A dynamic prognostic nomogram to predict the benefit from surgical treatment modality for patients with laryngeal squamous cell carcinoma

Abstract

Background Although nonsurgical treatment strategy is increasingly adopted in patients with locoregionally advanced laryngeal squamous cell carcinoma (LSCC), survival disparities were reported between surgical treatment modality and organ preservation protocols, highlighting the great importance for accurate patients' selection. Method This secondary analysis used data from the Surveillance, Epidemiology, and End Results database between 2010 and 2015 with follow‐up data up to 2018. We developed and validated a dynamic prognostic nomogram for overall survival (OS) in 4237 patients with LSCC and subgroup of 2087 patients with locoregionally advanced laryngeal squamous cell carcinoma (LALSCC). Based on the total risk score derived from the dynamic nomogram, two well‐matched risk groups (i.e., low‐ and high‐risk) were created via X‐tile software and 1‐to‐1 propensity score matching (PSM); surgical treatment modality was compared with nonsurgical one in each risk group. Results A more accurate and convenient dynamic prognostic nomogram based on age, marital status, T category, N category, M category, tumor size, and tumor differentiation was developed and validated, of which the predictive performance was superior to that of TNM staging system. For high‐risk LALSCC selected by the dynamic nomogram, after 1‐to‐1 PSM, significantly improved OS was observed in patients with receiving surgical treatment compared to those receipt of nonsurgical management (restricted mean survival time at 36‐month: 26.6 vs 22.7, p < 0.001; restricted mean survival time at 60‐month: 36.7 vs 31.0, p = 0.003). Conclusion We establish and validate a more accurate and convenient dynamic prognostic nomogram for patients with LSCC, which may predict the benefit from surgical treatment modality for patients with high‐risk LALSCC.

A novel risk classification system based on the eighth edition of TNM frameworks for esophageal adenocarcinoma patients: A deep learning approach

“…Despite these strengths, it is important to recognize several limitations of the present study. Firstly, it is regrettable that the SEER database could not provide information on lifestyle habits and overall comorbidity, more information about peritoneal metastases, and biomarkers in the laboratory, which may further improve the predictive accuracy ( 28 ). Secondly, we also acknowledge it hard to interpret how the deep learning model works because the process of predictions is much like black boxes.…”