Assessment Of Small Airway Disease Based On CT Images And Deep Learning Methods And Airway Hydrodynamics Research In COPD

Part 1 A study of the value of paired respiratory phase chest CT scanning for diagnosis and severity classification of COPD and assessment of dynamic changes in pulmonary function【Objective】To study the value of Parametric response map(PRM)parameters based on paired respiratory phase chest CT scanning in COPD diagnosis,severity classification,and assessment of dynamic changes in lung function.And to construct a deep learning model for COPD diagnosis and grading based on paired respiratory phase chest CT images and PRM.【Materials and Methods】We enrolled 349 cases(97 normal,89 COPD GOLD class I,117 COPD GOLD class II,and 46 COPD GOLD class III-IV)who underwent screening for the three major diseases of the chest in our hospital from August 2018 to December 2019 and had a pulmonary function and paired respiratory phase chest CT examination,of which 126 subjects(83normal,14 COPD GOLD class I,23 COPD GOLD class II,and 6 COPD GOLD class III)underwent a 1-2 year review of pulmonary function and paired respiratory phase chest CT.The quantitative parameters of dual-phase chest CT images mainly included lung volumes and PRM parameters,and the PRM parameters included the volumes(cc)of emphysema,functional small airways(f SAD),normal lung tissue(Normal),and unknown categorized lung tissue(Uncategorized)as well as their volume percentages(%),with a total of 72 parameters at the level of the whole lung,the right and left lungs,and the 5 lobes of the lung.Lung function parameters included FVC,FEV1,FEV1/FVC,FEV1 as a percentage of predicted value(FEV1%pred),MMF,MMF as a percentage of predicted value(MMF%pred),PEF,V75,V50,V25,and Extrap V.(1)The correlation between PRM parameters and pulmonary function parameters in the349 subjects was analyzed using Pearson’s correlation coefficient or Spearman’s correlation coefficient.(2)ANOVA or Kruskal-Wallis H-test was used to compare the differences in PRM parameters between normal population,and the differences in PRM parameters among the three subgroups of COPD(GOLD class I,GOLD class II,and GOLD class III-IV)in the349 subjects.(3)The annual rate of change in lung function was calculated for 126 patients with baseline and follow-up lung function and dual-phase chest CT.Annual rate of change in lung function=(review lung function-baseline lung function)/years of follow-up.The correlation between baseline PRM parameters and the rate of change in lung function was analyzed using Pearson’s correlation coefficient or Spearman’s correlation coefficient(4)The 349 subjects were randomly divided into 3 data sets in the ratio of 7:2:1:training set(244 cases,including 68 cases in the normal group,62 cases in GOLD class I group,82cases in GOLD class II group,and 32 cases in GOLD class III-IV group),validation set(70cases,19 cases in the normal group,18 cases in GOLD class I group,24 cases in GOLD class II group,and 9 cases in GOLD class III-IV group),and test set(35 cases,10 cases in normal group,9 cases in GOLD class I group,11 cases in GOLD class II group,and 5 cases in GOLD class III-IV group).【Results】(1)There was a weak correlation between PRM parameters and pulmonary function parameters in the 349 subjects(r/r_s=0.2 to 0.4/-0.2 to-0.4,P<0.05).(2)Among the 349 subjects,there were 42 functional small airway parameters that were statistically different between the normal group and the COPD group(P<0.05),and 46functional small airway parameters were statistically different between COPD GOLD grades III-IV and COPD GOLD grades I and II,respectively(P<0.05).The PRM parameters were all not statistically different between COPD GOLD grades I and II.(3)In the 126 cases of the follow-up cohort,the rate of the change of FEV1 was-0.274±0.715(range:-2.23-2.42).There was a weak correlation between the rate of change of lung function parameters and the baseline PRM parameters(r_s=0.2 to 0.4/-0.2 to-0.4,P<0.05).(4)CNN biclassification and triclassification prediction models using paired respiratory phase chest CT images or single-inspiratory-phase chest CT images combined with PRM parameters as inputs were successfully constructed.Among them,when the two-classification(normal and COPD patients)model used paired respiratory phase chest CT images as inputs,the precision rate could reach 98.5%,the recall(sensitivity)reached99.10%,and the F1 was 98.80%,and when single-inspiratory-phase chest CT images combined with PRM parameters were used as inputs,the precision rate could reach 98.9%,the recall(sensitivity)reached 99.00%,and the F1 was 98.95%.When the triclassification prediction(GOLD class I,GOLD class II,and GOLD class III-IV COPD patients)model used paired respiratory phase chest CT images as inputs,the precision rate was up to 73.79%,the recall(sensitivity)was up to 75.59%,and the F1 was 74.68%,and when single-inspiratory-phase chest CT images combined with PRM parameters were used as inputs,the precision rate was up to 74.35%,the recall(sensitivity)up to 73%and F1 up to 73.67%.【Conclusion】The PRM parameters could distinguish between normal people and COPD patients at the level of whole lung,right and left lungs and each lobe,and could distinguish between GOLD class III-IV COPD patients and GOLD class I/II COPD patients at the level of whole lung,right and left lungs,and each lobe except for the middle lobe of the right lung,but it was unable to distinguish between GOLD class I and II COPD patients.The F1 of the COPD deep learning diagnostic model based on paired respiratory phase chest CT images was applied up to 98.95%,and the F1 of the COPD deep learning grading model was applied up to 74.68,in which the model efficacy was about the same when inputting dual-phase chest CT images or inputting single-inspiratory-phase CT images in combination with PRM parameters.The PRM parameters had correlations with changes of pulmonary function parameters.Part 2 The study on the feasibility of deep learning models based on inspiratory-phase chest CT to evaluate functional small airway disease【Objective】To reduce radiation dose while improving the diagnosis of chronic obstructive pulmonary disease(COPD),artificial intelligence algorithms were utilized to explore the feasibility of replacing paired respiratory phase chest CT with a single inspiratory-phase CT to obtain parametric response mapping(PRM)results.【Materials and Methods】308 subjects who underwent paired respiratory phase chest CT scans and pulmonary function tests between 2018 and 2021 in Changzheng Hospital were enrolled retrospectively.They were randomly divided into the training set(216 cases),the validation set(31 cases),and the test set(61 cases)according to the ratio of 7:1:2.194 cases from Changzheng Hospital in 2023 were included as the second internal test set at a different time period from the test set.And external test was performed in 45 cases from the Second Hospital of Ningbo.All paired respiratory phase chest CT images were processed using commercial software to obtain PRM.(1)We built deep learning(DL)models by employing two approaches.The first one is the predictive method,which performs an end-to-end voxel-wise class prediction model.The UNet and Attention Unet were applied as the backbone of the model.(2)The second approach is the generative method.In the generation method,the prediction task based on single inspiratory-phase CT images is decoupled into bi-phase deformable registration task and expiratory-phase CT images generation task.The bi-phase deformable registration method is constructed for image registration and compared with the open-source registration algorithms,Syn and TVMSQC,to evaluate their registration efficacy.The efficiency of the registration algorithms is evaluated using the target registration error(TRE)and the inference time.(3)Three frameworks for expiratory phase image style generation were tried,including the na?ve generation method,the residual learning scheme,and the method for generator,discriminator interactions to improve generation quality.The three frameworks were evaluated using mean absolute error(MAE),structural similarity index(SSIM),PRM accuracy,and peak signal-to-noise ratio(PSNR).5-fold cross-validation was performed on all cohort data.(4)Using the PRM map obtained by paired respiratory phase chest CT images as the gold standard,we use voxel-wise metrics including Sensitivity,Dice Similarity Coefficient(DSC),and False Positive Rate(FPR)to evaluate and compare the performance of the predictive method and the generative method in predicting emphysema and functional small airway disease(f SAD).(5)The better performing method were further tested on a second internal test set and an external test set to evaluate the model performance using accuracy(ACC),area under the ROC curve(AUC),and FPR.【Results】(1)Of the two prediction models,the model with UNet as the backbone predicted emphysema with a sensitivity of 0.6762,a DSC of 0.6284,and an FPR of 0.0036,and predicted f SAD with a sensitivity of 0.3583,a DSC of 0.3781,and an FPR of 0.0117;the model with Attention Unet as the backbone predicted emphysema with a sensitivity of 0.6869,DSC of 0.6340,and FPR of 0.0036,and the sensitivity of predicting f SAD was 0.3893,DSC of 0.4126,and FPR of 0.0119.(2)The bi-phase deformable registration method used in the generative method has a mean TRE of 17.9064 and an inference time of 5 seconds,both of which outperform the open source algorithms Syn(TRE: 28.7574,inference time: 180 seconds)and TVMSQC(TRE: 28.7566,inference time: 195 seconds).(3)Among the three generative methods,the na?ve generation method has an MAE of 0.014±0.003,an SSIM of 0.933±0.012,a PRM accuracy of 0.8±0.011,and a PSNR of 30.510±0.909,which is superior to the other two methods except for the PSNR(the residual learning scheme: MAE 0.017±0.003,SSIM 0.915±0.013,the PRM accuracy 0.788±0.013,PSNR: 32.091±0.864;generator and discriminator interaction method: MAE0.017±0.003,SSIM0.908±0.014,PRM accuracy 0.781±0.010,PSNR: 29.076±0.707).(4)The sensitivity of the na?ve generation method for predicting emphysema was 0.7955,DSC was 0.8365,and FPR was 0.0013;the sensitivity for predicting f SAD was 0.8626,DSC was 0.3762,and FPR was 0.1342.The generative approach significantly outperforms the predictive approach for the detection of f SAD with a sensitivity of 86.26%(vs 38.93%)in the test set.(5)The na?ve generation method achieved 80.70% accuracy in the second internal test set with AUCs of 64.37%,84.45% and 96.53% for emphysema,f SAD and healthy lung tissue,respectively.It also achieved a prediction accuracy of 79.90% in the external test set,with AUCs of 58.40%,84.87% and 93.95% for emphysema,f SAD and healthy lung tissue,respectively.【Conclusion】The generative approach not only generates reliable CT images of the expiratory phase but also significantly outperforms the predictive model in the detection of f SAD.And the algorithm is robust for it has achieved satisfactory test performance in the second internal test and the external test(0.807 and 0.799 accuracy).Part 3 The study on the feasibility of chest CT image-based aerodynamic simulation for the assessment of small airway resistance in COPD【Objective】To develop a novel method for calculating small airway resistance using computational fluid dynamics(CFD)based on CT data and evaluate its value to identify COPD.【Materials and Methods】90 subjects,containing 30 normal subjects,30 PRISm patients,and 30 COPD patients,who underwent chest CT scans and pulmonary function tests between August 2020 and December 2020 were enrolled retrospectively.The pulmonary function parameters include the total airway resistance(RBP)at the expiration phase,FEV1,FEV1/FVC,FEV1 measured value as a percentage of the predicted value(FEV1 % predicted),PEF,PEF as a percentage of the predicted value(PEF% predicted),MEF 25,MEF 50,MEF 75,MMEF75/25,and MEF 25/50/75 or the MMEF75/25 as a percentage of the predicted value(MEF25% predicted,MEF50% predicted,MEF75% predicted and MMEF 75/25% predicted).The airway from the trachea down to the sixth generation of bronchioles was reconstructed by a 3D slicer.And tetrahedral computational grids were generated with ICEM-CFD(ANSYS,Canonsburg,USA).The outlet boundary condition at the tracheal opening was specified as the standard atmosphere(i.e.,101,325 Pa),whereas the inlet boundary condition at the distal end of each bronchiole was derived from the FEV1.The ANSYS-CFX solver was used to resolve the flow physiology within the first second of forced expiratory.The endobronchial resistance was calculated based on the bronchial diameter,boundary conditions,and pressure drop.We calculated the effective resistance of the reconstructed airway model(RCFD),that is the total airway resistance for levels 1-6,and its percentage of total airway resistance(RCFD%)based on the topology by analogy with an electric circuit.RCFD was subtracted from RBP to obtain small airway resistance(RSA),and RSA as a percentage of total airway resistance(RSA%)was also calculated.ANOVA or Kruskal-Wallis H-test was used to compare the differences in airway resistance parameters among the normal,PRISm and COPD groups.Correlations between airway resistance parameters and pulmonary function parameters were analyzed using Pearson’s correlation coefficient or Spearman’s correlation coefficient.【Results】The RSA,RSA% and RCFD% were all significantly different among the normal,PRISm and COPD groups(p<0.05).There was a significant difference in RCFD between the normal group and the PRISm/COPD groups(P < 0.05).RCFD % tended to decrease with the disease progression.RSA and RSA % tended to increase with the disease progression.Airway resistance parameters(RCFD,RCFD%,RSA and RSA%)were correlated with pulmonary function parameters(FEV1,FEV1/FVC,FEV1% predicted,PEF,PEF% predicted,MEF 25,MEF 50,MEF 75,MMEF75/25,MEF25% predicted,MEF50% predicted,MEF75% predicted and MMEF 75/25% predicted).Among them,RSA and RSA% were strongly negatively correlated with FEV1,FEV1%predicted,FEV1/FVC,MEF75,MEF75%predicted,MEF50,MEF50%predicted,MEF25,MMEF75/25 and MMEF75/25%predicted(r/rs=-0.6 ~-0.8,P < 0.001).RCFD% was strongly and positively correlated with FEV1,FEV1%predicted,FEV1/FVC,MEF75,MEF75%predicted,MEF50,MEF50%predicted,MEF25,MMEF75/25 and MMEF75/25%predicted(r/rs = 0.6 ~ 0.8,P < 0.001).【Conclusion】The airway aerodynamic allows decoupling of the large airway resistance from the total airway resistance based on a 1-6 level large airway model and successfully calculates the small airway resistance.The calculated RCFD%,RSA and RSA % were able to distinguish normal,PRISm and COPD populations and correlated well with pulmonary function parameters.