León Xavier, Rodriguez Camilo, Rovira Carlota, García Jacinto, López Montserrat, Quer Miquel
Servicio de Otorrinolaringología, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, España; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, España.
Servicio de Otorrinolaringología, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, España.
Acta Otorrinolaringol Esp (Engl Ed). 2020 May-Jun;71(3):131-139. doi: 10.1016/j.otorri.2019.02.004. Epub 2019 May 3.
Recursive partitioning analysis (RPA) is a technique that allows prognostic classification in oncological patients. The aim of the present study is to analyse by means of an RPA a cohort of patients with squamous carcinomas of the head and neck (SCHN).
5,226 SCHN were retrospectively analysed with an RPA, considering the specific survival and local control of the disease as dependent variables. A cohort of patients was used for the creation of the classification model, and another cohort was used to carry out its internal validation.
Considering specific survival as a dependent variable we obtained a classification tree with 14 terminal nodes that were grouped into 5 categories, including as partition variables the local and regional extent of the tumour, and the location of the tumour. When considering the local control of the disease as a dependent variable we obtained a classification tree with 10 terminal nodes that were grouped into 4 categories, including as partition variables the local extension and location of the tumour, the type of treatment performed, the age of the patient, and if it was a first tumour or a subsequent neoplasm. The validation study confirmed the prognostic capacity of the models developed with the RPA. One of the advantages of the RPA is that it allows the identification of groups of patients with specific behaviour.
RPA is shown to be an effective technique for the prognostic classification of patients with a SCHN.
递归分割分析（RPA）是一种可用于肿瘤患者预后分类的技术。本研究旨在通过RPA分析一组头颈部鳞状细胞癌（SCHN）患者。
对5226例SCHN患者进行回顾性RPA分析，将疾病的特定生存率和局部控制情况作为因变量。一组患者用于创建分类模型，另一组用于进行内部验证。
以特定生存率作为因变量，我们得到了一棵有14个终末节点的分类树，这些节点被分为5类，分割变量包括肿瘤的局部和区域范围以及肿瘤的位置。以疾病的局部控制作为因变量时，我们得到了一棵有10个终末节点的分类树，这些节点被分为4类，分割变量包括肿瘤的局部扩展和位置、所进行的治疗类型、患者年龄以及是否为原发性肿瘤或复发性肿瘤。验证研究证实了用RPA建立的模型的预后能力。RPA的优点之一是它能够识别具有特定行为的患者群体。
RPA被证明是一种对头颈部鳞状细胞癌患者进行预后分类的有效技术。
