Integrated evaluation of clinical, pathological and radiological prognostic factors in squamous cell carcinoma of the lung

PLoS One. 2019 Oct 4;14(10):e0223298. doi: 10.1371/journal.pone.0223298. eCollection 2019.

Abstract

Objective: Little is known about prognostic factors for lung squamous cell carcinoma (SCC). We aimed to explore radiologic and clinical factors affecting prognosis and to compare the prognosis of both central and peripheral lung SCCs.

Materials and methods: Radiologic, clinical, and pathologic profiles of surgically confirmed SCCs from 382 patients were retrospectively reviewed. Tumor location, enhancement, necrosis, the presence of obstructive pneumonitis/atelectasis and underlying lung disease were evaluated on chest CT examination. Age, pulmonary function, tumor marker, and cancer stage were also assessed. Univariate and multivariate Cox regression analyses were performed to identify any correlation to overall survival (OS) and disease-free survival (DFS). Hazard rate estimation and competing risk analysis were done to evaluate recurrence pattern.

Results: The median follow-up period was 56.2 months. Tumors were located centrally in 230 patients (60.2%) and peripherally in 152 patients (39.8%). Age (p = 0.002, hazard ratio [HR] 1.03, 95% confidence interval [CI] = [1.01, 1.06]) and interstitial lung abnormalities (ILAs) (p<0.001, HR 5.41, 95% CI = [3.08, 9.52]) were associated with poor OS on multivariate analysis. ILAs also had a strong association to DFS (p<0.001, HR 4.25, 95% CI = [3.08, 9.52]). Central cancers had two peaks of local recurrence development at 15 and 60 months after surgery, and peripheral tumors showed rising curves for metastasis development at 60 months.

Conclusions: CT-determined ILAs are a strong biomarker predicting poor outcome. Prognosis may not vary according to tumor location, but the two groups exhibited different recurrence patterns.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Biomarkers
  • Biopsy
  • Carcinoma, Squamous Cell / diagnosis*
  • Carcinoma, Squamous Cell / mortality*
  • Female
  • Humans
  • Image Processing, Computer-Assisted
  • Kaplan-Meier Estimate
  • Lung Neoplasms / diagnosis*
  • Lung Neoplasms / mortality*
  • Male
  • Middle Aged
  • Multimodal Imaging
  • Neoplasm Grading
  • Neoplasm Staging
  • Prognosis
  • Proportional Hazards Models
  • Radiography / methods
  • Sensitivity and Specificity

Substances

  • Biomarkers

Grants and funding

This research was supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), which was funded by Ministry of Health & Welfare (HI17C0086) and National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIP; Ministry of Science, ICT & Future Planning) (No. NRF-2016R1A2B4013046 and NRF-2017M2A2A7A02018568) to HYL.