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There are several important challenges in radiomics research; one of them is feature selection. Since many quantitative features are non-informative, feature selection becomes essential. Feature selection methods have been mixed with filter, wrapper, and embedded methods without a rule of thumb. This study aims to develop a framework for optimal feature selection in radiomics research. We developed the framework that the optimal features were selected to quickly through controlling relevance and redundancy among features. A ‘FeatureMap’ was generated containing information for each step and used as a platform. Through this framework, we can explore the optimal combination of radiomics features and evaluate the predictive performance using only selected features. We assessed the framework using four real datasets. The FeatureMap generated 6 combinations, with the number of features selected varying for each combination. The predictive models obtained high performances; the highest test area under the curves (AUCs) were 0.792, 0.820, 0.846 and 0.738 in the cross-validation method, respectively. We developed a flexible framework for feature selection methods in radiomics research and assessed its usefulness using various real-world data. Our framework can assist clinicians in efficiently developing predictive models based on radiomics. [ABSTRACT FROM AUTHOR]

Transarterial chemoembolization (TACE) represent the standard of therapy for non-operative hepatocellular carcinoma (HCC), while prediction of long term treatment outcomes is a complex and multifactorial task. In this study, we present a novel machine learning approach utilizing radiomics features from multiple organ volumes of interest (VOIs) to predict TACE outcomes for 252 HCC patients. Unlike conventional radiomics models requiring laborious manual segmentation limited to tumoral regions, our approach captures information comprehensively across various VOIs using a fully automated, pretrained deep learning model applied to pre-TACE CT images. Evaluation of radiomics random survival forest models against clinical ones using Cox proportional hazard demonstrated comparable performance in predicting overall survival. However, radiomics outperformed clinical models in predicting progression-free survival. Explainable analysis highlighted the significance of non-tumoral VOI features, with their cumulative importance superior to features from the largest liver tumor. The proposed approach overcomes the limitations of manual VOI segmentation, requires no radiologist input and highlight the clinical relevance of features beyond tumor regions. Our findings suggest the potential of this radiomics models in predicting TACE outcomes, with possible implications for other clinical scenarios. [ABSTRACT FROM AUTHOR]

Defining the exact histological features of salivary gland malignancies before treatment remains an unsolved problem that compromises the ability to tailor further therapeutic steps individually. Radiomics, a new methodology to extract quantitative information from medical images, could contribute to characterizing the individual cancer phenotype already before treatment in a fast and non-invasive way. Consequently, the standardization and implementation of radiomic analysis in the clinical routine work to predict histology of salivary gland cancer (SGC) could also provide improvements in clinical decision-making. In this study, we aimed to investigate the potential of radiomic features as imaging biomarker to distinguish between high grade and low-grade salivary gland malignancies. We have also investigated the effect of image and feature level harmonization on the performance of radiomic models. For this study, our dual center cohort consisted of 126 patients, with histologically proven SGC, who underwent curative-intent treatment in two tertiary oncology centers. We extracted and analyzed the radiomics features of 120 pre-therapeutic MRI images with gadolinium (T1 sequences), and correlated those with the definitive post-operative histology. In our study the best radiomic model achieved average AUC of 0.66 and balanced accuracy of 0.63. According to the results, there is significant difference between the performance of models based on MRI intensity normalized images + harmonized features and other models (p value < 0.05) which indicates that in case of dealing with heterogeneous dataset, applying the harmonization methods is beneficial. Among radiomic features minimum intensity from first order, and gray level-variance from texture category were frequently selected during multivariate analysis which indicate the potential of these features as being used as imaging biomarker. The present bicentric study presents for the first time the feasibility of implementing MR-based, handcrafted radiomics, based on T1 contrast-enhanced sequences and the ComBat harmonization method in an effort to predict the formal grading of salivary gland carcinoma with satisfactory performance. [ABSTRACT FROM AUTHOR]

This study aimed to explore the ability of radiomics derived from both MRI and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) images to differentiate glioblastoma (GBM) from solitary brain metastases (SBM) and to investigate the combined application of multiple models. The imaging data of 100 patients with brain tumours (50 GBMs and 50 SBMs) were retrospectively analysed. Three model sets were built on MRI, 18F-FDG-PET, and MRI combined with 18F-FDG-PET using five feature selection methods and five classification algorithms. The model set with the highest average AUC value was selected, in which some models were selected and divided into Groups A, B, and C. Individual and joint voting predictions were performed in each group for the entire data. The model set based on MRI combined with 18F-FDG-PET had the highest average AUC compared with isolated MRI or 18F-FDG-PET. Joint voting prediction showed better performance than the individual prediction when all models reached an agreement. In conclusion, radiomics derived from MRI and 18F-FDG-PET could help differentiate GBM from SBM preoperatively. The combined application of multiple models can provide greater benefits. [ABSTRACT FROM AUTHOR]

Making timely assessments of disease progression in patients with COVID-19 could help offer the best personalized treatment. The purpose of this study was to explore an effective model to predict the outcome of patients with COVID-19. We retrospectively included 188 patients (124 in the training set and 64 in the test set) diagnosed with COVID-19. Patients were divided into aggravation and improvement groups according to the disease progression. Three kinds of models were established, including the radiomics, clinical, and combined model. Receiver operating characteristic curves, decision curves, and Delong's test were used to evaluate and compare the models. Our analysis showed that all the established prediction models had good predictive performance in predicting the progress and outcome of COVID-19. [ABSTRACT FROM AUTHOR]