Predictive Model to Guide Brain Magnetic Resonance Imaging Surveillance in Patients With Metastatic Lung Cancer: Impact on Real-World Outcomes.
Predictive Model to Guide Brain Magnetic Resonance Imaging Surveillance in Patients With Metastatic Lung Cancer: Impact on Real-World Outcomes.
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DOI：
10.1200/po.22.00220
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发表时间：
2022-10
影响因子：
4.6
通讯作者：
Han, Summer S.
中科院分区：
文献类型：
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作者：
Wu, Julie;Ding, Victoria;Luo, Sophia;Choi, Eunji;Hellyer, Jessica;Myall, Nathaniel;Henry, Solomon;Wood, Douglas;Stehr, Henning;Ji, Hanlee;Nagpal, Seema;Gephart, Melanie Hayden;Wakelee, Heather;Neal, Joel;Han, Summer S.
脑转移在肺癌中是常见的，并且脑转移的治疗可导致显著的发病率。虽然早期发现脑转移瘤可能会改善预后，但目前还没有预测模型来识别脑磁共振成像（MRI）监测的高风险患者。我们的目标是开发一种基于机器学习的临床基因组学预测模型，以估计患者水平的脑转移风险。在斯坦福大学医疗保健中心，使用2014年1月至2019年6月期间诊断为肺癌的330名患者开发了一个惩罚回归竞争风险模型，并随访至2021年6月。主要结局是从远处转移性疾病诊断到发生脑转移、死亡或删失的时间。在330例患者中，84例（25%）在627人-年内发生脑转移，1年累积脑转移发生率为10.2%（95%CI，6.8 - 13.6）。选择纳入模型的特征包括组织学、癌症分期、诊断时的年龄、原发部位以及RB 1和ALK改变。预测模型产生了高区分度（曲线下面积0.75）。当使用1年风险阈值> 14.2%（第85百分位数）对队列进行风险分层时，高风险组与低风险组相比，1年累积脑转移发生率增加（30.8% v6.1%，P <0.01）。在48例高危患者中，24例发生脑转移，其中12例患者在最后一次脑MRI后超过7个月才发现脑转移。与那些更频繁进行MRI的患者相比，错过7个月时间窗的患者有更大的脑转移（58% v33%最大直径> 10 mm;比值比，2.80，CI，0.51 - 13）。所提出的模型可以识别高风险患者，这些患者可能受益于更密集的脑MRI监测，以通过早期检测降低后续治疗的发病率。
Brain metastasis is common in lung cancer, and treatment of brain metastasis can lead to significant morbidity. Although early detection of brain metastasis may improve outcomes, there are no prediction models to identify high-risk patients for brain magnetic resonance imaging (MRI) surveillance. Our goal is to develop a machine learning–based clinicogenomic prediction model to estimate patient-level brain metastasis risk. A penalized regression competing risk model was developed using 330 patients diagnosed with lung cancer between January 2014 and June 2019 and followed through June 2021 at Stanford HealthCare. The main outcome was time from the diagnosis of distant metastatic disease to the development of brain metastasis, death, or censoring. Among the 330 patients, 84 (25%) developed brain metastasis over 627 person-years, with a 1-year cumulative brain metastasis incidence of 10.2% (95% CI, 6.8 to 13.6). Features selected for model inclusion were histology, cancer stage, age at diagnosis, primary site, and RB1 and ALK alterations. The prediction model yielded high discrimination (area under the curve 0.75). When the cohort was stratified by risk using a 1-year risk threshold of > 14.2% (85th percentile), the high-risk group had increased 1-year cumulative incidence of brain metastasis versus the low-risk group (30.8% v 6.1%, P < .01). Of 48 high-risk patients, 24 developed brain metastasis, and of these, 12 patients had brain metastasis detected more than 7 months after last brain MRI. Patients who missed this 7-month window had larger brain metastases (58% v 33% largest diameter > 10 mm; odds ratio, 2.80, CI, 0.51 to 13) versus those who had MRIs more frequently. The proposed model can identify high-risk patients, who may benefit from more intensive brain MRI surveillance to reduce morbidity of subsequent treatment through early detection.