用于预测接受明确放化疗的局部晚期非小细胞肺癌患者预后的放射基因组学模型。

IF 4 2区 医学 Q2 ONCOLOGY
Translational lung cancer research Pub Date : 2024-08-31 Epub Date: 2024-08-28 DOI:10.21037/tlcr-24-145
Xiaoyu Song, Li Li, Qingxi Yu, Ning Liu, Shouhui Zhu, Shuanghu Yuan
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引用次数: 0

摘要

背景:确定性化放疗(dCRT)是治疗局部晚期非小细胞肺癌(LA-NSCLC)的基石。该研究旨在构建一个多组学模型,整合基线临床数据、计算机断层扫描(CT)图像和遗传信息,以预测LA-NSCLC患者dCRT的预后:研究回顾性地纳入了105名接受过dCRT治疗的III期LA-NSCLC患者。收集治疗前的 CT 图像,使用 3D-Slicer 在图像上划分原发肿瘤的感兴趣区(ROI),并提取放射组学特征。采用最小绝对收缩和选择算子(LASSO)进行降维和特征选择。基因组信息是从基线肿瘤组织样本中获取的。然后,我们结合基线临床数据、放射组学和基因组学特征构建了一个多组学模型。该模型的预测性能通过接收者操作特征曲线下面积(AUC)和一致性指数(C-index)进行评估:中位随访时间为30.1个月,中位无进展生存期(PFS)为10.60个月。四个特征被用于构建放射组学模型。多变量分析表明,Rad-score、KEAP1和MET突变是PFS的独立预后因素。放射组学模型、基因组学模型和放射基因组学模型的C指数在训练组(0.590 vs. 0.606 vs. 0.663)和验证组(0.599 vs. 0.594 vs. 0.650)均表现良好:结论:放射组学模型、基因组学模型和放射基因组学模型都能预测LA-NSCLC dCRT的预后,且放射基因组学模型优于单一类型模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Radiogenomics models for predicting prognosis in locally advanced non-small cell lung cancer patients undergoing definitive chemoradiotherapy.

Background: Definitive chemoradiotherapy (dCRT) is the cornerstone for locally advanced non-small cell lung cancer (LA-NSCLC). The study aimed to construct a multi-omics model integrating baseline clinical data, computed tomography (CT) images and genetic information to predict the prognosis of dCRT in LA-NSCLC patients.

Methods: The study retrospectively enrolled 105 stage III LA-NSCLC patients who had undergone dCRT. The pre-treatment CT images were collected, and the primary tumor was delineated as a region of interest (ROI) on the image using 3D-Slicer, and the radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) was employed for dimensionality reduction and selection of features. Genomic information was obtained from the baseline tumor tissue samples. We then constructed a multi-omics model by combining baseline clinical data, radiomics and genomics features. The predictive performance of the model was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) and the concordance index (C-index).

Results: The median follow-up time was 30.1 months, and the median progression-free survival (PFS) was 10.60 months. Four features were applied to construct the radiomics model. Multivariable analysis demonstrated the Rad-score, KEAP1 and MET mutations were independent prognostic factors for PFS. The C-index of radiomics model, genomics model and radiogenomics model all performed well in the training group (0.590 vs. 0.606 vs. 0.663) and the validation group (0.599 vs. 0.594 vs. 0.650).

Conclusions: The radiomics model, genomics model and radiogenomics model can all predict the prognosis of dCRT for LA-NSCLC, and the radiogenomics model is superior to the single type model.

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来源期刊
CiteScore
7.20
自引率
2.50%
发文量
137
期刊介绍: Translational Lung Cancer Research(TLCR, Transl Lung Cancer Res, Print ISSN 2218-6751; Online ISSN 2226-4477) is an international, peer-reviewed, open-access journal, which was founded in March 2012. TLCR is indexed by PubMed/PubMed Central and the Chemical Abstracts Service (CAS) Databases. It is published quarterly the first year, and published bimonthly since February 2013. It provides practical up-to-date information on prevention, early detection, diagnosis, and treatment of lung cancer. Specific areas of its interest include, but not limited to, multimodality therapy, markers, imaging, tumor biology, pathology, chemoprevention, and technical advances related to lung cancer.
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