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Authors Lee CW, Wilkinson KH, Sheka AC, Leverson GE, Kennedy GD
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Journal Oncologist Volume: 21 Issue: 4 Pages: 425-32
Publish Date 2016 Apr
PubMed ID 26975865
PMC ID 4828122
Abstract

The log odds of positive lymph nodes (LODDS) is an empiric transform formula that incorporates positive and negative lymph node data into a single ratio for prognostic utility. We sought to determine the value of the log odds ratio as a prognostic indicator compared with established lymph node indices in advanced-stage rectal cancer patients who have undergone curative resection.Retrospective analysis of rectal cancer operations from 1995 to 2013 identified all stage III cancer patients who underwent curative resection. Patients were stratified into three groups according to calculated lymph node ratios (LNRs) and log odds ratios (LODDS). The relationship between LNR, LODDS, and 5-year overall survival (OS) were assessed.OS for all patients was 81.4%. Both LNR and LODDS stratifications identified differences in 5-year OS. LODDS stratification was significantly associated with OS (p = .04). Additional significant clinicopathologic demographic variables included sex (p = .02), venous invasion (p = .02), tumor location (p < .001), and receipt of adjuvant chemotherapy (p = .047). LODDS separated survival among patients in the low LNR group (LNR1).This study confirms that the measure of lymph node involvement transformed by the log odds ratio is a suitable predictor of 5-year overall survival in stage III rectal cancer. LODDS may be applied to stratify high-risk patients in the management of adjuvant therapy.Traditionally, clinicians have relied solely on the total number of positive lymph nodes affected when determining patient prognosis in rectal cancer. However, the current staging strategy does not account for “high-risk,” biologically aggressive tumors that fall into the same risk categories as less clinically aggressive tumors. The log odds of positive lymph nodes is a logistic transform formula that uses pathologic lymph node data to stratify survival differences among patients within a single stage of disease. This formula allows clinicians to identify whether patients with clinically aggressive tumors fall into higher-risk groups, providing additional insight into how to better counsel patients and manage postoperative therapies.

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