ArticleAnnals of translational medicine2022
Development and validation of a model to predict the risk of recurrence in patients with laryngeal squamous cell carcinoma after total laryngectomy.
Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review.PloS one · 2024Pooled it
- Deep learning radiomics nomogram predicts lymph node metastasis in laryngeal squamous cell carcinoma.Frontiers in oncology · 2025Article
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9 authors.
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Abstract
Background: Recurrence is still the main obstacle to the survival of laryngeal squamous cell carcinoma (LSCC) patients who have undergone a total laryngectomy. Previous models for recurrence prediction in patients with LSCC were based on pathological information, while the role of easily accessible inflammatory markers in the prognosis of LSCC patients has rarely been reported. This study sought to develop and validate a model to predict the risk of recurrence in LSCC patients who underwent total laryngectomy. Methods: A total of 204 LSCC patients who underwent a total laryngectomy were included in this retrospective cohort study. Demographics, pathology, and inflammatory markers of patients were collected. All the patients were randomly divided into a training set and a test set at a ratio of 4:1. Patients were followed up for 3 years after surgery or until death occurred during this period. The random-forest method was used to develop a predictive model. The performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC) with the 95% confidence interval (CI), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: Of the 204 LSCC patients, 56 (27.45%) patients had a recurrence. The random-forest prediction model was an all-factor model, and the most important predictors of the model were the albumin/globulin ratio (AGR), neutrophil/lymphocyte ratio (NLR), and platelet/lymphocyte ratio (PLR), with proportions of 0.121, 0.100, and 0.092, respectively. The AUCs of the model in predicting the recurrence of LSCC in the training set and the test set were 0.960 (95% CI, 0.931-0.989) and 0.721 (95% CI, 0.716-0.726), respectively. The sensitivity, specificity, accuracy, PPV, and NPV of the model in the test set were 0.750 (95% CI, 0.505-0.995), 0.690 (95% CI, 0.521-0.858), 0.707 (95% CI, 0.568-0.847), 0.500 (95% CI, 0.269-0.921), and 0.870 (95% CI, 0.732-1.000), respectively. Conclusions: A model to predict the risk of recurrence in LSCC patients who have undergone a total laryngectomy was established, and inflammatory markers AGR, NLR, and PLR play an important role in the predictive model.
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