Predicting clinical outcome with phenotypic clusters using quantitative CT fibrosis and emphysema features in patients with idiopathic pulmonary fibrosis

A lung structure and function information-guided residual diffusion model for predicting idiopathic pulmonary fibrosis progression

Idiopathic Pulmonary Fibrosis (IPF) is a progressive lung disease that continuously scars and thickens lung tissue, leading to respiratory difficulties. Timely assessment of IPF progression is essential for developing treatment plans and improving patient survival rates. However, current clinical standards require multiple (usually two) CT scans at certain intervals to assess disease progression. This presents a dilemma: the disease progression is identified only after the disease has already progressed. To address this issue, a feasible solution is to generate the follow-up CT image from the patient's initial CT image to achieve early prediction of IPF. To this end, we propose a lung structure and function information-guided residual diffusion model. The key components of our model include (1) using a 2.5D generation strategy to reduce computational cost of generating 3D images with the diffusion model; (2) designing structural attention to mitigate negative impact of spatial misalignment between the two CT images on generation performance; (3) employing residual diffusion to accelerate model training and inference while focusing more on differences between the two CT images (i.e., the lesion areas); and (4) developing a CLIP-based text extraction module to extract lung function test information and further using such extracted information to guide the generation. Extensive experiments demonstrate that our method can effectively predict IPF progression and achieve superior generation performance compared to state-of-the-art methods.

A systematic review of the role of quantitative CT in the prognostication and disease monitoring of interstitial lung disease

BACKGROUND

The unpredictable trajectory and heterogeneity of interstitial lung disease (ILDs) make prognostication challenging. Current prognostic indices and outcome measures have several limitations. Quantitative computed tomography (qCT) provides automated numerical assessment of CT imaging and has shown promise when applied to the prognostication and disease monitoring of ILD. This systematic review aims to highlight the current evidence underpinning the prognostic value of qCT in predicting outcomes in ILD.

METHODS

A comprehensive search of four databases (Medline, EMCare, Embase and CINAHL (Cumulative Index to Nursing and Allied Health Literature)) was conducted for studies published up to and including 22 November 2024. A modified CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) checklist was used for data extraction. The risk of bias was assessed using a Quality in Prognostic Studies template.

RESULTS

The search identified 1134 unique studies, of which 185 studies met inclusion and exclusion criteria. Commonly studied ILD subtypes included idiopathic pulmonary fibrosis (41%, n=75), mixed subtypes (26%, n=48) and systemic sclerosis ILD (16%, n=30). Numerous studies showed significant prognostic signals, even when adjusted for common covariates and/or significant correlation between serial qCT biomarkers and conventional outcome measures. Heterogenous and nonstandardised reporting methods meant that direct comparison or meta-analysis of studies was not possible. Studies were limited by the use of retrospective methodology without prospective validation and significant study attrition.

DISCUSSION

qCT has shown efficacy in the prognostication and disease monitoring of a range of ILDs. Hurdles exist to widespread adoption including governance concerns, appropriate algorithm anchoring and standardisation of image acquisition. International collaboration is underway to address these hurdles, paving the way for regulatory approval and ultimately patient benefit.

Correlation between CT-based phenotypes and serum biomarker in interstitial lung diseases

BACKGROUND

The quantitative analysis of computed tomography (CT) and Krebs von den Lungen-6 (KL-6) serum level has gained importance in the diagnosis, monitoring, and prognostication of interstitial lung disease (ILD). However, the associations between quantitative analysis of CT and serum KL-6 level remain poorly understood.

METHODS

In this retrospective observational study conducted at tertiary hospital between June 2020 and March 2022, quantitative analysis of CT was performed using the deep learning-based method including reticulation, ground glass opacity (GGO), honeycombing, and consolidation. We investigated the associations between CT-based phenotypes and serum KL-6 measured within three months of the CT scan. Furthermore, we evaluated the performance of the combined CT-based phenotypes and KL-6 levels in predicting hospitalizations due to respiratory reasons of ILD patients.

RESULTS

A total of 131 ILD patients (104 males) with a median age of 67 years were included in this study. Reticulation, GGO, honeycombing, and consolidation extents showed a positive correlation with KL-6 levels. [Reticulation, correlation coefficient (r) = 0.567, p < 0.001; GGO, r = 0.355, p < 0.001; honeycombing, r = 0.174, p = 0.046; and consolidation, r = 0.446, p < 0.001]. Additionally, the area under the ROC of the combined reticulation and KL-6 for hospitalizations due to respiratory reasons was 0.810 (p < 0.001).

CONCLUSIONS

Quantitative analysis of CT features and serum KL-6 levels ascertained a positive correlation between the two. In addition, the combination of reticulation and KL-6 shows potential for predicting hospitalizations of ILD patients due to respiratory causes. The combination of reticulation, focusing on phenotypic change in lung parenchyma, and KL-6, as an indicator of lung injury extent, could be helpful for monitoring and predicting the prognosis of various types of ILD.

Clustering analysis of HRCT parameters measured using a texture-based automated system: relationship with clinical outcomes of IPF

PURPOSE

The extent of honeycombing and reticulation predict the clinical prognosis of IPF. Emphysema, consolidation, and ground glass opacity are visible in HRCT scans. To date, there have been few comprehensive studies that have used these parameters. We conducted automated quantitative analysis to identify predictive parameters for clinical outcomes and then grouped the subjects accordingly.

METHODS

CT images were obtained while patients held their breath at full inspiration. Parameters were analyzed using an automated lung texture quantification system. Cluster analysis was conducted on 159 IPF patients and clinical profiles were compared between clusters in terms of survival.

RESULTS

Kaplan-Meier analysis revealed that survival rates declined as fibrosis, reticulation, honeycombing, consolidation, and emphysema scores increased. Cox regression analysis revealed that reticulation had the most significant impact on survival rate, followed by honeycombing, consolidation, and emphysema scores. Hierarchical and K-means cluster analyses revealed 3 clusters. Cluster 1 (n = 126) with the lowest values for all parameters had the longest survival duration, and relatively-well preserved FVC and DLCO. Cluster 2 (n = 15) with high reticulation and consolidation scores had the lowest FVC and DLCO values with a predominance of female, while cluster 3 (n = 18) with high honeycombing and emphysema scores predominantly consisted of male smokers. Kaplan-Meier analysis revealed that cluster 2 had the lowest survival rate, followed by cluster 3 and cluster 1.

CONCLUSION

Automated quantitative CT analysis provides valuable information for predicting clinical outcomes, and clustering based on these parameters may help identify the high-risk group for management.

MCAFNet: multiscale cross-layer attention fusion network for honeycomb lung lesion segmentation

Accurate segmentation of honeycomb lung lesions from lung CT images plays a crucial role in the diagnosis and treatment of various lung diseases. However, the availability of algorithms for automatic segmentation of honeycomb lung lesions remains limited. In this study, we propose a novel multi-scale cross-layer attention fusion network (MCAFNet) specifically designed for the segmentation of honeycomb lung lesions, taking into account their shape specificity and similarity to surrounding vascular shadows. The MCAFNet incorporates several key modules to enhance the segmentation performance. Firstly, a multiscale aggregation (MIA) module is introduced in the input part to preserve spatial information during downsampling. Secondly, a cross-layer attention fusion (CAF) module is proposed to capture multiscale features by integrating channel information and spatial information from different layers of the feature maps. Lastly, a bidirectional attention gate (BAG) module is constructed within the skip connection to enhance the model's ability to filter out background information and focus on the segmentation target. Experimental results demonstrate the effectiveness of the proposed MCAFNet. On the honeycomb lung segmentation dataset, the network achieves an Intersection over Union (IoU) of 0.895, mean IoU (mIoU) of 0.921, and mean Dice coefficient (mDice) of 0.949, outperforming existing medical image segmentation algorithms. Furthermore, experiments conducted on additional datasets confirm the generalizability and robustness of the proposed model. The contribution of this study lies in the development of the MCAFNet, which addresses the lack of automated segmentation algorithms for honeycomb lung lesions. The proposed network demonstrates superior performance in accurately segmenting honeycomb lung lesions, thereby facilitating the diagnosis and treatment of lung diseases. This work contributes to the existing literature by presenting a novel approach that effectively combines multi-scale features and attention mechanisms for lung lesion segmentation. The code is available at https://github.com/Oran9er/MCAFNet .

The applications of CT with artificial intelligence in the prognostic model of idiopathic pulmonary fibrosis

TAKE HOME MESSAGE

The review summarizes the applications of CT and AI algorithms for prognostic models in IPF and procedures of model construction. It reveals the current limitations and prospects of AI-aid models, and helps clinicians to recognize the AI algorithms and apply them to more clinical work.

Walking the path of treatable traits in interstitial lung diseases

Interstitial lung diseases (ILDs) are complex and heterogeneous diseases. The use of traditional diagnostic classification in ILD can lead to suboptimal management, which is worsened by not considering the molecular pathways, biological complexity, and disease phenotypes. The identification of specific "treatable traits" in ILDs, which are clinically relevant and modifiable disease characteristics, may improve patient's outcomes. Treatable traits in ILDs may be classified into four different domains (pulmonary, aetiological, comorbidities, and lifestyle), which will facilitate identification of related assessment tools, treatment options, and expected benefits. A multidisciplinary care team model is a potential way to implement a "treatable traits" strategy into clinical practice with the aim of improving patients' outcomes. Multidisciplinary models of care, international registries, and the use of artificial intelligence may facilitate the implementation of the "treatable traits" approach into clinical practice. Prospective studies are needed to test potential therapies for a variety of treatable traits to further advance care of patients with ILD.

Potential predictive value of CT radiomics features for treatment response in patients with COVID-19

INTRODUCTION

This study aims to explore the predictive value of CT radiomics and clinical characteristics for treatment response in COVID-19 patients.

METHODS

Data were collected from clinical/auxiliary examinations and follow-ups of COVID-19 patients. Whole lung radiomics feature extraction was performed at baseline chest CT. Radiomics, clinical, and combined features (nomogram) were evaluated for predicting treatment response.

RESULTS

Among 36 COVID-19 patients, mild, common, severe, and critical disease symptoms were found in 1, 21, 13, and 1 of them, respectively. Twenty-five (1 mild, 18 common, and 6 severe) patients showed a good response to treatment and 11 poor/fair responses. The clinical classification (p = 0.025) and serum creatinine (p = 0.010) on admission and small area emphasis (p = 0.036) from radiomics analysis significantly differed between the two groups. Predictive models were constructed based on the radiomics, clinical features, and nomogram showing an area under the curve of 0.651, 0.836, and 0.869, respectively. The nomogram achieved good calibration.

CONCLUSION

This new, non-invasive, and low-cost prediction model that combines the radiomics and clinical features is useful for identifying COVID-19 patients who may not respond well to treatment.

Syndrome of Combined Pulmonary Fibrosis and Emphysema: An Official ATS/ERS/JRS/ALAT Research Statement

Background: The presence of emphysema is relatively common in patients with fibrotic interstitial lung disease. This has been designated combined pulmonary fibrosis and emphysema (CPFE). The lack of consensus over definitions and diagnostic criteria has limited CPFE research. Goals: The objectives of this task force were to review the terminology, definition, characteristics, pathophysiology, and research priorities of CPFE and to explore whether CPFE is a syndrome. Methods: This research statement was developed by a committee including 19 pulmonologists, 5 radiologists, 3 pathologists, 2 methodologists, and 2 patient representatives. The final document was supported by a focused systematic review that identified and summarized all recent publications related to CPFE. Results: This task force identified that patients with CPFE are predominantly male, with a history of smoking, severe dyspnea, relatively preserved airflow rates and lung volumes on spirometry, severely impaired DlCO, exertional hypoxemia, frequent pulmonary hypertension, and a dismal prognosis. The committee proposes to identify CPFE as a syndrome, given the clustering of pulmonary fibrosis and emphysema, shared pathogenetic pathways, unique considerations related to disease progression, increased risk of complications (pulmonary hypertension, lung cancer, and/or mortality), and implications for clinical trial design. There are varying features of interstitial lung disease and emphysema in CPFE. The committee offers a research definition and classification criteria and proposes that studies on CPFE include a comprehensive description of radiologic and, when available, pathological patterns, including some recently described patterns such as smoking-related interstitial fibrosis. Conclusions: This statement delineates the syndrome of CPFE and highlights research priorities.

Quantitative CT in mortality prediction in pulmonary fibrosis with or without emphysema

Aim:

We aimed to evaluate the quantitative CT analysis of patients with CPFE in comparison with IPF and emphysema.

Methods:

Patients with CPFE(n:36), IPF(n:38) and emphysema(n:32) were retrospectively included in the study with the approval of the ethics committee.

Results:

There was a positive correlation between total lung volume and FVC%, TLCO% and 6 MWT, and negative correlation between mMRC and mortality. Negative correlation was found between right, left lung density and FVC%, TLCO% and 6 MWT, and positive correlation between mortality. Also, total lung volume, right and left lung densities were significant in predicting mortality and cut-off values are ≤3831,> -778 and> -775, respectively (p = 0.040, 0.020, 0.013).

Conclusion:

Quantitative CT are guiding in predicting mortality of the disease.

Computer-Aided quantitative analysis in interstitial lung diseases - A pictorial review using CALIPER

Computer-based quantitative computed tomography analysis has a growing role in the clinical evaluation, prognosis, and longitudinal management of diffuse parenchymal diseases. It provides improved characterization and quantification of disease. The pulmonary vessel-related structure score is a purely computer-based parameter that cannot be evaluated by the human eye and allows us to prognosticate outcomes in patients with fibrosing interstitial lung disease.

COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda

Chronic Obstructive Pulmonary Disease (COPD) is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, clinicians have classified patients sharing certain symptomatic characteristics, such as symptom intensity and history of exacerbations, into distinct phenotypes. In recent years, the growing use of machine learning algorithms, and cluster analysis in particular, has promised to advance this classification through the integration of additional patient characteristics, including comorbidities, biomarkers, and genomic information. This combination would allow researchers to more reliably identify new COPD phenotypes, as well as better characterize existing ones, with the aim of improving diagnosis and developing novel treatments. Here, we systematically review the last decade of research progress, which uses cluster analysis to identify COPD phenotypes. Collectively, we provide a systematized account of the extant evidence, describe the strengths and weaknesses of the main methods used, identify gaps in the literature, and suggest recommendations for future research.