建立基于临床因素预测结直肠癌HER-2状态的诺莫图模型

Establishment of a Nomogram Model for Predicting the HER-2 Status of Colorectal Cancer Based on Clinical Factors

摘要: 研究背景：肿瘤HER-2表达的准确预测对于肿瘤治疗和预后评估起着至关重要的作用。然而，现有检测方法具有一定的局限性。研究目的：本研究旨在通过性别、吸烟史、饮酒史、血红蛋白、中性粒细胞、肿瘤大小、T分期等多个因素建立列线图，以预测肿瘤HER-2表达，并通过自举法验证列线图模型的准确性。研究内容：研究分析了肿瘤HER-2表达的生物学意义，采用最小绝对收缩和选择算子(LASSO回归)进行模型特征筛选，采用十折交叉验证来选择最优正则化参数，通过筛选出的HER-2相关的临床因素建立临床列线图，预测肿瘤HER-2表达，并通过自举法验证列线图模型准确性。从而通过患者临床病理因素预测肿瘤HER-2表达。研究结果：开发了基于临床信息的多因素预测HER-2表达的列线图预测模型，该模型具备良好的预测性能与临床应用能力，其曲线下面积(AUC)为0.860 (95%置信区间：0.8124~0.9068)。研究局限性及未来展望：我们开发的基于多因素预测HER-2表达的列线图预测模型具备良好的预测性能与临床应用能力，其曲线下面积(AUC)为0.860 (95%置信区间：0.8124~0.9068)。我们开发的多因素列线图预测模型为肿瘤HER-2表达的预测提供了准确、可靠的方法。但研究也存在局限性，未来可从扩大样本量、探索其他因素、结合分子生物学技术及开展临床干预研究等方面进一步完善。

Abstract: Research Background: Accurate prediction of tumor HER-2 expression plays a crucial role in tumor treatment and prognosis evaluation. However, existing detection methods have certain limitations. Research Purpose: This study aims to establish a nomogram through multiple factors such as gender, smoking history, drinking history, hemoglobin, neutrophils, tumor size, and T stage to predict tumor HER-2 expression and verify the accuracy of the nomogram model through the bootstrap method. Research Content: The biological significance of tumor HER-2 expression was analyzed. The least absolute shrinkage and selection operator (LASSO regression) was used for model feature screening. Ten-fold cross-validation was used to select the optimal regularization parameter. A clinical nomogram was established through the screened HER-2-related clinical factors to predict tumor HER-2 expression. The accuracy of the nomogram model was verified by the bootstrap method. Thus, tumor HER-2 expression is predicted through patients’ clinicopathological factors. Research Results: A nomogram prediction model based on multi-factor prediction of HER-2 expression was developed. This model has good prediction performance and clinical application ability. Its area under the curve (AUC) is 0.860 (95% confidence interval: 0.8124~0.9068). Research Limitations and Future Prospects: The nomogram prediction model based on multi-factor prediction of HER-2 expression developed by us has good prediction performance and clinical application ability. Its area under the curve (AUC) is 0.860 (95% confidence interval: 0.8124~0.9068). The multi-factor nomogram prediction model developed by us provides an accurate and reliable method for predicting tumor HER-2 expression. However, the study also has limitations. In the future, it can be further improved by expanding the sample size, exploring other factors, combining molecular biology techniques, and conducting clinical intervention studies.