Elaboration of a multimodal MRI-based radiomics signature for the preoperative prediction of the histological subtype in patients with non-small-cell lung cancer

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.

Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

“…Many studies have shown that radiomics features have great potential to be the maker for tumor phenotype (8)(9)(10)(11)(12)(13)(14)(15)(16)(17), and found Adc can be differentiated from Sqc by radiomics (17)(18)(19)(20)(21)(22)(23). However, The data sets of those studies only included Adc and Sqc, that is to say, the accuracy of those models will be affected by other histological subtypes of lung cancer.…”

The Invasion Depth Measurement of Bladder Cancer based on MRI

“…After the feature extraction from each voxel, a total of 1159 features were generated for each voxel to characterize its properties and group. Considering that not all the features contributed greatly to classify the group of each voxel, and feature redundancy might actually exist and impair the capability of the classification model, feature selection was performed using support vector machine (SVM)-recursive feature elimination (RFE) approach [24][25][26]. The results were shown in Fig.…”

The Invasion Depth Measurement of Bladder Cancer based on MRI

“…After the feature extraction from each voxel, a total of 1159 features were generated for each voxel to characterize its properties and group. Considering that not all the features contributed greatly to classify the group of each voxel, and feature redundancy might actually exist and impair the capability of the classi cation model, feature selection was performed using support vector machine (SVM)-recursive feature elimination (RFE) approach [24][25][26]. The results were shown in Fig.…”