The promotion and application of pulmonary function tests (PFTs) in China have achieved preliminary success;however, numerous deficiencies persist in multidisciplinary PFT clinical application pathways and management. Current challenges remain in identifing target population, implementing pathways within and outside medical institutions, allocating hardware, training personnel, managing information, and assessing performance.The consensus aims to optimize multidisciplinary clinical pathways and management models for PFTs by leveraging information technology and strengthening organizational coordination. Using a modified Delphi method and a systematic literature review, 14 core clinical questions were identified across four categories: (1) cognitive and information management platforms, (2) laboratory infrastructure and personnel, (3) performance evaluation, and (4) outreach and expansion capabilities. Furthermore, it proposes targeted and feasible implementation pathways and solutions, aiming to standardize PFTs practices and enhance multidisciplinary collaboration nationwide.Summary of Key RecommendationsQuestion 1: How to identify the candidates eligible for PFTs?Recommendation 1: By defining keywords for the PFT-eligible population, clinical physicians can enhance their ability to identify individuals at high risk for pulmonary function abnormalities and increase PFT utilization rates. The eligible candidates can be automatically identified through Hospital Information System (HIS) that recognize keywords in outpatient or inpatient records and trigger pop-up alerts.Question 2: If pop-up alerts are not feasible, which scales can be used to assist in identifying the PFT-eligible candidates?Recommendation 2: Evaluating patients' history of high-risk exposure or completing the screening questionnaires before visits can help improve the identification rate of the PFT-eligible candidates. Questionnaires include the Chronic Obstructive Pulmonary Disease Screening Questionnaire (COPD-SQ) or the Lung Function Screening Questionnaire (LFQ), etc. (Strong Recommendation)Currently, the COPD-SQ and LFQ questionnaires are commonly used for screening high-risk COPD populations. Due to the low cost and ease of use, they are suitable for widespread use across all levels of medical institutions. Other risk factors such as significant exposure to biomass fuels, asthma, and recurrent respiratory infections in early childhood can also lead to lung function decline or impaired lung development. Some necessary items can be added to improve the sensitivity and applicability of PFTs, forming a modified version or Chinese version of PFT screening tool.Question 3: How to select appropriate PFT modalities based on clinical needs?Recommendation 3: Physicians should select the optimal testing methods and their combinations based on each patient's specific clinical status. (Strong Recommendation)PFTs include basic and advanced tests. Basic tests primarily refer to pulmonary ventilation function tests (spirometry) or forced oscillation technique (FOT). The latter is particularly suitable for patients with ventilation impairments who cannot cooperate with forced expiratory maneuvers. Advanced tests include Bronchodilator Test/Bronchodilator Reversibility Test, Diffusing Capacity of the Lung for Carbon Monoxide (DLCO) test, Lung Plethysmography, Airway Resistance Assessment, Fractional Exhaled Nitric Oxide (FeNO) test, 6-Minute Walk Test (6MWT), Cardiopulmonary Exercise Testing, etc. Physicians should strategically combine these tests into different clinical pathways according to the results of PFTs and clinical needs.Question 4: How to delineate responsibilities and implementation protocols for PFTs among different hospital departments?Recommendation 4: The attending physician who orders the PFT should follow up on the results and proceed with subsequent tests independently or by consulting with clinical specialists. If discrepancies are found, PFT laboratory staff should communicate in a timely and two-way manner to form a closed loop with the attending physician, including timely adjustment of test items, handling of emergencies, and explaining results to the patients. When necessary, PFT specialists should participate in clinical consultations. (Strong Recommendation)Question 5: How to develop the PFT pathways for health centers?Recommendation 5: Health centers should incorporate PFTs into routine physical examinations. For individuals aged ≥40 years, or those with high-risk factors for chronic respiratory diseases (such as a long-term smoking history, exposure to dusts, chronic respiratory symptoms, or a family history of chronic respiratory diseases), PFTs should be mandatory. After tests, the PFT results should be included in the health reports to provide information for health or referral recommendations. (Strong Recommendation)Question 6: How to establish PFT pathways based on imaging findings?Recommendation 6: For patients with abnormal radiographic findings such as emphysema, pulmonary fibrosis, or bronchiectasis, further PFTs should be recommended.① Keywords in manual or electronical imaging reports should be used to prompt the PFTs. Keywords for chest X-ray include: emphysema, pulmonary bullae, tracheal stenosis, bronchial wall thickening, mucous plugging, distal bronchiolar destruction, bronchiectasis, reticular and honeycomb-like lesions, interstitial fibrosis, chest wall deformity, etc. Keywords for CT imaging include: airway wall thickening,≥5% emphysema involvement (defined as areas with lung density below -950 HU), destruction or abnormal mucous plugging in distal bronchioles, atelectasis, giant lung mass, moderate or severe pleural effusion, etc. (Strong Recommendation)② Institutions with the requisite facilities are recommended to optimize the diagnosis and treatment of respiratory system diseases by integrating Artificial Intelligence (AI)-enhanced CT with PFTs. Using AI-CT for in-depth analysis of chest CT images can automatically assist in identifying lung lesions and, combined with PFT data, provide more accurate recommendations for clinical management. (Strong Recommendation)Question 7: How to enable the digital transmission of PFT data?Recommendation 7① PFT reports from different PFT analyzers should be integrated with HIS, picture archiving and communication system (PACS)and laboratory information system (LIS)to enable automated data synchronization. (Strong Recommendation)② Recommendations for information system access standards and conversion requirements: digital PFT reports should follow a unified and standardized format for HIS integration, including the patient's demographics, test indicators, data, images and the electronic signature of the operator or reviewing physician. If digital data cannot be integrated, images or PDFs of the reports can be uploaded to PACS. (Strong Recommendation)Question 8: How to establish a standardized PFT data platform within healthcare institutions?Recommendation 8① Integrate PFT data across all hospital departments to ensure real-time, accurate synchronization with the Hospital Information System (HIS). Records of the types, frequency and results of PFTs can be used to establish the unified, standardized PFT database for centralized management and analysis. (Strong Recommendation)② The platform should feature basic functions such as data storage, querying, statistics and quality control. It is recommended to embed a standardized quality assessment and grading system within electronic reports to assist clinicians in rapidly evaluating data quality and interpreting the reliability of results. (Strong Recommendation)③ Patients with confirmed chronic respiratory diseases should be integrated into the platform, or PFT data should be incorporated into existing patient management systems. This integration supports clinical research and hospital administration and enables the visual management of longitudinal pulmonary function data. (Strong Recommendation)Question 9: How to establish a Cloud-based Data Sharing Platform?Recommendation 9: Standards and specifications for uploading PFT data should be established to ensure data security and patient privacy. PFT data sharing among medical institutions within the region should be achieved through regional cloud storage to provide the data support for hierarchical diagnosis and treatment and telemedicine. In principle, patient information should be anonymized prior to being uploaded to regional sub-centers. Regional sub-centers are responsible for reviewing reports, revising conclusions, and assigning quality control ratings. The cloud data center should aggregate the quality control data from all sub-centers, and regularly evaluate and sample the sub-centers' quality control reports. (Strong Recommendation)Question 10: How to improve the quality control and the interpretation of PFTs?Recommendation 10① Quality control and interpretation of PFT results should be performed by physicians or technicians who have received specialized training in PFTs and possess relevant qualifications. In addition, AI can be utilized to achieve quality control and interpretation of pulmonary function test results, promoting the optimal allocation of medical resources and improving efficiency. (Strong Recommendation)② Implementation methods for AI-assisted PFTs: PFT equipment manufacturers should integrate AI-assisted PFT systems into instruments or cloud platforms. Using AI deep learning and other models to automatically identify abnormalities, the acceptability, repeatability and quality grading of PFT results can be intelligently interpreted, more accurately distinguishing types of ventilation dysfunction, improving diagnostic accuracy and efficiency, and achieving efficient automatic identification of abnormal populations. (Strong Recommendation)Currently, quality control and result interpretation of PFTs rely primarily on manual assessment, which is inefficient, subject to observer bias, and requires trained and qualified personnel. The introduction of AI technology can enable automatic quality control and assisted interpretation of PFT data, enhancing the efficiency and quality of PFTs.AI deep learning models based on large-scale standardized PFT data can perform real-time evaluation of data collection and curve quality during the testing process, issuing timely warnings and requesting re-testing for unqualified results. Acceptable PFT results can be used for further determining the abnormalities, assessing the type and severity of pulmonary function impairment, and intelligently generating real-time warnings, patient guidance and auxiliary interpretation, improving the quality control and interpretation efficiency of PFT results.Question 11: How to configure equipment and personnel for the PFT laboratory?Recommendation 11① Space configuration: A comfortable examination environment for patients should be provided by establishing PFT suites, ensuring reasonable spatial layout with proper ventilation and temperature/humidity control. (Strong Recommendation)② Hardware configuration: The PFT devices should be equipped according to the specific needs of the healthcare institution. The equipment should comply with industry technical specifications, undergo regular calibration and maintenance, and be documented in comprehensive equipment management logs. (Strong Recommendation)③ Personnel configuration: The full-time technical personnel should be assigned reasonably according to the equipment and workload needs, and should undergo the relevant training and pass the assessments. (Strong Recommendation)Question 12: How to improve the patient experience and PFT efficiency?Recommendation 12① By setting up self-service terminals for PFTs or using mobile applications, the process of registration, payment, appointment, check-in, and report viewing can be completed quickly, which helps improve the patient experience, reduce waiting time, and optimize workflow. (Strong Recommendation)② Instructional videos can visually help patients learn about the significance of PFTs, improve maneuver compliance, and adherence to precautions. (Strong Recommendation)③ Adopting augmented reality technology (e.g., head-mounted displays) helps facilitate rapid learning and improved testing efficiency. Providing an immersive interactive experience increases patient engagement and understanding, which helps to improve the implementation rate and efficiency of PFTs. (Strong Recommendation)Question 13: When conducting multidisciplinary PFTs, how to set the target testing rates for different departments?Recommendation 13: In addition to routine clinical application in the Department of Pulmonary and Critical Care Medicine (PCCM), PFTs are also significant in multiple departments such as Surgery, Cardiology and Geriatrics, and should be encouraged in high-risk populations. It is recommended that the proportion of PFTs ordered by clinical departments other than PCCM Department and Thoracic Surgery Department should constitute more than 30% of the total hospital-wide PFT volume. (Strong Recommendation)Question 14: How to set the assessment criteria for PFTs?Recommendation 14: Competency-based criteria for physicians and technicians performing PFTs should be established, encompassing both the quantity and quality of tests. Personnel who do not meet the standards should undergo re-training and re-assessment. (Strong Recommendation)This consensus is registered on the International Practice Guideline Registration and Transparency Platform:PREPARE-2025CN1382 DOI: 10.3760/cma.j.cn112147-20251114-00713.

Chronic Obstructive Pulmonary Disease (COPD), a major chronic respiratory disease with persistent airflow limitation, is a leading global cause of disability and mortality. Respiratory spirogram time series, routinely collected during pulmonary function tests (PFTs), play a critical role in the early detection of respiratory diseases and in monitoring lung function over time. However, most current AI models for COPD diagnosis are limited to outputting classification results without providing a rationale for their diagnostic process, while current Large Language Models (LLMs) cannot understand spirograms yet, which severely limits their clinical trust and adoption. To tackle this challenge, we leverage a cohort of 234,028 individuals from the UK Biobank (UKB) to propose SpiroLLM, the first multimodal large language model that can understand spirogram. The model extracts morphological features from respiratory curves via a SpiroEncoder and aligns them with PFT numerical values in a unified latent space using a SpiroProjector, ultimately empowering a large language model to generate a comprehensive diagnostic report. Experimental results confirm that SpiroLLM achieved a diagnostic AUROC of 0.8977 (95% CI: 0.88-0.91). In a robustness test with missing core data, it maintained a 100% valid response rate, far surpassing the 13.4% of a text-only model and showcasing the superiority of its multimodal design. This work demonstrates the substantial potential of deeply fusing physiological signals with large language models, establishing a new paradigm for the next generation of interpretable and reliable clinical decision support tools.

Objective.Accurate lung function assessment is essential for diagnosing and managing diseases like chronic obstructive pulmonary disorder, pulmonary emboli, and lung cancer. Single-photon emission computed tomography (SPECT) provides valuable 3D functional imaging of ventilation and perfusion, but is limited by low spatial resolution, availability, additional radiation, and cost. Alternative methods, including CT-based perfusion (CT-P) and deep learning models, require large datasets to validate results that are often scarce. Pulmonary function tests (PFTs) offer rapid and noninvasive global lung function measures and are clinically widely used. While ventilation correlates well with PFTs, perfusion imaging presents challenges due to complex blood flow and difficulty summarizing 3D data into one value. Additionally, commonly employed percentile scaling removes absolute quantitative information, complicating interpretation.Approach.We propose a framework leveraging lung discretizations based on Voronoi diagrams to capture local spatial information from raw-valued and percentile-scaled perfusion maps (SPECT and CT-P). We compute hierarchical descriptive statistics at 3 levels (intra-subvolume, inter-subvolume, left-right lungs) to derive one global value per patient.Main results.Across PFT measures of diffusing capacity of lungs for carbon monoxide, forced expiratory volume after one second (FEV1), and FEV1/forced vital capacity, we find that discretizing perfusion maps into Voronoi subvolumes always yields stronger Spearman correlations than not discretizing. Specifically, our approach demonstrates strong correlations of0.636⩽ρ⩽0.843(P < 0.005) for raw-valued (SPECT and CT-P) maps,0.590⩽ρ⩽0.789(P < 0.005) for percentile-scaled maps, and reliably distinguishes normal from abnormal lung function via logistic regression analysis (0.865⩽AUC⩽0.937for raw-valued maps,0.877⩽AUC⩽0.933for percentile-scaled maps).Significance.This framework bridges regional perfusion imaging and global pulmonary function assessment, enabling meaningful quantitative comparisons between SPECT and CT-P maps. By preserving local spatial variability, the method offers a noninvasive tool for integrating imaging and physiological data, paving the way toward broader clinical and AI-driven applications in lung function evaluation.

Impulse oscillometry system (IOS) is an effort-independent pulmonary function testing technique that assesses respiratory system mechanics during tidal breathing and has gained increasing interest as a complementary modality to traditional pulmonary function tests (PFTs), while its physiological relevance and clinical role alongside conventional tests remain incompletely defined. This narrative literature review synthesises evidence on the correlations between IOS parameters and traditional PFT indices, summarises clinical applications across major chronic respiratory diseases, and discusses emerging developments including artificial intelligence-based approaches; the review is based on studies published since 2010 that compare IOS with spirometry, body plethysmography, and diffusing capacity of the lung for carbon monoxide (DLCO) in chronic obstructive pulmonary disease (COPD), asthma, and interstitial lung disease (ILD). Correlation studies indicate that IOS resistance-related parameters are more closely associated with obstructive ventilatory impairment, particularly in COPD and asthma, whereas reactance-related parameters show stronger associations with restrictive ventilatory abnormalities, lung volumes, and diffusion impairment, especially in ILD. Across disease entities, R5-20 and resonant frequency consistently demonstrate higher sensitivity for detecting small airway dysfunction. Clinically, IOS provides value in assessing bronchodilator responsiveness, evaluating asthma control, detecting small airway involvement when spirometry is preserved, and monitoring exposure-related airway effects. Artificial intelligence-based models integrating multidimensional IOS data further highlight its potential in disease screening and classification. Overall, IOS provides complementary physiological and clinical information beyond traditional PFTs, and further large-scale, multicentre studies and methodological standardisation are needed to support its broader clinical implementation.

Sarcoidosis is a systemic disease of unknown etiology, characterized by granulomatous inflammation in various organs. Elevated serum Immunoglobulin G4 (IgG4) levels are typically associated with IgG4-related disease (IgG4-RD), characterized by plasma cell infiltration and fibrosis, but their role in sarcoidosis remains unclear. Given the fibro-inflammatory nature of both conditions, this study aimed to describe a cohort of sarcoidosis patients with elevated IgG4 levels and explore the potential clinical relevance of this finding. We retrospectively analyzed biopsy-confirmed sarcoidosis cases with measured serum IgG4 levels. Clinical data were gathered, including demographics, organ involvement, pulmonary function tests (PFTs), sarcoidosis-associated pulmonary hypertension (SAPH), and markers of autoimmunity. The study cohort included ten patients with a mean age of 50 ± 15 years, comprising six males and seven Black individuals. Two patients reported a family history of sarcoidosis; the mean body mass index (BMI) was 28.5 ± 5.7. Five patients were never smokers, and four were former smokers. Eight patients had lung involvement, two with advanced Scadding stage 4, four with airflow obstruction, and two with restriction physiology on PFT. Out of seven patients with echocardiography, five had SAPH. Other organ manifestation included ocular disease in three patients, joint involvement in two, liver disease in two, and heart disease in one. Autoimmune markers were present in five patients. These findings suggest a potential overlap between IgG4-RD and sarcoidosis. Further research is needed to clarify IgG4's role in sarcoidosis and whether elevated serum IgG4 identifies a subset of patients who may benefit from B-cell depletion therapies.

Bronchoscopic lung volume reduction (BLVR) is an established treatment for patients with severe chronic obstructive pulmonary disease (COPD), characterized as emphysema and hyperinflation, but current methods of patient selection and lobe identification are limited due to invasiveness, lack of specificity, and poor regional ventilation and perfusion assessments. This study aimed to evaluate lobar changes in ventilation, perfusion, and volume before and after endobronchial valve (EBV) placement using SPECT/CT imaging in patients with severe emphysema, and to assess their association with clinical outcomes. A prospective, single-center, pilot study enrolled six patients with severe COPD (FEV1 ≤ 50%), and hyperinflation on pulmonary function tests (PFTs). Additionally, participants had severe heterogeneous emphysema (≥ 70% with voxel density ≤ - 910 Hounsfield units), and adequate fissure integrity (≥ 95%), both quantified by StratX™. All participants underwent BLVR in a single target lobe and received pre- and post-procedure SPECT/CT imaging using nebulized [99m]Tc inhaled in the upright position to assess ventilation followed by [99m]Tc-macroaggregated albumin injection to assess perfusion. Ventilation, perfusion, and volume were quantified at the target, adjacent, and contralateral lobes using AutoLung 3D software. PFTs and six-minute walk tests (6MWT) were obtained before and after BLVR. Treated lobes demonstrated a significant reduction in ventilation (66.7% [IQR 47.1-88.2]), perfusion (71.9% [63.1-78.8]), and volume (26.2% [10.4-35.3]) after BLVR. Adjacent lobes exhibited compensatory increases in ventilation (16.7% [8.5-51.3]), perfusion (+ 22.9% [19.7-33.9]), and volume (+ 12.6% [8.0-17.4]), while changes in contralateral lobes were minimal. These physiological changes weakly correlated with FEV1 and 6MWT, which may suggest a disconnect between regional physiological adaptation and functional capacity. While prior studies have evaluated baseline physiology with SPECT/CT prior to BLVR, this is the first study to use this novel imaging to assess physiological responses after BLVR. SPECT/CT is a promising tool for evaluating lobar-level physiological responses to BLVR. Larger, multicenter studies with long-term follow-up are needed to validate its utility in patient selection, procedural planning, and outcome prediction following BLVR.

Though a normal forced vital capacity (FVC) is typically thought to imply the absence of restriction, recent data suggest that restriction may in fact be common among patients with normal spirometry. However, the clinical significance of restriction with normal spirometry is unknown.

Background Pulmonary function tests (PFTs) are the clinical standard for diagnosing chronic obstructive pulmonary disease (COPD) but face limitations in accessibility and complexity. Dynamic chest radiography (DCR) is an emerging technique that enables dynamic pulmonary function assessment with low radiation exposure, suggesting potential as an alternative to PFTs. However, its diagnostic validity remains unproven. Purpose To determine whether DCR is an efficacious alternative approach to PFTs for COPD screening. Materials and Methods This prospective single-center observational study enrolled participants with or without COPD from November 2022 to July 2024. Correlations between DCR parameters and PFT indexes were assessed using Pearson correlation. Receiver operating characteristic analysis was used to evaluate diagnostic performance. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to select features and build and train predictive models, which were tested on an internal test set. A nomogram was used to visualize COPD probability, calibrated using the Hosmer-Lemeshow test. Results This study included 553 participants (median age, 60 years [IQR, 53-67 years]; 355 men; 198 women; 191 with COPD, 362 controls). DCR parameters were positively correlated with PFT indexes in participants with COPD: The combined bilateral (right and left lungs) rate of change in projected lung area (ΔPLA) during deep breathing was correlated with forced expiratory volume in 1 second (FEV1) percent predicted (r = 0.650; P < .001) and the ratio of FEV1 to forced vital capacity (r = 0.638; P < .001). The diagnostic performance of bilateral ΔPLA during deep breathing was high in the internal test set (area under the receiver operating characteristic curve [AUC], 0.78 [95% CI: 0.72, 0.85]). Three DCR features were selected to construct the DCR model, which performed well in the internal test set (AUC, 0.82 [95% CI: 0.75, 0.89]). The combined model incorporating a DCR parameter and smoking status demonstrated high diagnostic performance (internal test set: AUC, 0.85 [95% CI: 0.78, 0.92]). Conclusion A DCR-based nomogram demonstrated robust diagnostic performance in identifying COPD, suggesting that DCR represents an efficacious alternative approach to standard PFTs for COPD screening. ClinicalTrials.gov Identifier: NCT06653413 © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Hatabu et al in this issue.

This study aimed to evaluate the correlation between the %LAV-950 threshold method and a deep learning-based algorithm for emphysema quantification in chest computed tomography (CT) scans with pulmonary function test (PFT) parameters. This retrospective study included 101 chronic obstructive pulmonary disease patients who underwent chest CT and PFTs to assess FEV₁ and carbon monoxide transfer coefficient (KCO). Emphysema was quantified using the %LAV-950 method and a deep learning-based algorithm. Quantitative results were obtained from lung and soft tissue reconstruction kernels. Pearson correlation coefficients were calculated to assess associations between CT metrics and PFT parameters. Permutation testing was used to compare correlation coefficients. The %LAV-950 method showed a weak but significant correlation with FEV₁ using soft tissue kernel (r = -0.281; P = .005), while no significant association was observed with KCO. The deep learning-based method demonstrated stronger and more consistent correlations: a moderate correlation with FEV₁ using lung kernel (r = -0.303; P = .005), a weak correlation with FEV₁ using soft tissue kernel (r = -0.289; P = .006), and a moderate correlation with KCO using soft tissue kernel (r = -0.404; P = .014). Permutation testing confirmed a significant difference in the correlation with KCO between both methods using soft tissue kernel (r = -0.264; P = .025). The deep learning-based approach demonstrated stronger and more consistent correlations with pulmonary function parameters than the traditional %LAV-950 method, especially for soft tissue kernel reconstructions. These findings highlight its potential to provide a more accurate quantification of emphysema and support its integration into routine CT analysis and artificial intelligence-assisted diagnostic workflows.