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The classification of the breast imaging-reporting and data system (BI-RADS) based on magnetic resonance imaging (MRI) refers to the classification of the degree of lesions according to the image signs of lesions, which is usually subjective. Moreover, the benign and malignant lesions of BI-RADS 3-5 are overlapping, which is prone to unnecessary invasive treatment due to high diagnostic categories in clinical diagnosis. To address these problems, this research applied radiomics for feature extraction and fusion of T1-weighted (T1W) and dynamic contrast-enhanced (DEC) MRI. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen out the optimal feature collection of each type of MR image. Support vector machine (SVM), random forest (RF), K-nearest neighbour (KNN) and logistic regression (LR) algorithms were applied for BI-RADS 3-5 classification, based on which the benign and malignant lesions were further classified. The results showed that the classification accuracy of breast BI-RADS 3-5 by four radiomics models based on feature fusion was 81.25%, 87.50%, 78.38%, and 81.25%, respectively. Their accuracy in distinguishing the benign and malignant breast lesions was 90.91%, 93.55%, 92.73%, and 94.55%, respectively. This indicates that the combination of radiomics and machine learning correlation algorithm has a good effect on breast MRI BI-RADS classification and benign and malignant differentiation, and feature fusion can further improve the accuracy of classification prediction.

BACKGROUND: Liver metastases is a pivotal factor of death in patients with colorectal cancer. The longitudinal data of colorectal liver metastases (CRLM) during treatment can monitor and reflect treatment efficacy and outcomes. OBJECTIVE: The objective of this study is to establish a radiomic model based on longitudinal magnetic resonance imaging (MRI) to predict chemotherapy response in patients with CRLM. METHODS: This study retrospectively enrolled longitudinal MRI data of five modalities on 100 patients. According to Response Evaluation Criteria in Solid Tumors (RECIST 1.1), 42 and 58 patients were identified as responders and non-responders, respectively. First, radiomic features were computed from different modalities of image data acquired pre-treatment and early-treatment, as well as their differences (Δ). Next, the features were screened by a two-sample t-test, max-relevance and min-redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO). Then, several ensemble radiomic models that integrate support vector machine (SVM), k-nearest neighbor (KNN), gradient boost decision tree (GBDT) and multi-layer perceptron (MLP) were established based on voting method to predict chemotherapy response. Data samples were divided into training and verification queues using a ratio of 8:2. Finally, we used the area under ROC curve (AUC) to evaluate model performance. RESULTS: Using the ensemble model developed using featue differences (Δ) computed from the longitudinal apparent diffusion coefficient (ADC) images, AUC is 0.9007±0.0436 for the training cohort. Applying to the testing cohort, AUC is 0.8958 and overall accuracy is 0.9. CONCLUSIONS: Study results demonstrate advantages and high performance of the ensemble radiomic model based on the radiomics feature difference of the longitudinal ADC images in predicting chemotherapy response, which has potential to assist treatment decision-making and improve clinical outcome. [ABSTRACT FROM AUTHOR]

PURPOSE: To investigate the value of a CT-based radiomics model in identification of Crohn's disease (CD) active phase and remission phase. METHODS: CT images of 101 patients diagnosed with CD were retrospectively collected, which included 60 patients in active phase and 41 patients in remission phase. These patients were randomly divided into training group and test group at a ratio of 7 : 3. First, the lesion areas were manually delineated by the physician. Meanwhile, radiomics features were extracted from each lesion. Next, the features were selected by t-test and the least absolute shrinkage and selection operator regression algorithm. Then, several machine learning models including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to construct CD activity classification models respectively. Finally, the soft-voting mechanism was used to integrate algorithms with better effects to perform two classifications of data, and the receiver operating characteristic curves were applied to evaluate the diagnostic value of the models. RESULTS: Both on the training set and the test set, AUC of the five machine learning classification models reached 0.85 or more. The ensemble soft-voting classifier obtained by using the combination of SVM, LR and KNN could better distinguish active CD from CD remission. For the test set, AUC was 0.938, and accuracy, sensitivity, and specificity were 0.903, 0.911, and 0.892, respectively. CONCLUSION: This study demonstrated that the established radiomics model could objectively and effectively diagnose CD activity. The integrated approach has better diagnostic performance. [ABSTRACT FROM AUTHOR]