A New Online Dynamic Nomogram: Construction and Validation of an Assistant Decision-Making Model for Laryngeal Squamous Cell Carcinoma - Liu, Yuchen; Han, Yanxun; Chen, Bangjie; Zhang, Jian; Yin, Siyue; Li, Dapeng; Wu, Yu; Jiang, Yuan; Wang, Xinyi; Wang, Jianpeng; Fu, Ziyue; Shen, Hailong; et al. (Frontiers in Oncology, 2022)
Background: Laryngeal squamous cell carcinoma (LSCC) is the most common head and neck squamous cell carcinoma (HNSCC). However, currently, there are no reliable biomarkers for the diagnosis and prognosis of LSCC. This study lies in developing and validating a new dynamic web version nomogram that can predict auxiliary laryngeal carcinogenesis. Method: We retrospectively reviewed the medical history of 221 patients who were recently diagnosed with LSCC and 359 who were recently diagnosed with benign laryngeal lesions (BLL) in the First Affiliated Hospital of Anhui Medical University. Using the bootstrap method, 580 patients were divided 7:3 into a training cohort and an internal validation cohort . Meanwhile, a retrospective analysis of 31 cases of LSCC and 54 cases of BLL from Fuyang Hospital Affiliated to Anhui Medical University was also performed as an external validation cohort. In the training cohort, we initially screened relevant indexes by univariate analysis. Then we used LASSO logistic analysis to evaluate statistically significant potential independent risk factors and constructed a dynamic online diagnostic nomogram whose discrimination was evaluated with AUC while consistency was evaluated with calibration plots. Its clinical application was evaluated by decision analysis and validated by internal validation of the training set and external validation of the validation set. Results: Five independent risk factors, the gender, age, smoking , RDW, ALBwere screened from the multivariate logistic analysis of the training cohort and included in the LSCC diagnostic nomogram. The nomogram in the training cohort predicted LSCC with an AUC of 0.894, in the internal testing cohort with an AUC of 0.907, and in the external validation cohort with an AUC of 0.966. The calibration curve also proved that the nomogram predicted outcomes were close to the ideal curve, it could be considered that the predicted outcomes were consistent with the real outcomes, and the DCA curve showed that all patients could benefit. This was also confirmed in the validation cohort. Conclusion: An online nomogram for LSCC was constructed with good predictive performance, which may provide a practical approach for personalized early screening and auxiliary diagnosis of potential risk factors.