physiol Res, 2024, 73(4): 487-497.
16	Ouyang X, Qian Y, Tan Y, et al. The prognostic role of high-density lipoprotein cholesterol/C-reactive protein ratio in idiopathic pulmonary fibrosis[J]. QJM, 2024, 117(12): 858-865.
17	Zhang H, Li X, Zhang X, et al. Quantitative CT analysis of idiopathic pulmonary fibrosis and correlation with lung function study[J]. BMC Pulm Med, 2024, 24(1): 437-446.
18	Kraven LM, Taylor AR, Molyneaux PL, et al. Cluster analysis of transcriptomic datasets to identify endotypes of idiopathic pulmonary fibrosis[J]. Thorax, 2023, 78(6): 551-558.
19	Fainberg HP, Moodley Y, Triguero I, et al. Cluster analysis of blood biomarkers to identify molecular patterns in pulmonary fibrosis: assessment of a multicentre, prospective, observational cohort with independent validation[J]. Lancet Respir Med, 2024, 12(9): 681-692.
20	Yoon HY, Lee SH, Ha S, et al. The value of 18F-FDG PET/CT in evaluating disease severity and prognosis in idiopathic pulmonary fibrosis patients[J]. J Korean Med Sci, 2021, 36(41): e257-e270.
21	Park HJ, Lee SM, Song JW, et al. Texture-based automated quantitative assessment of regional patterns on initial CT in patients with idiopathic pulmonary fibrosis: Relationship to decline in forced Vital Capacity[J]. AJR Am J Roentgenol, 2016, 207(5): 976-983.
22	Üçsular F, Karadeniz G, Polat G, et al. Quantitative CT in mortality prediction in pulmonary fibrosis with or without emphysema[J]. Sarcoidosis Vasc Diffuse Lung Dis, 2021, 38(3): e2021024-e2021034.
23	戴晓瑛，王志峰，颜洪顺，等. 肺纤维化合并肺气肿患者血清IGF-1 VEGF MMP-9表达变化及其与肺功能水平的相关性分析[J]. 河北医学，2023, 29(3): 393-397.
24	崔璐，程留慧，朱永月，等. 肺纤维化合并肺气肿综合征定量CT与肺功能的相关性分析[J]. 影像科学与光化学，2024, 42(1): 58-64.
25	Yamamoto Y, Hirata H, Shiroyama T, et al. Respiratory impedance is associated with ventilation and diffusing capacity in patients with idiopathic pulmonary fibrosis combined with emphysema[J]. Int J Chron Obstruct Pulmon Dis, 2022, 17: 1495-1506.
26	邓文静，樊娜，张娜，等. 女性特发性肺间质纤维化并发肺气肿的危险因素分析及相关预测模型构建[J]. 西部医学，2023, 35(11): 1615-1619, 1624.
27	Minoru I, Soichiro I, Nobuyasu A, et al. An autopsy study of combined pulmonary fibrosis and emphysema: correlations among clinical, radiological, and pathological features[J]. BMC Pulm Med, 2014, 14: 104.
28	Wu WJ, Huang WM, Liang CH, et al. Pulmonary vascular volume is associated with DLCO and fibrotic score in idiopathic pulmonary fibrosis: an observational study[J]. BMC Med Imaging, 2022, 22(1): 76-85.

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Clinical outcomes of patients with idiopathic pulmonary fibrosis were predicted using the characteristics of quantitative CT fibrosis and emphysema

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Clinical outcomes of patients with idiopathic pulmonary fibrosis were predicted using the characteristics of quantitative CT fibrosis and emphysema