Machine learning assisted evaluation of interstitial lung diseases

Chi Wan Koo

doi:10.1121/1.5101137

Prognostic importance of Kidney, Heart and Interstitial lung diseases (KHI triad) in PH: A machine learning study

Archives of Cardiovascular Diseases ◽

10.1016/j.acvd.2020.05.011 ◽

2020 ◽

Vol 113 (10) ◽

pp. 630-641

Author(s):

Charles Fauvel ◽

Olivier Raitière ◽

Nassima Si Belkacem ◽

Stéphane Dominique ◽

Elise Artaud-Macari ◽

...

Keyword(s):

Machine Learning ◽

Lung Diseases ◽

Interstitial Lung Diseases ◽

Learning Study ◽

Prognostic Importance

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

Respiration ◽

10.1159/000519215 ◽

2021 ◽

pp. 1-10

Author(s):

Theodoros Karampitsakos ◽

Christina Kalogeropoulou ◽

Vasilios Tzilas ◽

Ourania Papaioannou ◽

Alexandra Kazantzi ◽

...

Keyword(s):

Machine Learning ◽

Mycophenolate Mofetil ◽

Lung Diseases ◽

Interstitial Lung Diseases ◽

Ground Glass ◽

Similar Performance ◽

Significant Difference ◽

Hrct Patterns ◽

Learning Software ◽

Steroid Sparing

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.

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