Author:
Tang Xing,Xu Xiaopan,Han Zhiping,Bai Guoyan,Wang Hong,Liu Yang,Du Peng,Liang Zhengrong,Zhang Jian,Lu Hongbing,Yin Hong
Abstract
Abstract
Background
Non-invasive discrimination between lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) subtypes of non-small-cell lung cancer (NSCLC) could be very beneficial to the patients unfit for the invasive diagnostic procedures. The aim of this study was to investigate the feasibility of utilizing the multimodal magnetic resonance imaging (MRI) radiomics and clinical features in classifying NSCLC. This retrospective study involved 148 eligible patients with postoperative pathologically confirmed NSCLC. The study was conducted in three steps: (1) feature extraction was performed using the online freely available package with the multimodal MRI data; (2) feature selection was performed using the Student’s t test and support vector machine (SVM)-based recursive feature elimination method with the training cohort (n = 100), and the performance of these selected features was evaluated using both the training and the validation cohorts (n = 48) with a non-linear SVM classifier; (3) a Radscore model was then generated using logistic regression algorithm; (4) Integrating the Radscore with the semantic clinical features, a radiomics–clinical nomogram was developed, and its overall performance was evaluated with both cohorts.
Results
Thirteen optimal features achieved favorable discrimination performance with both cohorts, with area under the curve (AUC) of 0.819 and 0.824, respectively. The radiomics–clinical nomogram integrating the Radscore with the independent clinical predictors exhibited more favorable discriminative power, with AUC improved to 0.901 and 0.872 in both cohorts, respectively. The Hosmer–Lemeshow test and decision curve analysis results furtherly showed good predictive precision and clinical usefulness of the nomogram.
Conclusion
Non-invasive histological subtype stratification of NSCLC can be done favorably using multimodal MRI radiomics features. Integrating the radiomics features with the clinical features could further improve the performance of the histological subtype stratification in patients with NSCLC.
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
Shaanxi Research and development plan
Publisher
Springer Science and Business Media LLC
Subject
Radiology Nuclear Medicine and imaging,Biomedical Engineering,General Medicine,Biomaterials,Radiological and Ultrasound Technology
Cited by
25 articles.
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