ArticleMedicine2015
The Prognostic Ability of Log Odds of Positive Lymph Nodes in Oral Cavity Squamous Cell Carcinoma.
Article in Medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- Prognostic Implications of the Number of Lymph Node Metastases in Oral Tongue Squamous Cell Carcinoma: A Population Study of the SEER Database and an Institutional Registry.Cancer medicine · 2024Article
- Evaluation of the prognostic performance of different cutoff values of lymph node ratio staging system for stage III colorectal cancer.Updates in surgery · 2024Article
- Lymph Node Ratio as a Prognostic Factor in Neck Dissection in Oral Cancer Patients: A Systematic Review and Meta-Analysis.Cancers · 2022Review
- Towards an Improved Pathological Node Classification for Prognostic Stratification of Patients With Oral Cavity Squamous Cell Carcinoma: Results From a Nationwide Registry Study.Frontiers in oncology · 2022Article
- Article
- Article
- Log Odds Could Better Predict Survival in Muscle-Invasive Bladder Cancer Patients Compared with pN and Lymph Node Ratio.Journal of Cancer · 2019Article
- Recommendation for incorporation of a different lymph node scoring system in future AJCC N category for oral cancer.Scientific reports · 2017Article
- Which is the most suitable lymph node predictor for overall survival after primary surgery of head and neck cancer: pN, the number or the ratio of positive lymph nodes, or log odds?Journal of cancer research and clinical oncology · 2016Article
- Log odds of positive lymph nodes as an independent predictor of overall survival in oral squamous cell carcinoma.Journal of oral and maxillofacial pathology : JOMFPArticle
- A 3 M Evaluation Protocol for Examining Lymph Nodes in Cancer Patients: Multi-Modal, Multi-Omics, Multi-Stage Approach.Technology in cancer research & treatmentReview
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, log odds of positive lymph nodes (LODDS) was proven a better prediction of outcomes than other methods in gastric cancer, pancreatic cancer, and colon cancer. However, the validity is not yet tested in oral cavity squamous cell carcinoma (OSCC). We conducted a retrospective study to compare the predictive ability of LODDS, traditional pN classification and lymph node ratio (rN) in OSCC patients.In total, 347 OSCC patients receiving surgery with or without adjuvant therapy at the time of diagnosis between 2004 and 2013 were identified from the cancer registry database of the Dalin Tzu Chi Hospital. Cox proportional hazards models were used to compare the disease-specific survival (DSS) rates for pN, rN, and LODDS after adjusting for possible confounding risk factors. The discriminatory ability of different classification systems was evaluated using the adjusted hazard ratio and Akaike information criterion (AIC) by multivariate regression model. The prediction accuracy of the model was assessed by Harrell's c-statistic.The 347 OSCC patients had a mean age of 57 years old. Among them, 322 patients (92.8%) were male and 189 patients (54.5%) were in stages III to IV. LODDS showed better discriminatory ability for patients with <5 pathological cervical metastatic nodes and those with rN < 0.2. The hypothetical T-LODDS-M staging system had higher linear trend Chi-square, lower AIC, and higher prediction accuracy compared with the American Joint Committee on Cancer (AJCC) TNM, or hypothetical T-rN-M system. After adjusting for other factors, the LODDS unfavorable group had the highest adjusted hazard ratio (HR, 5.42; 95% confidence interval [CI], 3.19-9.12) and LODDS-based model lowest AIC of 704, comparing with pN and rN-based model. The LODDS-based system had the highest prediction accuracy for 3-year DSS (Harrell's c-statistic, 0.803).In our series, LODDS shows great promise as a prognostic tool for OSCC. Compared with the AJCC pN classification and the rN classification, LODDS can stratify OSCC patients and help to identify high-risk patients missed by the other systems.
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