Improved Cancer-Specific Risk Stratification by the Lymph Node Ratio-Based Nomogram: A Potential Role in Guiding Postoperative Management Decisions for Oral Cavity Carcinoma

JCO PRECISION ONCOLOGY(2023)

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摘要
PURPOSE To develop and validate a nomogram integrating lymph node ratio (LNR) to predict cancer-specific survival (CSS) and assist decision making for postoperative management in nonmetastatic oral cavity squamous cell carcinoma (OCSCC). MATERIALS AND METHODS We retrospectively retrieved 6,760 patients with OCSCC primarily treated with surgery from surveillance, epidemiology, and end results database between 2010 and 2015. They were randomly divided into training and validation cohorts. Performance of the nomogram was evaluated by calibration curve, consistency index, area under the curve, and decision curve analysis and was compared with that of the LNR, positive lymph nodes (PLN) and tumor node metastasis (TNM) staging. According to the individualized nomogram score, patients were classified into three risk cohorts. The therapeutic efficacy of postoperative radiotherapy and chemotherapy was evaluated in each cohort. RESULTS The nomogram incorporated six independent variables, including race, tumor site, grade, T stage, PLN, and LNR. Calibration plots demonstrated a good match between the predicted and observed CSS. C-indices for training and validation cohorts were 0.746 (95% CI, 0.740 to 0.752) and 0.726 (95% CI, 0.713 to 0.739), compared with 0.687, 0.695, and 0.669 for LNR, PLN, and TNM staging, respectively (P < .001). Decision curve analyses confirmed that nomogram showed the best performance in clinical utility. Postoperative radiotherapy presented survival benefit in medium-and high-risk groups but showed a negative effect in the low-risk group. Chemotherapy was only beneficial in the high-risk group. CONCLUSION The LN status-incorporated nomogram demonstrated good discrimination and predictive accuracy of CSS for patients with OCSCC and could identify those most likely to benefit from adjuvant therapy.
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