Elaboration of a multimodal MRI-based radiomics signature for the preoperative prediction of the histological subtype in patients with non-small-cell lung cancer
| Autor: | Xiaopan Xu, Hongbing Lu, Yang Liu, Hong Wang, Zhengrong Liang, Hong Yin, Jian Zhang, Guoyan Bai, Zhiping Han, Xing Tang, Peng Du |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Lung adenocarcinoma
Adult Male medicine.medical_specialty lcsh:Medical technology Lung Neoplasms Support Vector Machine Biomedical Engineering Feature selection Logistic regression Nomogram 030218 nuclear medicine & medical imaging Biomaterials 03 medical and health sciences Young Adult 0302 clinical medicine Carcinoma Non-Small-Cell Lung Lung squamous cell carcinoma medicine Image Processing Computer-Assisted Humans Radiology Nuclear Medicine and imaging Lung cancer Aged Aged 80 and over Radiological and Ultrasound Technology medicine.diagnostic_test business.industry Research Area under the curve Magnetic resonance imaging Retrospective cohort study Clinical features General Medicine Middle Aged medicine.disease Multimodal MRI radiomics features Magnetic Resonance Imaging lcsh:R855-855.5 030220 oncology & carcinogenesis Preoperative Period Adenocarcinoma Female Radiology business Non-small-cell lung cancer |
| Zdroj: | BioMedical Engineering BioMedical Engineering OnLine, Vol 19, Iss 1, Pp 1-17 (2020) |
| ISSN: | 1475-925X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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