Radiomic signatures of brain metastases on MRI: utility in predicting pathological subtypes of lung cancer

被引：0

作者：

Sun, Linlin ^{[1
]}

Luan, Shihai ^{[2
,3
,4
]}

Yu, Liheng ^{[5
]}

Zhu, Huiyuan ^{[1
]}

Dong, Haiyang ^{[1
]}

Liu, Xuemei ^{[1
]}

Tao, Guangyu ^{[1
]}

He, Pengbo ^{[6
,7
,8
]}

Li, Qiang ^{[6
,7
,8
]}

Chen, Weiqiang ^{[6
,7
,8
,9
]}

Yu, Zekuan ^{[5
]}

Yu, Hong ^{[1
]}

Zhu, Li ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Shanghai Chest Hosp, Sch Med, Dept Radiol, 241 Huaihai West Rd, Shanghai 200030, Peoples R China

[2] Fudan Univ, Huashan Hosp, Dept Neurosurg, Shanghai, Peoples R China

[3] Fudan Univ, Inst Neurosurg, Shanghai, Peoples R China

[4] Fudan Univ, Shanghai Key Lab Brain Funct Restorat & Neural Reg, Shanghai, Peoples R China

[5] Fudan Univ, Acad Engn & Technol, Shanghai, Peoples R China

[6] Chinese Acad Sci, Inst Modern Phys, Lanzhou, Peoples R China

[7] Key Lab Heavy Ion Radiat Biol & Med, Lanzhou, Gansu, Peoples R China

[8] Key Lab Basic Res Heavy Ion Radiat Applicat Med, Lanzhou, Gansu, Peoples R China

[9] Univ Chinese Acad Sci, Inst Modern Phys, Beijing, Peoples R China

来源：

TRANSLATIONAL CANCER RESEARCH | 2024年 / 13卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Radiomics; brain metastases (BM); lung cancer; magnetic resonance imaging (MRI); machine learning (ML); TEXTURE ANALYSIS; HIGH-RESOLUTION; HIGH-FIELD; CONTRAST; EPIDEMIOLOGY; IMAGES;

D O I：

10.21037/tcr-24-1147

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Background: The pathological sub-classification of lung cancer is crucial in diagnosis, treatment and prognosis for patients. Quick and timely identification of pathological subtypes from imaging examinations rather than histological tests could help guiding therapeutic strategies. The aim of the study is to construct a non-invasive radiomics-based model for predicting the subtypes of lung cancer on brain metastases (BMs) from multiple magnetic resonance imaging (MRI) sequences. Methods: One hundred and sixty-one patients of primary lung cancer with synchronous BMs [121 with adenocarcinoma (AD); 40 with small cell lung carcinoma (SCLC)] were enrolled in the study (129 and 32 in the training set and validation set). A total of 960 radiomics features were extracted from multiple MRI sequences [fluid attenuated inversion recovery (FLAIR), diffusion weighted imaging (DWI), contrast- enhanced T1 weighted imaging (CE-T1WI) and contrast-enhanced susceptibility weighted imaging (CESWI)] and four clinical features were recorded. Forty-one key features were selected by the least absolute shrinkage selection operator (LASSO). The machine learning (ML) models for predicting AD and SCLC with radiomics features alone and with radiomics features plus clinical features were constructed using classifiers of logistic regression (LR), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost). The prediction performance of models was evaluated by accuracy (ACC), sensitivity (SEN), specificity (SPE), F1 score and area under the curves (AUC). Results: The AUCs of LR, RF, SVM and XGBoost models were 0.8177 vs. 0.7604, 0.8177 vs. 0.7839, 0.4792 vs. 0.8594 and 0.9062 vs. 0.8750, respectively, when using radiomics features alone and radiomics features plus clinical features. In the best-performing model using XGBoost, combination of conventional MRI sequences and CE-SWI had better sub-classification performance than conventional MRI sequences. Conclusions: Radiomics of BMs from multiple MRI sequences provides high discriminatory performance in predicting AD and SCLC using classifiers of LR, RF and XGBoost, and can serve as a potential useful tool to non-invasively distinguish pathological subtypes of lung cancer.

引用

页码：6825 / 6836

页数：15

共 33 条

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