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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

202311-2161LE
10.1164/rccm.202311-2161LE
Correspondence
Comprehensive Strategies for the Follow-Up of Interstitial Lung Abnormality
Ni Lei 1 3
Sun Ye 2
Zhou Jian Ping 1
https://orcid.org/0000-0001-8128-6319
Li Qing Yun 1
1 Department of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China;
2 Department of Respiratory and Critical Care Medicine, Wuxi Branch of Ruijin Hospital, Shanghai Jiao Tong University, Wuxi, China; and
3 Department of Respiratory and Critical Care Medicine, People’s Hospital of Mojiang Hani Autonomous County, Yunnan, China
Correspondence and requests for reprints should be addressed to Qing Yun Li, M.D., Ph.D., Department of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China. Email: liqingyun68@hotmail.com.
12 6 2024
1 9 2024
12 6 2024
210 5 692693
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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pmcTo the Editor:

The current understanding of interstitial lung abnormalities (ILAs) is still in the exploratory stage, and, according to the Fleischner Society position paper on ILAs in 2020 (1), ILA is defined as a radiological term but also requires clinical exclusion of patients at high risk for interstitial lung disease (ILD) (e.g., patients with connective tissue disease, and family history of ILD). Currently, the mainstream view is that ILAs may be an early manifestation of ILD, with a correlation of respiratory symptoms, impaired lung function, disease progression, and an increased risk of death (2). It is recommended that patients with one or more clinical or radiological risk factors be actively monitored with pulmonary function tests and computed tomographic (CT) scans. Radiological examinations are beneficial for pulmonologists in timely identification of significant cases of ILD and further clinical management (3). Notably, Park and colleagues (4) conducted a valuable retrospective clinical study on the imaging follow-up protocol and risk stratification for ILAs. The study’s results suggest that CT follow-up at 3-year intervals is more suitable for individuals without risk factors. However, for patients with high-risk factors, such as quantified fibrotic ILAs or manifestations of honeycombing lung, the follow-up interval should be appropriately shortened. Notably, the study faced the challenge of screening and enrolling participants with ILAs for retrospective analysis. Out of 4,659 initially screened participants, thoracic radiologists identified 305 patients with ILAs or suspected ILAs, excluding those with pneumoconiosis and a confirmed history of ILD. The rigorous inclusion protocol enhances the study’s reliability. This innovative approach holds clinical utility, and the use of artificial intelligence (AI) software to establish risk thresholds for ILA progression informs the follow-up protocol based on these threshold values. The study underscores the growing role of AI, particularly deep learning from big data models, in ILA management. A deep learning algorithm of CT imaging analysis has shown promise in providing reliable outcome prediction in patients with progressive fibrotic lung disease (5). Chae and colleagues (6) reported high sensitivity and specificity in detecting ILAs using deep learning–based texture analysis. The integration of AI in ILD research is poised to open new horizons, revolutionizing both initial diagnosis and follow-up procedures.

The increasing incidence of incidentally detected ILAs as a result of health checkups and lung cancer screening highlights the need for high-quality clinical studies. Thus, several aspects of this retrospective study need to be noted. First, standardized imaging evaluation processes are crucial for ILA diagnosis. The Fleischner Society’s position paper on ILAs identifies imaging manifestations but emphasizes the need to exclude CT signs misdiagnosed as interstitial lesions, such as dependent abnormalities, inadequate inspiration, postabsorption changes, smoking-related centrilobular nodules, and so forth. Retrospective studies may struggle to fully account for these confounders in CT imaging. Second, the lack of a clear criterion for defining ILA and ILD in study inclusion poses challenges. Although ILA is primarily an imaging term, ILD diagnosis requires a multifactorial approach. The Fleischner Society defines ILA on the basis of nondependent abnormalities affecting more than 5% of any lung zone, but a future reliance on deep learning for ILD quantification could aid in differentiation. Third, ongoing studies need to address the patients with a smoking history, being a high-risk factor for the development of ILAs. Because 77.4% of the patients enrolled in the study had a history of smoking, it may be helpful to analyze the relationship between smoking and the progression or natural remission of ILAs as a result of smoking cessation during the follow-up process. Fourth, stratifying the management of patients with ILA merely on the basis of imaging factors may lead to delayed diagnosis in some patients. Future research may be directed toward designing an ILA risk factor rating scale that includes age, smoking history, history of environmental exposures, medication use, clinical symptoms, lung function, biomarkers, and imaging features and then formulating different follow-up management programs based on the scores. Fifth, it has been confirmed that advanced age and tobacco exposure are high-risk factors for lung cancer. Given that the mean age of patients in this study reached 65 years and a high proportion of patients had a smoking history, even for the low-risk group, the 3-year follow-up interval is debatable, and an excessively long interval may pose a disadvantage to early screening for lung cancer.

In summary, several unanswered questions about ILAs need to be addressed, such as the genetic characteristics of the ILA population, immune status, susceptibility to lung infections, and tumor incidence in the ILA population, all of which need to be explored in the future. Comprehensive management should be conducted in patients with ILA, considering symptoms, imaging features, pulmonary function and laboratory tests, and other risk factors. For patients with ILA, a personalized follow-up plan should be established. In the event of disease progression, a comprehensive re-evaluation should be conducted, and an appropriate intervention plan should be formulated accordingly.

Supported by the Wuxi Taihu Lake Talent Plan - High-level Medical Talents Project (RJWX-001).

Originally Published in Press as DOI: 10.1164/rccm.202311-2161LE on June 12, 2024

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

1. Hatabu H Hunninghake GM Richeldi L Brown KK Wells AU Remy-Jardin M et al. Interstitial lung abnormalities detected incidentally on CT: a position paper from the Fleischner Society Lancet Respir Med 2020 8 726 737 32649920
2. Putman RK Gudmundsson G Axelsson GT Hida T Honda O Araki T et al. Imaging patterns are associated with interstitial lung abnormality progression and mortality Am J Respir Crit Care Med 2019 200 175 183 30673508
3. Hata A Hino T Yanagawa M Nishino M Hida T Hunninghake GM et al. Interstitial lung abnormalities at CT: subtypes, clinical significance, and associations with lung cancer Radiographics 2022 42 1925 1939 36083805
4. Park S Choe J Hwang HJ Noh HN Jung YJ Lee JB et al. Long-term follow-up of interstitial lung abnormality: implication in follow-up strategy and risk thresholds Am J Respir Crit Care Med 2023 208 858 867 37590877
5. Walsh SLF Mackintosh JA Calandriello L Silva M Sverzellati N Larici AR et al. Deep learning-based outcome prediction in progressive fibrotic lung disease using high-resolution computed tomography Am J Respir Crit Care Med 2022 206 883 891 35696341
6. Chae KJ Lim S Seo JB Hwang HJ Choi H Lynch D et al. Interstitial lung abnormalities at CT in the Korean National Lung Cancer Screening Program: prevalence and deep learning-based texture analysis Radiology 2023 307 e222828 37097142
