Department of Oral Pathology, Faculty of Dental Sciences, University of Peradeniya, Peradeniya, Sri Lanka.
Department of Community Medicine, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka.
Biomed Res Int. 2018 May 13;2018:8925818. doi: 10.1155/2018/8925818. eCollection 2018.
Developing histological prediction models that estimate the probability of developing metastatic deposit will help clinicians to identify individuals who need either radical or prophylactic neck dissection, which leads to better prognosis. Identification of accurate predictive models in oral cancer is important to overcome extensive prophylactic surgical management for neck nodes. Therefore, accurate prediction of metastasis in oral cancer would have an immediate clinical impact, especially to avoid unnecessary radical treatment of patients who are at a low risk of metastasis.
Histologically confirmed OSCC cases with neck dissection were used. Interrelation of demographic, clinical, and histological data was done using univariate and multivariate analysis.
465 cases were used and presence of metastasis and extracapsular invasion were statistically well correlated with level of differentiation ( < 0.001) and pattern of invasion ( < 0.001). Multivariate analysis showed level of differentiation, pattern of invasion, and stage as predictors of metastasis.
The proposed predictive model may provide some guidance for maxillofacial surgeons to decide the appropriate treatment plan for OSCC, especially in developing countries. This model appears to be reliable and simple and may guide surgeons in planning surgical management of neck nodes.
开发能够预测转移概率的组织学预测模型,有助于临床医生识别需要进行根治性或预防性颈部清扫术的个体,从而获得更好的预后。在口腔癌中确定准确的预测模型对于克服广泛的预防性颈部淋巴结手术管理至关重要。因此,准确预测口腔癌的转移将具有直接的临床影响,特别是可以避免对转移风险低的患者进行不必要的根治性治疗。
使用经组织学证实的伴有颈部清扫术的口腔鳞状细胞癌病例。使用单变量和多变量分析来研究人口统计学、临床和组织学数据之间的相互关系。
共使用了 465 例病例,转移和囊外侵犯的存在与分化程度(<0.001)和浸润模式(<0.001)具有统计学相关性。多变量分析显示,分化程度、浸润模式和分期是转移的预测因素。
所提出的预测模型可以为颌面外科医生提供一些指导,以决定口腔鳞状细胞癌的适当治疗方案,特别是在发展中国家。该模型似乎可靠且简单,可指导外科医生规划颈部淋巴结的手术管理。