ObjectiveTo explore the impact of number of positive regional lymph nodes (nPRLN) in N1 stage on the prognosis of non-small cell lung cancer (NSCLC) patients. MethodsPatients with TxN1M0 stage NSCLC who underwent lobectomy and mediastinal lymph node dissection from 2010 to 2015 were screened from SEER database (17 Regs, 2022nov sub). The optimal cutoff value of nPRLN was determined using X-tile software, and patients were divided into 2 groups according to the cutoff value: a nPRLN≤optimal cutoff group and a nPRLN>optimal cutoff group. The influence of confounding factors was minimized by propensity score matching (PSM) at a ratio of 1∶1. Kaplan-Meier curves and Cox proportional hazards models were used to evaluate overall survival (OS) and lung cancer-specific survival (LCSS) of patients. ResultsA total of 1316 patients with TxN1M0 stage NSCLC were included, including 662 males and 654 females, with a median age of 67 (60, 73) years. The optimal cutoff value of nPRLN was 3, with 1165 patients in the nPRLN≤3 group and 151 patients in the nPRLN>3 group. After PSM, there were 138 patients in each group. Regardless of before or after PSM, OS and LCSS of patients in the nPRLN≤3 group were superior to those in the nPRLN>3 group (P<0.05). N1 stage nPRLN>3 was an independent prognostic risk factor for OS [HR=1.52, 95%CI (1.22, 1.89), P<0.001] and LCSS [HR=1.72, 95%CI (1.36, 2.18), P<0.001]. ConclusionN1 stage nPRLN>3 is an independent prognostic risk factor for NSCLC patients in TxN1M0 stage, which may provide new evidence for future revision of TNM staging N1 stage subclassification.
ObjectiveTo analyze the risk factors affecting prognosis of appendiceal adenocarcinoma using data from the Surveillance, Epidemiology, and End Results (SEER) database. MethodsThe patients pathologically diagnosed with appendiceal adenocarcinoma from 2005 to 2015 were extracted from the SEER database and then randomly divided into a training cohort and validation cohort at a 7∶3 ratio. The univariate and multivariate Cox regression analyses were performed in the training cohort to identify the independent risk factors for overall survival (OS) and cancer-specific survival (CSS). Based on these factors, a nomogram prediction model was constructed and subsequently validated. ResultsA total of 749 patients with appendiceal adenocarcinoma were enrolled, with 524 in the training cohort and 225 in the validation cohort. The multivariate Cox regression analysis identified that the T, N, M stages, and surgery as the independent prognostic factors for both OS and CSS. Additionally, the age and tumor size were the independent prognostic factors for OS and CSS, respectively. Based on these factors, the nomogram prediction models for OS and CSS were constructed. The C-index (95%CI) of the OS nomogram prediction model was 0.716 (0.689, 0.743) in the training cohort and 0.695 (0.649, 0.740) in the validation cohort. The C-index of the CSS nomogram prediction model was 0.749 (0.716, 0.782) and 0.746 (0.699, 0.793) in the training and validation cohorts, respectively. The calibration curves demonstrated a good agreement between predicted and observed outcomes for both OS and CSS. The area under the receiver operating characteristic curve (AUC) of the OS nomogram prediction model in predicting 3- and 5-year overall survival rate was 0.780 (0.739, 0.821) and 0.773 (0.732, 0.814) respectively in the training cohort, was 0.789 (0.726, 0.852) and 0.776(0.715, 0.837) respectively in the validation cohort. The AUC of the CSS nomogram prediction model in predicting 3- and 5-year cancer-specific survival rate was 0.813 (0.768, 0.858) and 0.796 (0.753, 0.839) respectively in the training cohort, was 0.813 (0.750, 0.876)and 0.811 (.750, 0.872) respectively in the validation cohort. ConclusionsThrough analysis of appendiceal adenocarcinoma patients from the SEER database reveals that advanced T, N, and M stages, as well as lack of surgery are significant risk factors for both OS and CSS. The constructed nomogram prediction models for OS and CSS, incorporating these risk factors, has a good prediction ability.