Radiomics models using machine learning algorithms to differentiate the primary focus of brain metastasis.

IF 1.5 4区 医学 Q4 ONCOLOGY
Translational cancer research Pub Date : 2025-02-28 Epub Date: 2025-02-24 DOI:10.21037/tcr-24-1355
Yuping Xie, Xuanzi Li, Shuai Yang, Fujie Jia, Yuanyuan Han, Mingsheng Huang, Lei Chen, Wei Zou, Chuntao Deng, Zibin Liang
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Abstract

Background: Brain metastases are common brain tumors in adults. Brain metastases from different primary tumors have special magnetic resonance imaging (MRI) features. As a new technology that can extract and quantify medical image data, and with the rapid development of artificial intelligence, the machine learning model based on radiology has been successfully applied to the diagnosis and differentiation of tumors. This study aimed to develop radiomics models from post-contrast T1-weighted images using machine learning algorithms to differentiate lung cancer from breast cancer brain metastases.

Methods: A retrospective analysis was conducted on 118 lung cancer brain metastases patients and 62 breast cancer brain metastases patients confirmed by surgery pathology or combined clinical and imaging diagnosis at The Fifth Affiliated Hospital of Sun Yat-sen University from August 2015 to September 2023. Patients were randomly divided into a training set (126 cases) and a validation set (54 cases) at a 7:3 ratio. Enhanced T1-weighted images of all patients were imported into ITK-SNAP software to manually delineate the region of interest (ROI). Radiomic features were extracted based on the ROI and feature selection was performed using the least absolute shrinkage and selection operator. Significant features were used to develop models using logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), multilayer perceptron (MLP), and light gradient boosting machine (LightGBM). The diagnostic performance of the models was assessed using the receiver operating characteristic (ROC) curve.

Results: The LightGBM radiomics model exhibited the best diagnostic performance, with an area under the curve (AUC) of 0.875 [95% confidence interval (CI): 0.819-0.931] in the training set and 0.866 (95% CI: 0.740-0.993) in the validation set.

Conclusions: The enhanced MRI radiomics model, especially the LightGBM model, can accurately predict the primary lesion types of brain metastases from lung cancer and breast cancer origins.

放射组学模型使用机器学习算法来区分脑转移的主要焦点。
背景:脑转移瘤是成人常见的脑肿瘤。不同原发肿瘤的脑转移瘤具有特殊的磁共振成像(MRI)特征。作为一种可以提取和量化医学图像数据的新技术,随着人工智能的快速发展,基于放射学的机器学习模型已成功应用于肿瘤的诊断和鉴别。本研究旨在利用机器学习算法从对比后的t1加权图像中开发放射组学模型,以区分肺癌和乳腺癌脑转移。方法:回顾性分析中山大学附属第五医院2015年8月至2023年9月经手术病理或临床影像学联合诊断证实的118例肺癌脑转移患者和62例乳腺癌脑转移患者。将患者按7:3的比例随机分为训练组(126例)和验证组(54例)。将所有患者的增强t1加权图像导入ITK-SNAP软件,手动划定感兴趣区域(ROI)。基于ROI提取放射学特征,使用最小绝对收缩算子和选择算子进行特征选择。利用显著特征,利用逻辑回归(LR)、支持向量机(SVM)、k近邻(KNN)、多层感知机(MLP)和光梯度增强机(LightGBM)建立模型。采用受试者工作特征(ROC)曲线评估模型的诊断性能。结果:LightGBM放射组学模型表现出最好的诊断性能,训练集的曲线下面积(AUC)为0.875[95%置信区间(CI): 0.819-0.931],验证集的AUC为0.866 (95% CI: 0.740-0.993)。结论:增强MRI放射组学模型,尤其是LightGBM模型能够准确预测肺癌和乳腺癌脑转移的原发病灶类型。
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来源期刊
CiteScore
2.10
自引率
0.00%
发文量
252
期刊介绍: Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.
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