1
|
Selvarajah B, Platé M, Chambers RC. Pulmonary fibrosis: Emerging diagnostic and therapeutic strategies. Mol Aspects Med 2023; 94:101227. [PMID: 38000335 DOI: 10.1016/j.mam.2023.101227] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/02/2023] [Indexed: 11/26/2023]
Abstract
Fibrosis is the concluding pathological outcome and major cause of morbidity and mortality in a number of common chronic inflammatory, immune-mediated and metabolic diseases. The progressive deposition of a collagen-rich extracellular matrix (ECM) represents the cornerstone of the fibrotic response and culminates in organ failure and premature death. Idiopathic pulmonary fibrosis (IPF) represents the most rapidly progressive and lethal of all fibrotic diseases with a dismal median survival of 3.5 years from diagnosis. Although the approval of the antifibrotic agents, pirfenidone and nintedanib, for the treatment of IPF signalled a watershed moment for the development of anti-fibrotic therapeutics, these agents slow but do not halt disease progression or improve quality of life. There therefore remains a pressing need for the development of effective therapeutic strategies. In this article, we review emerging therapeutic strategies for IPF as well as the pre-clinical and translational approaches that will underpin a greater understanding of the key pathomechanisms involved in order to transform the way we diagnose and treat pulmonary fibrosis.
Collapse
Affiliation(s)
- Brintha Selvarajah
- Oncogenes and Tumour Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Manuela Platé
- Department of Respiratory Medicine (UCL Respiratory), Division of Medicine, University College London, UK
| | - Rachel C Chambers
- Department of Respiratory Medicine (UCL Respiratory), Division of Medicine, University College London, UK.
| |
Collapse
|
2
|
Suman G, Koo CW. Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases. J Thorac Imaging 2023; 38:S7-S18. [PMID: 37015833 DOI: 10.1097/rti.0000000000000705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
Interstitial lung disease (ILD) is a heterogeneous group of disorders with complex and varied imaging manifestations and prognosis. High-resolution computed tomography (HRCT) is the current standard-of-care imaging tool for ILD assessment. However, visual evaluation of HRCT is limited by interobserver variation and poor sensitivity for subtle changes. Such challenges have led to tremendous recent research interest in objective and reproducible methods to examine ILDs. Computer-aided CT analysis to include texture analysis and machine learning methods have recently been shown to be viable supplements to traditional visual assessment through improved characterization and quantification of ILDs. These quantitative tools have not only been shown to correlate well with pulmonary function tests and patient outcomes but are also useful in disease diagnosis, surveillance and management. In this review, we provide an overview of recent computer-aided tools in diagnosis, prognosis, and longitudinal evaluation of fibrotic ILDs, while outlining some of the pitfalls and challenges that have precluded further advancement of these tools as well as potential solutions and further endeavors.
Collapse
Affiliation(s)
- Garima Suman
- Division of Thoracic Imaging, Mayo Clinic, Rochester, MN
| | | |
Collapse
|
3
|
Lv W, Mao H, Ruan Y, Li S, Shimizu K, Zhang L, Zhang C. Identification and immunological characterization of PLA2G2A and cell death-associated molecular clusters in idiopathic pulmonary fibrosis. Life Sci 2023; 331:122071. [PMID: 37673297 DOI: 10.1016/j.lfs.2023.122071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/31/2023] [Accepted: 09/03/2023] [Indexed: 09/08/2023]
Abstract
AIMS Idiopathic pulmonary fibrosis (IPF) is a severe pulmonary interstitial pneumonia. Our study focuses on the role of PLA2 enzyme in the IPF to explore a more effective diagnosis and treatment mechanism of IPF. MAIN METHODS Transcriptome data of IPF from GEO database and bleomycin-induced pulmonary fibrosis mice were analyzed to identify PLA2 enzyme and their metabolite, lysophosphatidylcholines 18:0, in IPF. Based on PLA2G2A and PLA2G2D / PLA2G2A-associated cell death genes (PCDs), the consensus clustering analysis was used to identify the subtypes of IPF and the correlation between PLA2G2A and prognosis was analyzed. The machine learning (ML) models and artificial neural network (ANN) model was used to validate the diagnostic accuracy of PLA2s and PCDs in diagnosing IPF. The gene and protein expression of NLRP3, GSDMD, and CASP-1 was estimated in recombinant PLA2G2A protein induced MLE-12 cells. KEY FINDINGS The expression of PLA2G2D, PLA2G2A, and LPC18 significantly changed in IPF. Furtherly, PLA2G2A has a significant correlation with poor patient prognosis, which could predict the 2 or 3-years mortality rates of IPF. Two subtypes of IPF patients, identified based on PCDs, showed significant different immunoinfiltration. Recombinant PLA2G2A protein could induce the pyrotosis in the MLE-12 cell. The generalized linear model and ANN model of PLA2s or PCDs accurate diagnosis IPF. SIGNIFICANCE PLA2G2A is the most robustly associated gene with IPF among the PLA2s, which demonstrates a potential in diagnosing and prognostic value in IPF, and provides a foundation for further understanding and breakthroughs in IPF diagnosis and treatment.
Collapse
Affiliation(s)
- Weichao Lv
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Hongcai Mao
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Yang Ruan
- Laboratory of Systematic Forest and Forest Products Sciences, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka 819-0395, Japan
| | - Shuaiyu Li
- Saigo Laboratory, School of Information Science, Kyushu University, Fukuoka 819-0395, Japan
| | - Kuniyoshi Shimizu
- Laboratory of Systematic Forest and Forest Products Sciences, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka 819-0395, Japan
| | - Louqian Zhang
- Department of Thoratic Surgery, Nanjing Drum Tower Hospital, Medical School, Nanjing University, Nanjing, Jiangsu, China.
| | - Chaofeng Zhang
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China.
| |
Collapse
|
4
|
Karampitsakos T, Sotiropoulou V, Katsaras M, Tsiri P, Georgakopoulou VE, Papanikolaou IC, Bibaki E, Tomos I, Lambiri I, Papaioannou O, Zarkadi E, Antonakis E, Pandi A, Malakounidou E, Sampsonas F, Makrodimitri S, Chrysikos S, Hillas G, Dimakou K, Tzanakis N, Sipsas NV, Antoniou K, Tzouvelekis A. Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model. Front Med (Lausanne) 2023; 9:1083264. [PMID: 36733935 PMCID: PMC9886681 DOI: 10.3389/fmed.2022.1083264] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 12/30/2022] [Indexed: 01/19/2023] Open
Abstract
Introduction Post-acute sequelae of COVID-19 seem to be an emerging global crisis. Machine learning radiographic models have great potential for meticulous evaluation of post-COVID-19 interstitial lung disease (ILD). Methods In this multicenter, retrospective study, we included consecutive patients that had been evaluated 3 months following severe acute respiratory syndrome coronavirus 2 infection between 01/02/2021 and 12/5/2022. High-resolution computed tomography was evaluated through Imbio Lung Texture Analysis 2.1. Results Two hundred thirty-two (n = 232) patients were analyzed. FVC% predicted was ≥80, between 60 and 79 and <60 in 74.2% (n = 172), 21.1% (n = 49), and 4.7% (n = 11) of the cohort, respectively. DLCO% predicted was ≥80, between 60 and 79 and <60 in 69.4% (n = 161), 15.5% (n = 36), and 15.1% (n = 35), respectively. Extent of ground glass opacities was ≥30% in 4.3% of patients (n = 10), between 5 and 29% in 48.7% of patients (n = 113) and <5% in 47.0% of patients (n = 109). The extent of reticulation was ≥30%, 5-29% and <5% in 1.3% (n = 3), 24.1% (n = 56), and 74.6% (n = 173) of the cohort, respectively. Patients (n = 13, 5.6%) with fibrotic lung disease and persistent functional impairment at the 6-month follow-up received antifibrotics and presented with an absolute change of +10.3 (p = 0.01) and +14.6 (p = 0.01) in FVC% predicted at 3 and 6 months after the initiation of antifibrotic. Conclusion Post-COVID-19-ILD represents an emerging entity. A substantial minority of patients presents with fibrotic lung disease and might experience benefit from antifibrotic initiation at the time point that fibrotic-like changes are "immature." Machine learning radiographic models could be of major significance for accurate radiographic evaluation and subsequently for the guidance of therapeutic approaches.
Collapse
Affiliation(s)
| | - Vasilina Sotiropoulou
- Department of Respiratory Medicine, University General Hospital of Patras, Patras, Greece
| | - Matthaios Katsaras
- Department of Respiratory Medicine, University General Hospital of Patras, Patras, Greece
| | - Panagiota Tsiri
- Department of Respiratory Medicine, University General Hospital of Patras, Patras, Greece
| | | | | | - Eleni Bibaki
- Laboratory of Molecular and Cellular Pneumonology, Department of Thoracic Medicine, Medical School, University of Crete, Heraklion, Greece
| | - Ioannis Tomos
- 5th Department of Respiratory Medicine, Hospital for Thoracic Diseases, ‘SOTIRIA’, Athens, Greece
| | - Irini Lambiri
- Laboratory of Molecular and Cellular Pneumonology, Department of Thoracic Medicine, Medical School, University of Crete, Heraklion, Greece
| | - Ourania Papaioannou
- Department of Respiratory Medicine, University General Hospital of Patras, Patras, Greece
| | - Eirini Zarkadi
- Department of Respiratory Medicine, University General Hospital of Patras, Patras, Greece
| | | | - Aggeliki Pandi
- Department of Respiratory Medicine, Corfu General Hospital, Corfu, Greece
| | - Elli Malakounidou
- Department of Respiratory Medicine, University General Hospital of Patras, Patras, Greece
| | - Fotios Sampsonas
- Department of Respiratory Medicine, University General Hospital of Patras, Patras, Greece
| | - Sotiria Makrodimitri
- Department of Infectious Diseases-COVID-19 Unit, Laiko General Hospital, Athens, Greece
| | - Serafeim Chrysikos
- 5th Department of Respiratory Medicine, Hospital for Thoracic Diseases, ‘SOTIRIA’, Athens, Greece
| | - Georgios Hillas
- 5th Department of Respiratory Medicine, Hospital for Thoracic Diseases, ‘SOTIRIA’, Athens, Greece
| | - Katerina Dimakou
- 5th Department of Respiratory Medicine, Hospital for Thoracic Diseases, ‘SOTIRIA’, Athens, Greece
| | - Nikolaos Tzanakis
- Laboratory of Molecular and Cellular Pneumonology, Department of Thoracic Medicine, Medical School, University of Crete, Heraklion, Greece
| | - Nikolaos V. Sipsas
- Department of Infectious Diseases-COVID-19 Unit, Laiko General Hospital, Athens, Greece,Medical School, National and Kapodistrian University of Athens, Zografou, Greece
| | - Katerina Antoniou
- Laboratory of Molecular and Cellular Pneumonology, Department of Thoracic Medicine, Medical School, University of Crete, Heraklion, Greece
| | - Argyris Tzouvelekis
- Department of Respiratory Medicine, University General Hospital of Patras, Patras, Greece,*Correspondence: Argyris Tzouvelekis, ,
| |
Collapse
|
5
|
Zeng J, Li K, Cao F, Zheng Y. Development and validation of survival prediction model for gastric adenocarcinoma patients using deep learning: A SEER-based study. Front Oncol 2023; 13:1131859. [PMID: 36959782 PMCID: PMC10029996 DOI: 10.3389/fonc.2023.1131859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 02/21/2023] [Indexed: 03/09/2023] Open
Abstract
Background The currently available prediction models, such as the Cox model, were too simplistic to correctly predict the outcome of gastric adenocarcinoma patients. This study aimed to develop and validate survival prediction models for gastric adenocarcinoma patients using the deep learning survival neural network. Methods A total of 14,177 patients with gastric adenocarcinoma from the Surveillance, Epidemiology, and End Results (SEER) database were included in the study and randomly divided into the training and testing group with a 7:3 ratio. Two algorithms were chosen to build the prediction models, and both algorithms include random survival forest (RSF) and a deep learning based-survival prediction algorithm (DeepSurv). Also, a traditional Cox proportional hazard (CoxPH) model was constructed for comparison. The consistency index (C-index), Brier score, and integrated Brier score (IBS) were used to evaluate the model's predictive performance. The accuracy of predicting survival at 1, 3, 5, and 10 years was also assessed using receiver operating characteristic curves (ROC), calibration curves, and area under the ROC curve (AUC). Results Gastric adenocarcinoma patients were randomized into a training group (n = 9923) and a testing group (n = 4254). DeepSurv showed the best performance among the three models (c-index: 0.772, IBS: 0.1421), which was superior to that of the traditional CoxPH model (c-index: 0.755, IBS: 0.1506) and the RSF with 3-year survival prediction model (c-index: 0.766, IBS: 0.1502). The DeepSurv model produced superior accuracy and calibrated survival estimates predicting 1-, 3- 5- and 10-year survival (AUC: 0.825-0.871). Conclusions A deep learning algorithm was developed to predict more accurate prognostic information for gastric cancer patients. The DeepSurv model has advantages over the CoxPH and RSF models and performs well in discriminative performance and calibration.
Collapse
|
6
|
Wong A, Lu J, Dorfman A, McInnis P, Famouri M, Manary D, Lee JRH, Lynch M. Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design for Prediction of Pulmonary Fibrosis Progression From Chest CT Images. Front Artif Intell 2021; 4:764047. [PMID: 34805974 PMCID: PMC8596329 DOI: 10.3389/frai.2021.764047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 10/11/2021] [Indexed: 01/02/2023] Open
Abstract
Pulmonary fibrosis is a devastating chronic lung disease that causes irreparable lung tissue scarring and damage, resulting in progressive loss in lung capacity and has no known cure. A critical step in the treatment and management of pulmonary fibrosis is the assessment of lung function decline, with computed tomography (CT) imaging being a particularly effective method for determining the extent of lung damage caused by pulmonary fibrosis. Motivated by this, we introduce Fibrosis-Net, a deep convolutional neural network design tailored for the prediction of pulmonary fibrosis progression from chest CT images. More specifically, machine-driven design exploration was leveraged to determine a strong architectural design for CT lung analysis, upon which we build a customized network design tailored for predicting forced vital capacity (FVC) based on a patient's CT scan, initial spirometry measurement, and clinical metadata. Finally, we leverage an explainability-driven performance validation strategy to study the decision-making behavior of Fibrosis-Net as to verify that predictions are based on relevant visual indicators in CT images. Experiments using a patient cohort from the OSIC Pulmonary Fibrosis Progression Challenge showed that the proposed Fibrosis-Net is able to achieve a significantly higher modified Laplace Log Likelihood score than the winning solutions on the challenge. Furthermore, explainability-driven performance validation demonstrated that the proposed Fibrosis-Net exhibits correct decision-making behavior by leveraging clinically-relevant visual indicators in CT images when making predictions on pulmonary fibrosis progress. Fibrosis-Net is able to achieve a significantly higher modified Laplace Log Likelihood score than the winning solutions on the OSIC Pulmonary Fibrosis Progression Challenge, and has been shown to exhibit correct decision-making behavior when making predictions. Fibrosis-Net is available to the general public in an open-source and open access manner as part of the OpenMedAI initiative. While Fibrosis-Net is not yet a production-ready clinical assessment solution, we hope that its release will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon it.
Collapse
Affiliation(s)
- Alexander Wong
- Vision and Image Processing Research Group, University of Waterloo, Waterloo, ON, Canada
- Waterloo Artificial Intelligence Institute, University of Waterloo, Waterloo, ON, Canada
- DarwinAI Corp., Waterloo, ON, Canada
| | - Jack Lu
- DarwinAI Corp., Waterloo, ON, Canada
| | | | | | | | | | | | | |
Collapse
|
7
|
Karampitsakos T, Kalogeropoulou C, Tzilas V, Papaioannou O, Kazantzi A, Koukaki E, Katsaras M, Bouros E, Tsiri P, Tsirikos G, Zarkadi E, Ntoulias N, Sotiropoulou V, Efthymiou P, Chrysikos S, Malakounidou E, Sampsonas F, Bouros D, Tzouvelekis A. Safety and Effectiveness of Mycophenolate Mofetil in Interstitial Lung Diseases: Insights from a Machine Learning Radiographic Model. Respiration 2021; 101:262-271. [PMID: 34592744 DOI: 10.1159/000519215] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/13/2021] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION Treatment of interstitial lung diseases (ILDs) other than idiopathic pulmonary fibrosis (IPF) often includes systemic corticosteroids. Use of steroid-sparing agents is amenable to avoid potential side effects. METHODS Functional indices and high-resolution computed tomography (HRCT) patterns of patients with non-IPF ILDs receiving mycophenolate mofetil (MMF) with a minimum follow-up of 1 year were analyzed. Two independent radiologists and a machine learning software system (Imbio 1.4.2.) evaluated HRCT patterns. RESULTS Fifty-five (n = 55) patients were included in the analysis (male: 30 [55%], median age: 65.0 [95% CI: 59.7-70.0], mean forced vital capacity %predicted [FVC %pred.] ± standard deviation [SD]: 69.4 ± 18.3, mean diffusing capacity of lung for carbon monoxide %pred. ± SD: 40.8 ± 14.3, hypersensitivity pneumonitis: 26, connective tissue disease-ILDs [CTD-ILDs]: 22, other ILDs: 7). There was no significant difference in mean FVC %pred. post-6 months (1.59 ± 2.04) and 1 year (-0.39 ± 2.49) of treatment compared to baseline. Radiographic evaluation showed no significant difference between baseline and post-1 year %ground glass opacities (20.0 [95% CI: 14.4-30.0] vs. 20.0 [95% CI: 14.4-25.6]) and %reticulation (5.0 [95% CI: 2.0-15.6] vs. 7.5 [95% CI: 2.0-17.5]). A similar performance between expert radiologists and Imbio software analysis was observed in assessing ground glass opacities (intraclass correlation coefficient [ICC] = 0.73) and reticulation (ICC = 0.88). Fourteen patients (25.5%) reported at least one side effect and 8 patients (14.5%) switched to antifibrotics due to disease progression. CONCLUSION Our data suggest that MMF is a safe and effective steroid-sparing agent leading to disease stabilization in a proportion of patients with non-IPF ILDs. Machine learning software systems may exhibit similar performance to specialist radiologists and represent fruitful diagnostic and prognostic tools.
Collapse
Affiliation(s)
| | | | - Vasilios Tzilas
- First Academic Department of Pneumonology, Medical School, National and Kapodistrian University of Athens, Athens, Greece.,Athens Medical Center, Athens, Greece
| | - Ourania Papaioannou
- Department of Respiratory Medicine, University Hospital of Patras, Patras, Greece
| | | | - Evangelia Koukaki
- First Academic Department of Pneumonology, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Matthaios Katsaras
- Department of Respiratory Medicine, University Hospital of Patras, Patras, Greece
| | - Evangelos Bouros
- First Academic Department of Pneumonology, Medical School, National and Kapodistrian University of Athens, Athens, Greece.,Athens Medical Center, Athens, Greece
| | - Panagiota Tsiri
- Department of Respiratory Medicine, University Hospital of Patras, Patras, Greece
| | - Georgios Tsirikos
- Department of Respiratory Medicine, University Hospital of Patras, Patras, Greece
| | - Eirini Zarkadi
- Department of Respiratory Medicine, University Hospital of Patras, Patras, Greece
| | - Nikolaos Ntoulias
- Department of Respiratory Medicine, University Hospital of Patras, Patras, Greece
| | | | - Panagiotis Efthymiou
- Department of Respiratory Medicine, University Hospital of Patras, Patras, Greece
| | - Serafeim Chrysikos
- 5th Department of Pneumonology, Hospital for Thoracic Diseases "SOTIRIA,", Athens, Greece
| | - Elli Malakounidou
- Department of Respiratory Medicine, University Hospital of Patras, Patras, Greece
| | - Fotios Sampsonas
- Department of Respiratory Medicine, University Hospital of Patras, Patras, Greece
| | - Demosthenes Bouros
- First Academic Department of Pneumonology, Medical School, National and Kapodistrian University of Athens, Athens, Greece.,Athens Medical Center, Athens, Greece
| | - Argyrios Tzouvelekis
- Department of Respiratory Medicine, University Hospital of Patras, Patras, Greece
| |
Collapse
|