文章：

利用基于放射组学的机器学习模型识别结直肠癌肝转移患者的基因突变状态

Identifying Genetic Mutation Status in Patients with Colorectal Cancer Liver Metastases Using Radiomics-Based Machine-Learning Models

原文发布日期：29 November 2023

DOI: 10.3390/cancers15235648

类型: Article

开放获取: 是

英文摘要：

For patients with colorectal cancer liver metastases (CRLM), the genetic mutation status is important in treatment selection and prognostication for survival outcomes. This study aims to investigate the relationship between radiomics imaging features and the genetic mutation status (KRAS mutation versus no mutation) in a large multicenter dataset of patients with CRLM and validate these findings in an external dataset. Patients with initially unresectable CRLM treated with systemic therapy of the randomized controlled CAIRO5 trial (NCT02162563) were included. All CRLM were semi-automatically segmented in pre-treatment CT scans and radiomics features were calculated from these segmentations. Additionally, data from the Netherlands Cancer Institute (NKI) were used for external validation. A total of 255 patients from the CAIRO5 trial were included. Random Forest, Gradient Boosting, Gradient Boosting + LightGBM, and Ensemble machine-learning classifiers showed AUC scores of 0.77 (95%CI 0.62–0.92), 0.77 (95%CI 0.64–0.90), 0.72 (95%CI 0.57–0.87), and 0.86 (95%CI 0.76–0.95) in the internal test set. Validation of the models on the external dataset with 129 patients resulted in AUC scores of 0.47–0.56. Machine-learning models incorporating CT imaging features could identify the genetic mutation status in patients with CRLM with a good accuracy in the internal test set. However, in the external validation set, the models performed poorly. External validation of machine-learning models is crucial for the assessment of clinical applicability and should be mandatory in all future studies in the field of radiomics.

摘要翻译： 

对于结直肠癌肝转移（CRLM）患者而言，基因突变状态在治疗方案选择和生存预后评估中具有重要意义。本研究旨在基于大型多中心CRLM患者数据集，探究影像组学特征与基因突变状态（KRAS突变型与非突变型）之间的关联，并在外部数据集中验证研究结果。研究纳入了随机对照试验CAIRO5（NCT02162563）中接受系统治疗的初始不可切除CRLM患者。所有患者在治疗前均接受CT扫描，通过半自动分割技术提取病灶区域并计算影像组学特征。此外，研究采用荷兰癌症研究所（NKI）的数据进行外部验证。 CAIRO5试验共纳入255例患者。在内部测试集中，随机森林、梯度提升、梯度提升+LightGBM及集成机器学习分类器的受试者工作特征曲线下面积（AUC）分别为0.77（95%CI 0.62–0.92）、0.77（95%CI 0.64–0.90）、0.72（95%CI 0.57–0.87）和0.86（95%CI 0.76–0.95）。在包含129例患者的外部验证集中，模型验证的AUC值为0.47–0.56。 整合CT影像特征的机器学习模型在内部测试集中能较准确识别CRLM患者的基因突变状态，但在外部验证集中表现欠佳。机器学习模型的外部验证对评估临床适用性至关重要，未来所有影像组学研究均应强制纳入外部验证环节。

原文链接：

Identifying Genetic Mutation Status in Patients with Colorectal Cancer Liver Metastases Using Radiomics-Based Machine-Learning Models