基于超声和癌症指标构建乳腺癌术后生存列线图

Construction of a postoperative survival nomogram for breast cancer based on ultrasound and cancer indicators

  • 摘要:
    目的 探讨乳腺癌患者无进展生存期(PFS)的预后因素,构建并验证基于临床病理特征、术前癌症指标及超声图像特征的预后列线图模型。
    方法 回顾性分析2011年11月-2015年12月南通大学附属医院经手术治疗的260例乳腺癌患者的临床病理特征、术前癌症指标及超声资料,通过Cox风险比例模型逐步确定乳腺癌患者PFS的独立预后因素,建立预测模型并进行内部验证。
    结果 多因素Cox风险比例分析结果显示,肿块最大径、淋巴结转移、雌激素受体(ER)、糖类抗原125(CA125)、糖类抗原153(CA153)及生长方向是PFS的独立预测因素(P < 0.05)。根据以上指标构建列线图预测模型,验证结果表明,5年PFS的受试者工作特征(ROC)曲线的曲线下面积为0.844,一致性指数为0.793(95% CI为0.736~0.850),其3、5年校正曲线接近参考线,显示出良好的一致性。
    结论 本研究构建了综合乳腺癌患者临床病理特征、术前癌症指标及超声图像特征的预后预测列线图模型,为乳腺癌患者提供可视化的生存评估。

     

    Abstract:
    Objective To investigate the prognostic factors of progression free survival (PFS) in patients with breast cancer, and to construct and validate prognosis nomogram model based on clinicopathological features, preoperative cancer indicators and ultrasound features.
    Methods Clinicopathological features, preoperative cancer indicators and ultrasound data of 260 breast cancer patients in the Affiliated Hospital of Nantong University from November 2011 to December 2015 after surgical treatment were analyzed retrospectively, and the Cox risk model was used to gradually determine the independent prognostic factors of PFS in breast cancer patients. A prediction model was established and conducted with internal validation.
    Results The results of multivariate Cox analysis showed that the largest diameter of tumor, lymph node metastasis, estrogen receptor (ER), carbohydrate antigen 125 (CA125), carbohydrate antigen 153 (CA153) and growth direction were the independent predictors of PFS (P < 0.05). A nomogram model was established based on the above indicators, and the validation results showed that the area under the curve of the receiver operating characteristic (ROC) curve for 5-year PFS was 0.844, and the C-index was 0.793 (95%CI, 0.736 to 0.850), and the 3- and 5-year calibration curves of the nomogram was close to the reference line and showed a good consistency.
    Conclusion In this study, we construct a prognostic prediction nomogram model that combines the clinicopathological features, preoperative cancer markers and ultrasonographic features to provide a visual survival assessment for breast cancer patients.

     

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