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This article discusses the development and advancements of magnetic resonance imaging (MRI) in the field of medicine. It highlights the contributions of Paul C. Lauterbur and Raymond Damadian in the invention of MRI and their impact on the field. The article also mentions various MRI techniques and contrast mechanisms that have been introduced over the years, including T1ρ MRI. The focus of the paper by Qian and colleagues is on using dynamic glucose-enhanced T1ρ imaging to study glucose metabolism in the liver of patients with non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). The study demonstrates the potential of this technique in detecting and quantifying changes in liver metabolism. The article concludes by discussing the advantages of this novel application, including real-time functional and metabolic information, non-invasiveness, and improved image quality. However, it acknowledges the limitations of the study and suggests further research in this area. [Extracted from the article]

Background: Sentinel lymph node (SLN) status is an important prognostic factor for patients with breast cancer, which is currently determined in clinical practice by invasive SLN biopsy. Purpose: To noninvasively predict SLN metastasis in breast cancer using dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) intra‐ and peritumoral radiomics features combined with or without clinicopathologic characteristics of the primary tumor. Study Type: Retrospective. Population: A total of 163 breast cancer patients (55 positive SLN and 108 negative SLN). Field Strength/Sequence: 1.5T, T1‐weighted DCE‐MRI. Assessment: A total of 590 radiomic features were extracted for each patient from both intratumoral and peritumoral regions of interest. To avoid overfitting, the dataset was randomly separated into a training set (∼67%) and a validation set (∼33%). The prediction models were built with the training set using logistic regression on the most significant radiomic features in the training set combined with or without clinicopathologic characteristics. The prediction performance was further evaluated in the independent validation set. Statistical Tests: Mann–Whitney U‐test, Spearman correlation, least absolute shrinkage selection operator (LASSO) regression, logistic regression, and receiver operating characteristic (ROC) analysis were performed. Results: Combining radiomic features with clinicopathologic characteristics, six features were automatically selected in the training set to establish the prediction model of SLN metastasis. In the independent validation set, the area under ROC curve (AUC) was 0.869 (NPV = 0.886). Using radiomic features alone in the same procedure, 4 features were selected and the validation set AUC was 0.806 (NPV = 0.824). Data Conclusion: This is the first attempt to demonstrate the feasibility of using DCE‐MRI radiomics to predict SLN metastasis in breast cancer. Clinicopathologic characteristics improved the prediction performance. This study provides noninvasive methods to evaluate SLN status for guiding further treatment of breast cancer patients, and can potentially benefit those with negative SLN, by eliminating unnecessary invasive lymph node removal and the associated complications, which is a step further towards precision medicine. Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:131–140. [ABSTRACT FROM AUTHOR]