Candido Dos Reis Francisco J, Wishart Gordon C, Dicks Ed M, Greenberg David, Rashbass Jem, Schmidt Marjanka K, van den Broek Alexandra J, Ellis Ian O, Green Andrew, Rakha Emad, Maishman Tom, Eccles Diana M, Pharoah Paul D P
Department of Gynaecology and Obstetrics, Ribeirao Preto Medical School, University of Sao Paulo, Sao Paulo, Brazil.
Faculty of Medical Science, Anglia Ruskin University, Cambridge, UK.
Breast Cancer Res. 2017 May 22;19(1):58. doi: 10.1186/s13058-017-0852-3.
PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status.
Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT.
In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40.
The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer.
PREDICT是一个在线实施的乳腺癌预后和治疗获益模型。该模型在多个独立病例系列中的整体拟合效果良好，但已表明PREDICT会低估40岁以下确诊女性的乳腺癌特异性死亡率。另一个局限性是对肿瘤大小和淋巴结状态使用离散类别，导致类别之间转换时风险估计出现“阶梯”变化。我们使用东安格利亚的原始病例队列并更新生存时间，对PREDICT预后模型进行了重新拟合，以考虑诊断时的年龄，并平滑肿瘤大小和淋巴结状态的生存函数。
使用多变量Cox回归模型分别拟合雌激素受体（ER）阴性和ER阳性疾病的模型。连续变量使用分数多项式进行拟合，并通过使用分数多项式将每个患者的基线累积风险相对于时间进行回归，获得平滑的基线风险。然后在三个也用于验证PREDICT原始版本的独立数据集中测试预后模型的拟合情况。
在模型拟合数据中，在调整其他预后变量后，ER阳性疾病的年轻和老年患者的乳腺癌特异性死亡率风险增加，35岁之前确诊的女性风险大幅增加。在ER阴性疾病中，风险随年龄略有增加。乳腺癌特异性死亡率与肿瘤大小和阳性淋巴结数量之间的关联是非线性的，在ER阳性疾病中，随着大小增加和淋巴结数量增加，风险增加更为明显。新版本的PREDICT（v2）在模型开发和验证数据集中的整体校准和区分度良好，与上一版本相当。然而，v2在40岁以下确诊患者中的校准比v1有所改善。
与v1相比，PREDICT v2是一个改进的预后和治疗获益模型。在线版本应继续有助于早期乳腺癌女性的临床决策。
