Background
Given the difficulty of preoperative diagnosis of lymph node metastasis, patients with Stage I–III non-small cell lung cancer (NSCLC) are likely to be included in the clinical N1 (cN1) group. However, better treatment options may be selected through further stratification. This study aimed to identify preoperative clinicopathological prognostic and stratification factors for patients with cN1 NSCLC.
Methods
This retrospective evaluated 60 patients diagnosed with NSCLC from 2004 to 2014. Clinical nodal status had been evaluated using routine chest computed tomography (CT) and/or positron emission tomography (PET). To avoid the bias of the fluorodeoxyglucose uptake values based on inter-institution or inter-model differences, we historically used only two different models for PET instruments (one PET and one PET/CT). Relapse-free survival (RFS) and overall survival (OS) were the primary study outcomes. The maximum standardized uptake value (SUVmax) was calculated for the tumors and categorized as low or high based on the median value. Patient sex, age, histology, tumor size, and tumor markers were also assessed.
Results
Poor OS was associated with older age (P = 0.0159) and high SUVmax values (P = 0.0142). Poor RFS was associated with positive carcinoembryonic antigen (CEA) expression (P = 0.0035) and high SUVmax values (P = 0.015). Multivariate analyses confirmed that poor OS was independently predicted by older age (hazard ratio [HR] = 2.751, confidence interval [CI]: 1.300-5.822; P = 0.0081) and high SUVmax values (HR = 5.121, 95% CI: 1.759–14.910; P = 0.0027). Furthermore, poor RFS was independently predicted by positive CEA expression (HR = 2.376, 95% CI: 1.056–5.348; P = 0.0366) and high SUVmax values (HR = 2.789, 95% CI: 1.042–7.458; P = 0.0410). The primary tumor’s SUVmax value was also an independent prognostic factor for both OS and RFS.
Conclusions
For patients with cN1 NSCLC, preoperative prognosis and stratification might be performed based on the CEA expression, age, and the primary tumor’s SUVmax. In particular, regarding SUVmax, minimizing the bias between facilities and models could lead to a more accurate prognosis.