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    基于数据挖掘的鼻咽癌T3~4分期风险预测模型构建

    Construction of risk prediction model for nasopharyngeal carcinoma with T3–4 stage based on data dig

      摘要
    • 摘要:
      目的： 探讨基于数据挖掘的鼻咽癌（NPC）T3~4分期风险预测模型的构建及其在识别NPC高风险人群中的价值。
      方法： 回顾性采集NPC病人326例，以8∶2的比例将其随机分为训练组和验证组。采用LASSO算法筛选NPC病人T3~4分期的相关因素，对筛选出的因素进行logistic回归分析并构建风险预测模型。采用C指数（CI）、受试者工作特征曲线下面积（AUC）、校准曲线（CC）、决策曲线（DCA）对模型进行评价。结果：NPC风险预测模型训练组的CI = 0.770、AUC = 0.714、DCA净获益率为18%～80%、90%～98%，验证组的CI = 0.835、AUC = 0.781、DCA净获益率为28%～98%。
      结论： 基于数据挖掘构建的NPC T3~4分期风险预测模型在识别NPC高风险人群较为准确，可作为一种无创方法对NPC高危人群进行预测，并指导临床决策。

       

      Abstract:
      Objective To construct the risk prediction model for nasopharyngeal carcinoma (NPC) with T3–4 stage based on data dig, and explore its value in identifying the high risk NPC groups.
      Methods A total of 326 NPC patients were retrospectively collected, and randomly divided into the training group and verification group with a ratio of 8∶2. LASSO algorithm was used to screen the factors related to the T3–4 stage of NPC patients, the selected factors were analyzed by logistic regression, and the risk prediction model was built. The C index (CI), area under the receiver operating characteristic curve (AUC), calibration curve (CC) and decision curve analysis (DCA) were used to evaluate the model.
      Results In the training group of NPC risk prediction model, the CI was 0.770, the AUC was 0.714, and the DCA net benefit rate were 18%–80% and 90%–98%. In the verification group, the CI was 0.835, the AUC was 0.781, and the DCA net benefit rate was 28%–98%.
      Conclusions The NPC T3–4 stage risk prediction model built based on data dig is more accurate in identifying high-risk NPC groups, and can be used as a non-invasive method to predict high-risk NPC groups, and guide clinical decision-making.

       

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