ArticleCancer biology & therapy2023
Integrating DOI in T classification improves the predictive performance of laryngeal cancer staging.
Article in Cancer biology & therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Impaired vocal cord mobility due to thyroarytenoid muscle involvement predicts poorer prognosis in T2 glottic cancer. Opportunity for refinement of the TNM 8Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2026Review
- "Histologic type, treatment and prognosis of carcinomas of the larynx: A review".Virchows Archiv : an international journal of pathology · 2026Review
- Article
- The impact of histopathology on prognosis of squamous cell carcinoma of the larynx: can we do better?Virchows Archiv : an international journal of pathology · 2025Review
- The relationship between depth of invasion and cervical lymph node metastasis in patients with laryngeal squamous cell carcinoma.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Observational
- Artificial Intelligence Application for Anti-tumor Drug Synergy Prediction.Current medicinal chemistry · 2024Review
- Development and validation of a nomogram for predicting advanced liver fibrosis in patients with chronic hepatitis B.Frontiers in molecular biosciences · 2024Article
- Histopathologic Features of Mucosal Head and Neck Cancer Cachexia.International journal of surgical oncology · 2024Article
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Abstract
It has been recognized that depth of invasion (DOI) is closely associated with patient survival for most types of cancer. The purpose of this study was to determine the DOI optimal cutoff value and its prognostic value in laryngeal squamous carcinoma (LSCC). Most importantly, we evaluated the prognostic performance of five candidate modified T-classification models in patients with LSCC. LSCC patients from Harbin Medical University Cancer Hospital and Chinese Academy of Medical Sciences Cancer Hospital were divided into training group (n = 412) and validation group (n = 147). The primary outcomes were overall survival (OS) and relapse-free survival (RFS), and the effect of DOI on prognosis was analyzed using a multivariable regression model. We identified the optimal model based on its simplicity, goodness of fit and Harrell's consistency index. Further independent testing was performed on the external validation queue. The nomograms was constructed to predict an individual's OS rate at one, three, and five years. In multivariate analysis, we found significant associations between DOI and OS (Depth of Medium-risk invasion HR, 2.631; P < .001. Depth of high-risk invasion: HR, 5.287; P < .001) and RFS (Depth of high-risk invasion: HR, 1.937; P = .016). Model 4 outperformed the American Joint Committee on Cancer (AJCC) staging system based on a low Akaike information criterion score, improvement in the concordance index, and Kaplan-Meier curves. Inclusion of DOI in the current AJCC staging system can improve the differentiation of T classification in LSCC patients.
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