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Am J Respir Crit Care Med
Am J Respir Crit Care Med
ajrccm
American Journal of Respiratory and Critical Care Medicine
1073-449X
1535-4970
American Thoracic Society

202404-0777LE
10.1164/rccm.202404-0777LE
Correspondence
Reply to van der Sar et al.: Classifying Interstitial Lung Disease: Omics Are in the Air
Huang Yong
https://orcid.org/0000-0002-3058-9480
Ma Shwu-Fan
https://orcid.org/0000-0001-9846-5030
Noth Imre
Division of Pulmonary and Critical Care Medicine, University of Virginia, Charlottesville, Virginia
Correspondence and requests for reprints should be addressed to Imre Noth, M.D., Division of Pulmonary & Critical Care Medicine, Department of Medicine, University of Virginia, PO Box 800546, Charlottesville, VA 22908-0546. Email: in2c@uvahealth.org.
3 5 2024
1 9 2024
3 5 2024
210 5 691692
Copyright © 2024 by the American Thoracic Society
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0. For commercial usage and reprints, please e-mail Diane Gern (dgern@thoracic.org).
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pmcFrom the Authors:

We thank van der Sar and colleagues for their valuable discussion of our work using machine learning models of plasma proteomics to differentiate patients with idiopathic pulmonary fibrosis and connective tissue disease–associated interstitial lung disease (1). Artificial intelligence (AI) in medicine has entered an exciting period because such algorithms are capable of analyzing large datasets to enhance diagnosis and treatment decisions (2). By training AI models on omics data, researchers can identify intricate patterns within complex medical information. However, omics application in AI medicine faces significant challenges, and we greatly agree with van der Sar and colleagues that a key aspect is being capable of moving these discoveries into practical application.

Effectively representing the diverse clinical heterogeneity observed in patient populations is a major challenge. Addressing this challenge requires robust methods to capture the broad spectrum of clinical variations. Our study incorporated data from 42 medical centers with 1,247 patients with idiopathic pulmonary fibrosis and 352 patients with connective tissue disease–associated interstitial lung disease for recursive feature elimination, 37-protein classifier selection, and model training and testing, ensuring generalizability (1).

Omics studies often require high dimensionality and extensive cohort analyses. However, routine clinical practice necessitates individualized patient diagnosis. We developed a pipeline for single-sample classification. The iterative classification of single samples, followed by composite scoring methods across all four machine learning models, established a single-patient diagnosis model that mimicked clinical practice settings, we hope bridging a gap often encountered in cohort studies. Decision curve analysis revealed that various machine learning models outperformed sex and age across different clinical thresholds, highlighting the need to move the 37-protein classifier to the fully quantitative platform for practical application (1).

We concur with van der Sar and colleagues’ assertion that electronic nose (eNose) technology holds promise as a diagnostic tool for interstitial lung disease (ILD) (1). Our current model aims to complement the multidisciplinary team’s decision-making process for a diagnosis. The performance of breathomics based on the eNose also shows such promise. In Moor and colleagues’ study, eNose technology completely distinguished 322 patients with ILD from 48 healthy control subjects (3). This high-throughput measure of >2,000 volatile organic compounds using eNose technology is noninvasive and has low cost (4), making it attractive for large-scale screening of patients for early diagnosis and longitudinal monitoring of clinical courses if it proves generalizable.

Such tools should be complementary because integrating breathomics and proteomics may provide a more comprehensive tool for early diagnosis, subclassification, and monitoring clinical courses for patients with ILD. However, each data type has its unique format, scaling, and inherent limitations. Integrating and analyzing omics data from these diverse sources requires robust normalization and transformation techniques. Other challenges include high dimensionality and overcoming missing data across different assays. Even after successful integration, identifying the biological meaning behind the complex relationships uncovered in the multiomics data can be challenging. Researchers must bridge the gap between statistical associations and understanding the underlying biological mechanisms.

Integrated clinical multiomics data from blood, plasma, or breath offer immense potential for personalized medicine. Acceptance of these promising novel approaches to enhancing diagnoses requires the arduous steps of conversion to platforms that are practical on larger scales enabling multiple replications to ensure repeatability needed to build confidence among clinicians. As a community, we must value this in addition to the novel discoveries unlocked by these methods and strive to move these exciting approaches forward. Overcoming these challenges is essential to ensure reliable, interpretable, and ethically sound results that can translate into improved patient care. Despite these challenges, omics data have the potential to revolutionize AI medicine, and we expect to see even more breakthroughs in this field in the future.

Supported by NIH grant UG3HL145266.

Originally Published in Press as DOI: 10.1164/rccm.202404-0777LE on May 3, 2024

Author disclosures are available with the text of this letter at www.atsjournals.org.
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References

1. Huang Y Ma SF Oldham JM Adegunsoye A Zhu D Murray S et al. Machine learning of plasma proteomics classifies diagnosis of interstitial lung disease Am J Respir Crit Care Med 2024 210
2. Sharma A Lysenko A Jia S Boroevich KA Tsunoda T Advances in AI and machine learning for predictive medicine J Hum Genet 2024 10.1038/s10038-024-01231-y
3. Moor CC Oppenheimer JC Nakshbandi G Aerts JGJV Brinkman P Maitland-van der Zee AH et al. Exhaled breath analysis by use of eNose technology: a novel diagnostic tool for interstitial lung disease Eur Respir J 2021 57 2002042 32732331
4. van der Sar IG Wijsenbeek MS Moor CC Exhaled breath analysis in interstitial lung disease Curr Opin Pulm Med 2023 29 443 450 37405699
