Cancer Res Treat.  2022 Jul;54(3):744-752. 10.4143/crt.2021.772.

Predictive Value of Interstitial Lung Abnormalities for Postoperative Pulmonary Complications in Elderly Patients with Early-stage Lung Cancer

  • 1Lung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Korea
  • 2Department of Radiology, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Korea
  • 3Department of Internal Medicine, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Korea
  • 4Department of Thoracic and Cardiovascular Surgery, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Korea
  • 5Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University–Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea


Identifying pretreatment interstitial lung abnormalities (ILAs) is important because of their predictive value for complications after lung cancer treatment. This study aimed to assess the predictive value of ILAs for postoperative pulmonary complications (PPCs) in elderly patients undergoing curative resection for early-stage non-small cell lung cancer (NSCLC).
Materials and Methods
Elderly patients (age ≥ 70 years) who underwent curative resection for pathologic stage I or II NSCLC with normal preoperative spirometry results (pre-bronchodilator forced expiratory volume in 1 s to forced vital capacity [FVC] ratio > 0.70 and FVC ≥ 80% of the predicted value) between January 2012 and December 2019 were retrospectively identified. Univariable and multivariable regression analyses were performed to assess risk factors for PPCs. The Kaplan–Meier method and log-rank test were used to analyze the relationship between ILAs and postoperative mortality. One-way analysis of variance was performed to assess the correlation between ILAs and hospital stay duration.
A total of 262 patients (median age, 73 [interquartile range, 71–76] years; 132 male) were evaluated. A multivariable logistic regression model revealed that, among several relevant risk factors, fibrotic ILAs independently predicted both overall PPCs (adjusted odds ratio [OR], 4.84; 95% confidence interval [CI], 1.35–17.38; p=0.016) and major PPCs (adjusted OR, 8.72; 95% CI, 1.71–44.38; p=0.009). Fibrotic ILAs were significantly associated with higher postoperative mortality and longer hospital stay (F=5.21, p=0.006).
Pretreatment fibrotic ILAs are associated with PPCs, higher postoperative mortality, and longer hospital stay.


Interstitial lung abnormality; Postoperative pulmonary complication; Early-stage non-small cell lung neoplasms; Preoperative risk stratification; Elderly patients


  • Fig. 1 Subclassification of interstitial lung abnormalities (ILAs) according to the Fleischner Society classification system. (A) Nonsubpleural ILA: high-resolution computed tomography (CT) scan showing ground-glass abnormality with nonsubpleural distribution (white arrows) in both lower lungs. (B) Subpleural nonfibrotic ILA: high-resolution CT scan showing ground-glass abnormality (arrowheads) and mild traction bronchiectasis (white arrow) with subpleural distribution in both lower lungs. There is no evidence of fibrosis. (C) Subpleural fibrotic ILA: high-resolution CT scan showing honeycombing (black arrow), traction bronchiectasis (white arrow), and nonemphysematous cyst (arrowhead) with architectural distortion in both lower lobes.

  • Fig. 2 Flowchart of patient selection and study inclusion and exclusion criteria. a)A pre-bronchodilator forced expiratory volume in 1 second to forced vital capacity (FVC) ratio of > 0.70 and FVC ≥ 80% of the predicted value.

  • Fig. 3 Histogram of postoperative pulmonary complications. Some patients had two or more complications. ARDS, acute respiratory distress syndrome; BPF, bronchopleural fistula; ILA, interstitial lung abnormality.

  • Fig. 4 Graph of length of hospital stay according to interstitial lung abnormality (ILA) subtypes. SD, standard deviation; (A) < (C), p=0.022.



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