A risk stratification model for predicting brain metastasis and brain screening benefit in patients with metastatic triple-negative breast cancer.
A risk stratification model for predicting brain metastasis and brain screening benefit in patients with metastatic triple-negative breast cancer.
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DOI：
10.1002/cam4.3449
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发表时间：
2020-11
期刊：
影响因子：
4
通讯作者：
Zhang J
中科院分区：
文献类型：
--
作者：
Lin M;Jin Y;Jin J;Wang B;Hu X;Zhang J
转移性三阴性乳腺癌（mTNBC）患者经常发生脑转移。本研究旨在确定mTNBC患者的预后因素并构建预测脑转移可能性和脑筛查获益的nomogram。回顾性分析2011年1月至2018年12月在我院治疗的mTNBC患者。Fine和Gray的竞争风险模型用于识别独立的预后因素。通过整合这些预后因素，我们建立了一个相互竞争的风险nomogram和风险分层模型，并用一致性指数（C‐index）和校准曲线进行了评估。回顾性分析共472例患者，其中训练集305例，验证集I 78例，验证集II 89例。四个临床病理因素被确定为独立的预后因素：肺转移、转移器官部位数量、肺门/纵隔淋巴结转移和KI‐67指数。C‐指数和校准图表明，nomogram具有足够的判别能力。进一步进行风险分层，将所有患者分为三个预后组。低危组患者18个月脑转移的累积发生率为5.3%(95%可信区间[CI]， 2.5% ~ 9.7%)，中危组患者为14.3% (95% CI, 9.3% ~ 20.4%)，高危组为34.3% （95% CI, 26.8% ~ 41.9%）。常规脑MRI筛查提高了高危组的总生存率（HR 0.67, 95% CI 0.46 ~ 0.98, P = 0.039），但在低危组（HR 0.93, 95% CI 0.57 ~ 1.49, P = 0.751）和中危组（HR 0.83, 95% CI 0.55 ~ 1.27, P = 0.386）没有改善。我们已经开发了一种强大的工具，能够预测mTNBC患者随后的脑转移。我们的模型将允许选择可能从常规麸质MRI筛查中受益的脑转移高风险患者。本研究发现mTNBC患者发生脑转移的四个危险因素：肺转移、转移器官部位数量、肺门/纵隔淋巴结转移和KI‐67指数。通过整合这些预后因素，产生了一个竞争风险图来预测个体化脑转移的可能性。我们的模型将允许选择可能从常规麸质MRI筛查中受益的脑转移高风险患者。
Patients with metastatic triple‐negative breast cancer (mTNBC) frequently experience brain metastasis. This study aimed to identify prognostic factors and construct a nomogram for predicting brain metastasis possibility and brain screening benefit in mTNBC patients. Patients with mTNBC treated at our institution between January 2011 and December 2018 were retrospectively analyzed. Fine and Gray's competing risks model was used to identify independent prognostic factors. By integrating these prognostic factors, a competing risk nomogram and risk stratification model were developed and evaluated with concordance index (C‐index) and calibration curves. A total of 472 patients were retrospectively analyzed, including 305 patients in the training set, 78 patients in the validation set I and 89 patients in the validation set II. Four clinicopathological factors were identified as independent prognostic factors in the nomogram: lung metastasis, number of metastatic organ sites, hilar/mediastinal lymph node metastasis and KI‐67 index. The C‐indexes and calibration plots showed that the nomogram exhibited a sufficient level of discrimination. A risk stratification was further generated to divide all the patients into three prognostic groups. The cumulative incidence of brain metastasis at 18 months was 5.3% (95% confidence interval [CI], 2.5%‐9.7%) for patients in the low‐risk group, while 14.3% (95% CI, 9.3%‐20.4%) for patients with intermediate risk and 34.3% (95% CI, 26.8%‐41.9%) for patients with high risk. Routine brain MRI screening improved overall survival in high‐risk group (HR 0.67, 95% CI 0.46‐0.98, P = .039), but not in low‐risk group (HR 0.93, 95% CI 0.57‐1.49, P = .751) and intermediate‐risk group (HR 0.83, 95% CI 0.55‐1.27, P = .386). We have developed a robust tool that is able to predict subsequent brain metastasis in mTNBC patients. Our model will allow selection of patients at high risk for brain metastasis who might benefit from routine bran MRI screening. This study found four risk factors for the development of brain metastasis in mTNBC patients: lung metastasis, number of metastatic organ sites, hilar/mediastinal lymph node metastasis and KI‐67 index. By integrating these prognostic factors, a competing risk nomogram was generated to predict individualized brain metastasis possibility. Our model will allow selection of patients at high risk for brain metastasis who might benefit from routine bran MRI screening.
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影响因子：
120.7
作者：
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