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首页> 外文期刊>NPJ breast cancer. >Incorporation of clinical and biological factors improves prognostication and reflects contemporary clinical practice
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Incorporation of clinical and biological factors improves prognostication and reflects contemporary clinical practice

机译：临床和生物因素的纳入提高了预后，反映了当代临床实践

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We developed prognostic models for breast cancer-specific survival (BCSS) that consider anatomic stage and other important determinants of prognosis and survival in breast cancer, such as age, grade, and receptor-based subtypes with the intention to demonstrate that these factors, conditional on stage, improve prediction of BCSS. A total of 20,928 patients with stage I–III invasive primary breast cancer treated at The University of Texas MD Anderson Cancer Center between 1990 and 2016, who received surgery as an initial treatment were identified to generate prognostic models by Fine-Gray competing risk regression model. Model predictive accuracy was assessed using Harrell’s C-index. The Aalen–Johansen estimator and a selected Fine–Gray model were used to estimate the 5-year and 10-year BCSS probabilities. The performance of the selected model was evaluated by assessing discrimination and prediction calibration in an external validation dataset of 29,727 patients from the National Comprehensive Cancer Network (NCCN). The inclusion of age, grade, and receptor-based subtype in addition to stage significantly improved the model predictive accuracy (C-index: 0.774 (95% CI 0.755–0.794) vs. 0.692 for stage alone, p??0.0001). Young age (40), higher grade, and TNBC subtype were significantly associated with worse BCSS. The selected model showed good discriminative ability but poor calibration when applied to the validation data. After recalibration, the predictions showed good calibration in the training and validation data. More refined BCSS prediction is possible through a model that has been externally validated and includes clinical and biological factors.
机译：我们开发了乳腺癌特异性生存（BCS）的预后模型，以考虑解剖学阶段和其他重要决定因素的预后和生存在乳腺癌中，例如年龄，等级和基于受体的亚型，有意证明这些因素，条件在舞台上，改善了BCS的预测。 1990年至2016年间在德克萨斯州和2016年间，在德克萨斯州和2016年间，在1990年至2016年间接受了手术的初级乳腺癌患者的共有20,928例患有初级乳腺癌，作为初步治疗，通过细灰色竞争风险回归模型产生预后模型。使用Harrell的C索引评估了模型预测准确性。 AALEN-Johansen估计和选择的细灰色模型用于估计5年和10年的BCSS概率。通过评估来自国家综合癌症网络（NCCN）的29,727名患者的外部验证数据集中的歧视和预测校准来评估所选模型的性能。除了阶段之外，包含年龄，级和基于受体的亚型，显着提高了模型预测精度（C折射率：0.774（95％CI 0.75-0.794），单独阶段为0.692，p？<？0.0001）。年龄（<40），较高的等级和TNBC亚型与更严重的BCS显着相关。当应用于验证数据时，所选模型显示出良好的辨别能力但校准差。重新校准后，预测显示培训和验证数据中的良好校准。通过外部验证的模型可以提高BCSS预测，包括临床和生物因素。

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