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Objectives: Despite the increasingly modern surgical techniques in the oncology field, the factors that influence postoperative prognosis in patients with hypopharyngeal and laryngeal carcinoma (HLC) remain unclear. The study aimed to evaluate the factors influencing the prognosis of HLC patients with pathological diagnosis of squamous cell carcinoma, and the findings are intended to direct follow-up management strategies. Methods: A retrospective cohort study was performed. The study population included 407 postoperative patients with HLC from 2011 to 2015. Univariate and multivariate analyses were used to examine the prognostic factors identified. Results: Based on univariate analysis results, smoking and alcohol history, tumor differentiation, preoperative radiotherapy, primary tumor sites, flap reconstruction, lymph node invasion (LNI), and preoperative albumin levels (PAL) significantly affects the prognosis of HLC patients (P <.05). Meanwhile, multivariate analysis revealed that smoking pack-year (OR = 1.002, 95% CI = 1.001 ∼ 1.003), primary tumor sites (OR = 6.241, 95% CI = 1.715 ∼ 18.433), LNI (OR = 2.869, 95% CI = 1.095 ∼ 8.743), and PAL (OR =.020, 95% CI =.004 ∼ 0.104) were associated with complications. Tumor differentiation (OR = 0.650, 95% CI =.383 ∼ 0.855), primary tumor sites (OR = 12.392, 95% CI = 3.290 ∼ 26.679), LNI (OR = 16.323, 95% CI = 2.726 ∼ 47.729), preoperative radiotherapy (OR = 9.300, 95% CI = 3.182 ∼ 27.181), and PAL (OR =.321, 95% CI =.141 ∼.732) were associated with overall survival rates. Conclusion: Smoking and alcohol history, tumor differentiation, LNI, primary tumor sites, flap reconstruction, PAL, and preoperative radiotherapy are crucial factors that influence the postoperative prognosis of patients with HLC. In addition, a monogram of five factors was established to predict the survival rates of HLC patients. [ABSTRACT FROM AUTHOR]

Purpose: Our objective is to examine the independent prognostic risk factors for patients with Esophageal Cancer with Liver Metastasis (ECLM) and to develop a predictive model. Methods: In this study, clinical data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Cox regression analysis was employed to identify independent prognostic factors and construct nomograms based on the results of multivariate regression. The predictive performance of the nomograms was assessed using several methods, including the consistency index (C-index), calibration curve, time-dependent receiver-operating characteristic curve (ROC), and decision curve analysis (DCA). Additionally, Kaplan–Meier survival curves were generated to demonstrate the variation in overall survival between groups. Results: A total of 1163 ECLM patients were included in the study. Multivariate Cox analysis revealed that age, tumor differentiation grade, bone metastasis, therapy, and income were independently associated with overall survival (OS) in the training set. Subsequently, a prognostic nomogram was constructed based on these independent predictors. The C-index values were 0.739 and 0.715 in the training and validation sets, respectively. The area under the curve (AUC) values at 0.5, 1, and 2 years were all higher than 0.700. Calibration curves indicated that the nomogram accurately predicted OS. Decision curve analysis (DCA) showed moderately positive net benefits. Kaplan–Meier survival curves demonstrated significant differences in survival between high- and low-risk groups, which were divided based on the nomogram risk score. Conclusions: The nomogram we developed for ECLM patients has demonstrated good predictive capability, allowing clinicians to accurately evaluate patient prognosis and identify those at high risk, thereby facilitating the development of personalized treatment plans. [ABSTRACT FROM AUTHOR]