BMC cancer · 2025
Enhanced prognosis prediction model in esophageal cancer via lymph node assessment post-neoadjuvant.
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Zhou Yehan, Xiao Meng, Li Jiayu, Yang Hong, Wang Yi, Wang Qifeng, Leng Xuefeng, He Wenwu, Han Yongyao, Huang Zongyao, Liu Yang
Аннотация
UNLABELLED: Develop an improved lymph node evaluation system for prognosis prediction in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant therapy.
METHODS: Studied 282 ESCC patients who underwent neoadjuvant chemoradiotherapy (NCRT) and surgery. Evaluated lymph node factors like number, size, location, and intra-nodal cancer status. Developed and validated a prognostic model using these factors.
RESULTS: Cases with a clearance of 17-30 lymph nodes during surgery showed improved overall survival (OS) compared to those with fewer than 17 or more than 30 cleared nodes. Patients with a lower tumor regression grade of lymph nodes (LN-TRG) experienced improved OS and recurrence-free survival (RFS). Patients with LN extracapsular invasion had worse OS and RFS. Abdominal lymph nodes showed the highest ratio of residual cancer, highest hazard ratio (HR), and the largest number and proportion of positive cases. The five most relevant factors for prognosis, as revealed by Lasso regression and Cox univariate analysis, were LN-TRG, LN total tumor diameter, the clearance of 17-30 lymph nodes, the proportion of positive lymph nodes, and LN extracapsular invasion. The predictive model, based on these factors, achieved good risk stratification for OS and RFS (AUC = 0.705, 0.679), surpassing pathology N (pN) staging.
CONCLUSION: A comprehensive lymph node assessment using a predictive model enables improved prognosis prediction and risk stratification compared to pN staging for patients undergoing neoadjuvant therapy. This approach contributes to the accurate evaluation of clinical prognosis and assists in guiding treatment strategies.
METHODS: Studied 282 ESCC patients who underwent neoadjuvant chemoradiotherapy (NCRT) and surgery. Evaluated lymph node factors like number, size, location, and intra-nodal cancer status. Developed and validated a prognostic model using these factors.
RESULTS: Cases with a clearance of 17-30 lymph nodes during surgery showed improved overall survival (OS) compared to those with fewer than 17 or more than 30 cleared nodes. Patients with a lower tumor regression grade of lymph nodes (LN-TRG) experienced improved OS and recurrence-free survival (RFS). Patients with LN extracapsular invasion had worse OS and RFS. Abdominal lymph nodes showed the highest ratio of residual cancer, highest hazard ratio (HR), and the largest number and proportion of positive cases. The five most relevant factors for prognosis, as revealed by Lasso regression and Cox univariate analysis, were LN-TRG, LN total tumor diameter, the clearance of 17-30 lymph nodes, the proportion of positive lymph nodes, and LN extracapsular invasion. The predictive model, based on these factors, achieved good risk stratification for OS and RFS (AUC = 0.705, 0.679), surpassing pathology N (pN) staging.
CONCLUSION: A comprehensive lymph node assessment using a predictive model enables improved prognosis prediction and risk stratification compared to pN staging for patients undergoing neoadjuvant therapy. This approach contributes to the accurate evaluation of clinical prognosis and assists in guiding treatment strategies.