构建未进行预防性头颅照射的局限期小细胞肺癌患者脑转移预测模型

IF 1.9 4区 医学 Q3 RESPIRATORY SYSTEM Clinical Respiratory Journal Pub Date : 2024-01-15 DOI:10.1111/crj.13730
Jiayi Guo, Jianjiang Liu, Wanli Ye, Jun Xu, Wangyan Zhong, Xiaoyu Zhang, Hang Yuan, Hao Shi, Ting Li, Yibing Xu, Jiwei Mao, Bin Shen, Dongping Wu
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引用次数: 0

摘要

导言:小细胞肺癌(SCLC)是一种侵袭性极强的肺癌变种,以脑转移(BM)风险高而闻名。虽然早期的荟萃分析支持使用预防性头颅照射(PCI)来降低脑转移发生率并提高总生存率,但现代核磁共振成像技术使人们对PCI是否能普遍惠及局限期SCLC(LS-SCLC)患者产生了疑问。为此,我们创建了一个BM预测模型,旨在识别可能不需要PCI的低风险患者。 方法 我们共纳入了 194 名在 2009 年至 2021 年间接受治疗但未行 PCI 治疗的 LS-SCLC 患者。我们进行了单变量和多变量分析,以确定发生 BM 的相关因素。然后构建了一个预测 2 年和 3 年 BM 发生概率的提名图。 结果 单变量和多变量分析揭示了发生 BM 的几个重要独立风险因素。这些因素包括 TNM 分期、化疗(ChT)周期数、Ki-67 表达水平、治疗前血清乳酸脱氢酶(LDH)水平和血红蛋白(HGB)水平。这些发现强调了它们各自作为 BM 独立预测因子的作用。根据最终的多变量分析结果,建立了一个提名图模型。在训练队列中,该提名图在 2 年和 3 年的接收器操作特征曲线下面积(AUC)分别为 0.870 和 0.828。在验证队列中,2 年和 3 年的 AUC 值分别为 0.897 和 0.789。校准曲线显示,预测的 BM 概率与观察到的 BM 概率之间具有良好的一致性。 结论 已开发出一种新的提名图,用于预测确诊为 LS-SCLC 的患者发生 BM 的可能性。该工具有望帮助医护人员制定更明智、更有针对性的治疗方案。
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Construction of brain metastasis prediction model in limited stage small cell lung cancer patients without prophylactic cranial irradiation

Introduction

Small cell lung cancer (SCLC) is a highly aggressive lung cancer variant known for its elevated risk of brain metastases (BM). While earlier meta-analyses supported the use of prophylactic cranial irradiation (PCI) to reduce BM incidence and enhance overall survival, modern MRI capabilities raise questions about PCI's universal benefit for limited-stage SCLC (LS-SCLC) patients. As a response, we have created a predictive model for BM, aiming to identify low-risk individuals who may not require PCI.

Methods

A total of 194 LS-SCLC patients without PCI treated between 2009 and 2021 were included. We conducted both univariate and multivariate analyses to pinpoint the factors associated with the development of BM. A nomogram for predicting the 2- and 3-year probabilities of BM was then constructed.

Results

Univariate and multivariate analyses revealed several significant independent risk factors for the development of BM. These factors include TNM stage, the number of chemotherapy (ChT) cycles, Ki-67 expression level, pretreatment serum lactate dehydrogenase (LDH) levels, and haemoglobin (HGB) levels. These findings underscore their respective roles as independent predictors of BM. Based on the results of the final multivariable analysis, a nomogram model was created. In the training cohort, the nomogram yielded an area under the receiver operating characteristic curve (AUC) of 0.870 at 2 years and 0.828 at 3 years. In the validation cohort, the AUC values were 0.897 at 2 years and 0.789 at 3 years. The calibration curve demonstrated good agreement between the predicted and observed probabilities of BM.

Conclusions

A novel nomogram has been developed to forecast the likelihood of BM in patients diagnosed with LS-SCLC. This tool holds the potential to assist healthcare professionals in formulating more informed and tailored treatment plans.

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来源期刊
Clinical Respiratory Journal
Clinical Respiratory Journal 医学-呼吸系统
CiteScore
3.70
自引率
0.00%
发文量
104
审稿时长
>12 weeks
期刊介绍: Overview Effective with the 2016 volume, this journal will be published in an online-only format. Aims and Scope The Clinical Respiratory Journal (CRJ) provides a forum for clinical research in all areas of respiratory medicine from clinical lung disease to basic research relevant to the clinic. We publish original research, review articles, case studies, editorials and book reviews in all areas of clinical lung disease including: Asthma Allergy COPD Non-invasive ventilation Sleep related breathing disorders Interstitial lung diseases Lung cancer Clinical genetics Rhinitis Airway and lung infection Epidemiology Pediatrics CRJ provides a fast-track service for selected Phase II and Phase III trial studies. Keywords Clinical Respiratory Journal, respiratory, pulmonary, medicine, clinical, lung disease, Abstracting and Indexing Information Academic Search (EBSCO Publishing) Academic Search Alumni Edition (EBSCO Publishing) Embase (Elsevier) Health & Medical Collection (ProQuest) Health Research Premium Collection (ProQuest) HEED: Health Economic Evaluations Database (Wiley-Blackwell) Hospital Premium Collection (ProQuest) Journal Citation Reports/Science Edition (Clarivate Analytics) MEDLINE/PubMed (NLM) ProQuest Central (ProQuest) Science Citation Index Expanded (Clarivate Analytics) SCOPUS (Elsevier)
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