Safety and Effectiveness of Mycophenolate Mofetil in Interstitial Lung Diseases: Insights from a Machine Learning Radiographic Model

Karampitsakos, Theodoros; Kalogeropoulou, Christina; Tzilas, Vasilios; Papaioannou, Ourania; Kazantzi, Alexandra; Koukaki, Evangelia; Katsaras, Matthaios; Bouros, Evangelos; Tsiri, Panagiota; Tsirikos, Georgios; Zarkadi, Eirini; Ntoulias, Nikolaos; Sotiropoulou, Vasilina; Efthymiou, Panagiotis; Chrysikos, Serafeim; Malakounidou, Elli; Sampsonas, Fotios; Tzouvelekis, Argyrios

doi:10.1159/000519215

Cited by 9 publications

(11 citation statements)

References 44 publications

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“…High-Resolution Computed Tomography scans were evaluated through the validated machine learning software, named Imbio Lung Texture Analysis version 2.1 ( 14 ). Imbio Lung Texture Analysis had the following technical requirements for HRCT analysis: Minimal movement and acceptable position, slice thickness <2.0 mm, revolution time <1 s, pixel spacing <2.0 mm, and slice spacing <2.0 mm.…”

Section: Methodsmentioning

confidence: 99%

“…Meticulous radiographic evaluation might have a cardinal role for the guidance of therapeutic approaches in this new entity with unknown long-term effects. Toward this direction, we performed patients’ radiographic evaluation through a validated machine learning software system, denominated Imbio Lung Texture Analysis ( 14 ). Machine learning represents a subgroup of artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

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Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model

et al. 2023

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IntroductionPost-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).MethodsIn 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.ResultsTwo 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.ConclusionPost-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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model

et al. 2023

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“…was used to evaluate HRCT in patients with non-IPF ILDs receiving mycophenolate mofetil. The software demonstrated similar performance to specialist radiologists, indicating its potential as a valuable diagnostic and prognostic tool (ICC 0.73 vs 0.88) 39. In addition to providing diagnostic support, some studies also focus on the early detection or prediction of progressive fibrotic lung disease.…”

Section: Discussionmentioning

confidence: 95%

Novel 3D-based deep learning for classification of acute exacerbation of idiopathic pulmonary fibrosis using high-resolution CT

Huang,

Si,

et al. 2024

BMJ Open Resp Res

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PurposeAcute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) is the primary cause of death in patients with IPF, characterised by diffuse, bilateral ground-glass opacification on high-resolution CT (HRCT). This study proposes a three-dimensional (3D)-based deep learning algorithm for classifying AE-IPF using HRCT images.Materials and methodsA novel 3D-based deep learning algorithm, SlowFast, was developed by applying a database of 306 HRCT scans obtained from two centres. The scans were divided into four separate subsets (training set, n=105; internal validation set, n=26; temporal test set 1, n=79; and geographical test set 2, n=96). The final training data set consisted of 1050 samples with 33 600 images for algorithm training. Algorithm performance was evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve and weighted κ coefficient.ResultsThe accuracy of the algorithm in classifying AE-IPF on the test sets 1 and 2 was 93.9% and 86.5%, respectively. Interobserver agreements between the algorithm and the majority opinion of the radiologists were good (κw=0.90 for test set 1 and κw=0.73 for test set 2, respectively). The ROC accuracy of the algorithm for classifying AE-IPF on the test sets 1 and 2 was 0.96 and 0.92, respectively. The algorithm performance was superior to visual analysis in accurately diagnosing radiological findings. Furthermore, the algorithm’s categorisation was a significant predictor of IPF progression.ConclusionsThe deep learning algorithm provides high auxiliary diagnostic efficiency in patients with AE-IPF and may serve as a useful clinical aid for diagnosis.

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“…In addition, ML and DL have been applied as supportive tools in lung fibrosis radiology (i.e., high-resolution CT), resulting as pivotal and low-cost innovations that may overcome the absence of expertise in thoracic imaging. Due to the impressive and emerging role of ML and DL, they may have a crucial role in decision-making and problem-solving in many medical fields [27, 28].…”

Section: Discussionmentioning

confidence: 99%

The Impact of Telemedicine during Severe Acute Respiratory Syndrome Coronavirus 2 Pandemic and Future Perspectives: A Systematic Review

Monteleone,

Terzulli,

Cefaloni

et al. 2023

Respiration

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Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread worldwide since December 2019, causing the COVID-19 pandemic. Several measures have taken place in many countries to avoid further spread of the virus and to manage infected people according to disease severity. Notably, telemedicine (TM) was successfully used to manage less severe patients. Our aim was to assess the impact and the edges of using TM in home-isolated or hospitalized patients affected by SARS-CoV-2 infection and its further application. Methods: We performed a systematic review according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, focusing on randomized controlled trials (RCTs) published in English and available on PubMed database. Full texts were blindly reviewed and then assessed according to PICO model. Results: Our research identified a total of 1,959 records, of which 24 were potentially eligible through the articles full-text review. Six papers were included for data extraction and 18 articles were excluded: 10 articles were not RCTs and 8 articles did not involve SARS-CoV-2 patients. The TM application showed an improvement in psychological stress, mental disorders, and a significant reduction of general stress in patients affected by SARS-CoV-2 infection. The effectiveness of using TM in rehabilitative respiratory programs has been also reported. Furthermore, the benefits of TM application in tailored monitoring of vital parameters in home-isolated patients helped clinicians to early identify a deterioration of clinical conditions. Conclusion: The use of TM during COVID-19 pandemic represented a novel, intriguing, versatile, and useful tool to support clinical practice. This evidence suggests considering TM in a wider range of clinical applications.

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Safety and Effectiveness of Mycophenolate Mofetil in Interstitial Lung Diseases: Insights from a Machine Learning Radiographic Model

Cited by 9 publications

References 44 publications

Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model

Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model

Novel 3D-based deep learning for classification of acute exacerbation of idiopathic pulmonary fibrosis using high-resolution CT

The Impact of Telemedicine during Severe Acute Respiratory Syndrome Coronavirus 2 Pandemic and Future Perspectives: A Systematic Review

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