Magnetic resonance radiomic feature performance in pulmonary nodule classification and impact of segmentation variability on radiomics.

OBJECTIVE: Investigate the performance of multiparametric MRI radiomic features, alone or combined with current standard-of-care methods, for pulmonary nodule classification. Assess the impact of segmentation variability on feature reproducibility and reliability. METHODS: Radiomic features were extracted from 74 pulmonary nodules of 68 patients who underwent nodule resection or biopsy after MRI exam. The MRI features were compared with histopathology and conventional quantitative imaging values (maximum standardized uptake value [SUVmax] and mean Hounsfield unit [HU]) to determine whether MRI radiomic features can differentiate types of nodules and associate with SUVmax and HU using Wilcoxon rank sum test and linear regression. Diagnostic performance of features and four machine learning (ML) models were evaluated with area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs). Concordance correlation coefficient (CCC) assessed the segmentation variation impact on feature reproducibility and reliability. RESULTS: Elevn diffusion-weighted features distinguished malignant from benign nodules (adjusted p < 0.05, AUC: 0.73-0.81). No features differentiated cancer types. Sixty-seven multiparametric features associated with mean CT HU and 14 correlated with SUVmax. All significant MRI features outperformed traditional imaging parameters (SUVmax, mean HU, apparent diffusion coefficient [ADC], T1, T2, dynamic contrast-enhanced imaging values) in distinguishing malignant from benign nodules with some achieving statistical significance (p < 0.05). Adding ADC and smoking history improved feature performance. Machine learning models demonstrated strong performance in nodule classification, with extreme gradient boosting (XGBoost) having the highest discrimination (AUC = 0.83, CI=[0.727, 0.932]). We found good to excellent inter- and intrareader feature reproducibility and reliability (CCC≥0.80). CONCLUSION: Eleven MRI radiomic features differentiated malignant from benign lung nodules, outperforming traditional quantitative methods. MRI radiomic ML models demonstrated good nodule classification performances with XGBoost superior to three others. There was good to excellent inter- and intrareader feature reproducibility and reliability. ADVANCES IN KNOWLEDGE: Our study identified MRI radiomic features that successfully differentiated malignant from benign lung nodules and demonstrated high performance of our MR radiomic feature-based ML models for nodule classification. These new findings could help further establish thoracic MRI as a non-invasive and radiation-free alternative to standard practice for pulmonary nodule assessment.

A chromosome 11q quantitative-trait locus influences change of blood-pressure measurements over time in Mexican Americans of the San Antonio Family Heart Study.

Although previous genome scans have searched for quantitative-trait loci (QTLs) influencing variation in blood pressure (BP), few have investigated the rate of change in BP over time as a phenotype. Here, we compare results from genomewide scans to localize QTLs for systolic, diastolic, and mean arterial BPs (SBP, DBP, and MBP, respectively) and for rates of change in systolic, diastolic, and mean arterial BPs (rSBP, rDBP, and rMBP, respectively), with use of the longitudinal data collected about Mexican Americans of the San Antonio Family Heart Study (SAFHS). Significant evidence of linkage was found for rSBP (LOD 4.15) and for rMBP (LOD 3.94) near marker D11S4464 located on chromosome 11q24.1. This same chromosome 11q region also shows suggestive linkage to SBP (LOD 2.23) and MBP (LOD 2.37) measurements collected during the second clinic visit. Suggestive evidence of linkage to chromosome 5 was also found for rMBP, to chromosome 16 for rSBP, and to chromosomes 1, 5, 6, 7, and 21 for the single-time-point BP traits collected at the first two SAFHS clinic visits. We also present results from fine mapping the chromosome 11 QTL with use of SNP-association analysis within candidate genes identified from a bioinformatic search of the region and from whole-genome transcriptional expression data collected from 1,240 SAFHS participants. Our results show that the use of longitudinal BP data to calculate the rate of change in BP over time provides more information than do the single-time measurements, since they reveal physiological trends in the subjects that a single-time measurement could never capture. Further investigation of this region is necessary for the identification of the genetic variation responsible for QTLs influencing the rate of change in BP.