Academic Radiology最新文献

Rationale and objectives: This research aimed to develop a combined model based on proximal femur attenuation values and radiomics features at routine CT to predict hip fragility fracture using machine learning methods.

Method: A total of 254 patients (training cohort, n=132; test cohort 1, n=56；test cohort 2, n=66) who underwent hip or pelvic CT scans were included. Three different machine learning methods were used to build the Support Vector Machine (SVM) model, Logistic Regression (LR) model and Random Forest (RF) model respectively. The method that exhibited the best performance in the training cohort and test cohort 1 was selected to represent the radiomics model for subsequent studies. The mean CT Hounsfield unit of three-dimensional CT images at the proximal femur was extracted to construct the mean CTHU model. Multivariate logistic regression was performed using mean CT Hounsfield unit together with radiomics features, and the combined model was subsequently developed with a visualized nomogram.

Results: Among the radiomics models based on three machine learning methods, the LR model showed the best performance in the training cohort (AUC=0.875, 95% CI=0.806-0.926) and in the test cohort 1 (AUC=0.851, 95% CI=0.730-0.932). Compared to the mean CT model and the LR model, the combined model showed superior discriminatory power in the training cohort (AUC=0.934, 95% CI=0.895-0.972), the test cohort 1 (AUC=0.893, 95% CI=0.812-0.974) and the test cohort 2 (AUC=0.851, 95% CI=0.742-0.927).

Conclusion: The combined model, based on the mean CT Hounsfield unit of the proximal femur and radiomics features, can provide an accurate quantitative imaging basis for individualized risk prediction of hip fragility fracture.

Rationale and objectives: To develop and validate a machine learning-based prediction model for the use of multiparametric magnetic resonance imaging(MRI) to predict benign and malignant lesions in the testis.

Materials and methods: The study retrospectively enrolled 148 patients with pathologically confirmed benign and malignant testicular lesions, dividing them into: training set (n=103) and validation set (n=45). Radiomics characteristics were derived from T2-weighted(T2WI)、contrast-enhanced T1-weighted(CE-T1WI)、diffusion-weighted imaging(DWI) and Apparent diffusion coefficient(ADC) MRI images, followed by feature selection. A machine learning-based combined model was developed by incorporating radiomics scores (rad scores) from the optimal radiomics model along with clinical predictors. Draw the receiver operating characteristic (ROC) curve and use the area under the curve (AUC) to evaluate and compare the predictive performance of each model. The diagnostic efficacy of the various machine learning models was evaluated using the Delong test.

Results: Radiomics features were extracted from four sequence-based groups(CE-T1WI+DWI+ADC+T2WI), and the model that combined Logistic Regression(LR) machine learning showed the best performance in the radiomics model. The clinical model identified one independent predictors. The combined clinical-radiomics model showed the best performance, whose AUC value was 0.932(95% confidence intervals(CI)0.868-0.978), sensitivity was 0.875, specificity was 0.871 and accuracy was 0.884 in validation set.

Conclusion: The combined clinical-radiomics model can be used as a reliable tool to predict benign and malignant testicular lesions and provide a reference for clinical treatment method decisions.

Rationale and objectives: The tumor microenvironment (TME) is a critical regulator of cancer progression, metastasis, and treatment response. Currently, various imaging approaches exist to assess the pathophysiological features of the TME. This systematic review provides an overview of magnetic resonance imaging (MRI) methods used in clinical practice to characterize the pathophysiological features of the gliomas TME.

Methods: This review involved a systematic comprehensive search of original open-access articles reporting the clinical use of MR imaging in glioma patients of all ages in the PubMed, Scopus, and Web of Science databases between January 2010 and December 2023. We restricted our research to papers published in the English language.

Results: A total of 1137 studies were preliminarily identified through electronic database searches. After duplicate studies were removed, 44 studies met the eligibility criteria. The glioma TME was accompanied by alterations in metabolism, pH, vascularity, oxygenation, and extracellular matrix components, including tumor-associated macrophages, and sodium concentration.

Conclusion: Multiparametric MRI is capable of noninvasively assessing the pathophysiological features and tumor-supportive niches of the TME, which is in line with its application in personalized medicine.