Metabolic biomarkers for non-alcoholic fatty liver disease induced by high-fat diet: In vivo magnetic resonance spectroscopy of hyperpolarized [1-13C] pyruvate.

Hyperpolarized 13C magnetic resonance spectroscopy (MRS) to assess hepatic metabolism in non-alcoholic fatty liver disease (NAFLD) has not been reported. This study searched for cellular metabolism-based biomarkers for NAFLD induced by a high-fat diet (HFD) in rats. Also, correlations of the biomarkers with enzyme levels and histopathology were identified during a 6-week follow-up. Six rats were fed a control diet (CD) and seven rats were fed the HFD for 6 weeks. Hyperpolarized 13C dynamic MRS was performed on rat liver following an injection of hyperpolarized [1-13C] pyruvate. Compared with CD-fed rats, HFD-fed rats showed significant increases in the levels of serum alanine aminotransferase and low-density lipoprotein cholesterol at weeks 4 and 6 of follow-up. After the 6-week HFD, the ratios of [1-13C] alanine/pyruvate and [1-13C] lactate/pyruvate were significantly increased, as were the levels of alanine aminotransferase and lactate dehydrogenase, which are potentially associated with hepatosteatosis. The results implicate [1-13C] alanine and [1-13C] lactate as potentially useful noninvasive biomarkers of hepatosteatosis occurring in NAFLD.

Predicting the presence of adherent perinephric fat using MRI radiomics combined with machine learning.

OBJECTIVES: Adherent perinephric fat (APF) poses significant challenges to surgical procedures. This study aimed to evaluate the usefulness of machine learning algorithms combined with MRI-based radiomics features for predicting the presence of APF. MATERIALS AND METHODS: Patients with renal cell carcinoma who underwent surgery between April 2019 and February 2022 at Chonnam National University Hwasun Hospital were retrospectively screened, and 119 patients included. Twenty-one and seventeen patients were set aside for the internal and external test sets, respectively. Pre-operative T1-weighted MRI acquired at 60 s following a contrast injection (T1w-60) were collected. For each T1w-60 data, two regions of interest (ROIs) were manually drawn: the perinephric fat tissue and an aorta segment on the same level as the targeted kidney. Preprocessing steps included resizing voxels, N4 Bias Correction filtering, and aorta-based normalization. For each patient, 851 radiomics features were extracted from the ROI of perinephric fat tissue. Gender and BMI were added as clinical factors. Least Absolute Shrinkage and Selection Operator was adopted for feature selection. We trained and evaluated five models using a 4-fold cross validation. The final model was chosen based on the highest mean AUC across four folds. The performance of the final model was evaluated on the internal and external test sets. RESULTS: A total of 15 features were selected in the final set. The final model achieved the accuracy, sensitivity, specificity, and AUC of 81% (95% confidence interval, 61.9-95.2%), 72.7% (42.9-100%), 90% (66.7-100%), and 0.855 (0.615-1.0), respectively on the internal test set, and 88.2% (70.6-100%), 100% (100-100%), 80% (50%-100%), 0.971 (0.871-1.0), respectively on the external test set. CONCLUSIONS: Our study demonstrated the feasibility of machine learning algorithms trained with MRI-based radiomics features for APF prediction. Further studies with a multi-center approach are necessary to validate our findings.