Characterization of renal masses with MRI-based radiomics: assessment of inter-package and inter-observer reproducibility in a prospective pilot study.

OBJECTIVES: To evaluate radiomics features' reproducibility using inter-package/inter-observer measurement analysis in renal masses (RMs) based on MRI and to employ machine learning (ML) models for RM characterization. METHODS: 32 Patients (23M/9F; age 61.8 ± 10.6 years) with RMs (25 renal cell carcinomas (RCC)/7 benign masses; mean size, 3.43 ± 1.73 cm) undergoing resection were prospectively recruited. All patients underwent 1.5 T MRI with T2-weighted (T2-WI), diffusion-weighted (DWI)/apparent diffusion coefficient (ADC), and pre-/post-contrast-enhanced T1-weighted imaging (T1-WI). RMs were manually segmented using volume of interest (VOI) on T2-WI, DWI/ADC, and T1-WI pre-/post-contrast imaging (1-min, 3-min post-injection) by two independent observers using two radiomics software packages for inter-package and inter-observer assessments of shape/histogram/texture features common to both packages (104 features; n = 26 patients). Intra-class correlation coefficients (ICCs) were calculated to assess inter-observer and inter-package reproducibility of radiomics measurements [good (ICC ≥ 0.8)/moderate (ICC = 0.5-0.8)/poor (ICC < 0.5)]. ML models were employed using reproducible features (between observers and packages, ICC > 0.8) to distinguish RCC from benign RM. RESULTS: Inter-package comparisons demonstrated that radiomics features from T1-WI-post-contrast had the highest proportion of good/moderate ICCs (54.8-58.6% for T1-WI-1 min), while most features extracted from T2-WI, T1-WI-pre-contrast, and ADC exhibited poor ICCs. Inter-observer comparisons found that radiomics measurements from T1-WI pre/post-contrast and T2-WI had the greatest proportion of features with good/moderate ICCs (95.3-99.1% T1-WI-post-contrast 1-min), while ADC measurements yielded mostly poor ICCs. ML models generated an AUC of 0.71 [95% confidence interval = 0.67-0.75] for diagnosis of RCC vs. benign RM. CONCLUSION: Radiomics features extracted from T1-WI-post-contrast demonstrated greater inter-package and inter-observer reproducibility compared to ADC, with fair accuracy for distinguishing RCC from benign RM. CLINICAL RELEVANCE: Knowledge of reproducibility of MRI radiomics features obtained on renal masses will aid in future study design and may enhance the diagnostic utility of radiomics models for renal mass characterization.

Metabolic Biomarkers in Adults with Type 2 Diabetes: The Role of PPAR-γ2 and PPAR-ß/δ Polymorphisms.

Glucose and lipid metabolism regulation by the peroxisome proliferator-activated receptors (PPARs) has been extensively reported. However, the role of their polymorphisms remains unclear. OBJECTIVE: To determine the relation between PPAR-γ2 rs1801282 (Pro12Ala) and PPAR-ß/δ rs2016520 (+294T/C) polymorphisms and metabolic biomarkers in adults with type 2 diabetes (T2D). MATERIALS AND METHODS: We included 314 patients with T2D. Information on anthropometric, fasting plasma glucose (FPG), HbA1c and lipid profile measurements was taken from clinical records. Genomic DNA was obtained from peripheral blood. End-point PCR was used for PPAR-γ2 rs1801282, while for PPAR-ß/δ rs2016520 the PCR product was digested with Bsl-I enzyme. Data were compared with parametric or non-parametric tests. Multivariate models were used to adjust for covariates and interaction effects. RESULTS: minor allele frequency was 12.42% for PPAR-γ2 rs1801282-G and 13.85% for PPAR-ß/δ rs2016520-C. Both polymorphisms were related to waist circumference; they showed independent effects on HbA1c, while they interacted for FPG; carriers of both PPAR minor alleles had the highest values. Interactions between FPG and polymorphisms were identified in their relation to triglyceride level. CONCLUSIONS: PPAR-γ2 rs1801282 and PPAR-ß/δ rs2016520 polymorphisms are associated with anthropometric, glucose, and lipid metabolism biomarkers in T2D patients. Further research is required on the molecular mechanisms involved.

Pro12Ala PPAR-γ2 and +294T/C PPAR-δ Polymorphisms and Association with Metabolic Traits in Teenagers from Northern Mexico.

Peroxisome proliferator-activated receptors (PPARs) play roles in glucose and lipid metabolism regulation. Pro12Ala PPAR-γ2 and +294T/C PPAR-δ have been associated with dyslipidemia, hyperglycemia and high body mass index (BMI). We compared metabolic traits and determined associations with Pro12Ala PPAR-γ2 or +294T/C PPAR-δ polymorphism among teenagers from different ethnicity. Four hundred and twelve samples with previous biochemical and biometric measurements were used. Genomic DNA from peripheral blood was extracted and analyzed by end-point PCR for Pro12Ala PPAR-γ2. The +294T/C PPAR-δ PCR product was also digested with Bsl I. Two genotype groups were formed: major allele homozygous and minor allele carriers. Pro12Ala PPAR-γ2 G minor allele frequencies were: 10% in Mestizo-1, 19% in Mestizo-2, 23% in Tarahumara, 12% in Mennonite, and 17% in the total studied population. The +294T/C PPAR-δ C minor allele frequencies were: 18% in Mestizo-1, 20% in Mestizo-2, 6% in Tarahumara, 13% in Mennonite, and 12% in the total studied population. Teenagers with PPAR-γ2 G allele showed a greater risk for either high waist/height ratio or low high-density lipoprotein; and, also had lower total cholesterol. Whereas, PPAR-γ2 G allele showed lower overweight/obesity phenotype (BMI Z-score) frequency, PPAR-δ C allele was a risk factor for it. Metabolic traits were associated with both PPAR polymorphisms.