Log odds of positive lymph nodes as a novel prognostic indicator in NSCLC staging. (March 2017)

• Record Type: Journal Article
• Authors: Dziedzic, Dariusz; Piotr, Rudzinski; Langfort, Renata; Orlowski, Tadeusz
• Is Part Of: Surgical oncology. Volume 26:Number 1(2017)
• Page Start: 80
• Page End: 85
• Publication Date: 2017-03
• Subjects: Lung cancer -- Lymph node ratio -- Log odds; Cancer -- Surgery -- Periodicals; Neoplasms -- surgery -- Periodicals; Cancer -- Chirurgie -- Périodiques; Electronic journals; 616.994059
• DOI: 10.1016/j.suronc.2017.01.004
• Languages: English
• ISSNs: 0960-7404

Abstract

Abstract: Objectives: The paper aimed to compare the efficacy of log odds (LODDS) compared to a classification based on the distribution of involved lymph nodes (pN) and lymph node ratio (LNR). Methods: Material was collected retrospectively from an online survey-based database of the Polish Lung Cancer Group and included a group of 17, 369 patients who received radical surgical treatment (R0) due to lung cancer. Results: In the whole group the median survival for N0, N1 and N2 was 76.1, 41.7 and 24.2 months, respectively. The median survival for individual LODDS categories (−6, -4], (−4, -3], (−3, -2], (−2, -1], (−1, 0], (0, 1] and (1, 2] was 76.5, 76.3, 71.7, 45.4, 25.0, 19.1 and 17.7 months, respectively. The median survival for LNR in individual categories (0), (0, 0.25], (0.25, 05], (0.5075] and (0.75, 1.0] was 75.6, 40.3, 24.1, 18.8 and 16.4 months, respectively. A multi-variant analysis demonstrated that each LODDS category is an independent prognostic factor: (−4, -3] (HR = 0.982; 95% CI 0.867–1.112; P = 0.775), (−3, -2] (HR = 1.114; 95% CI 0.984–1.262; P = 0.089), (−2, -1] (HR = 1.241; 95% CI 1.080–1.425; P = 0.002), (−1, 0] (HR = 1.617; 95% CI 1.385–1.887; P < 0.0001), (0, 1] (HR = 1.918; 95% CI 1.579–2.329; P < 0.0001) and (1, 2] (HR = 2.016; 95% CI 1.579–2.573; P < 0.0001). Conclusions: Based on LODDS it is possible to discriminate patients with regard to lung cancer stage more effectively compared to pN and LNR classification, and it is also a better classification system.