Diffusion Tensor MRI is sensitive to fibrotic injury in a mouse model of oxalate induced chronic kidney disease.

Chronic kidney disease (CKD) is characterized by inflammation and fibrosis in the kidney. Renal biopsies and estimated glomerular filtration rate (eGFR) remain the standard of care, but these endpoints have limitations in detecting the stage, progression, and spatial distribution of fibrotic pathology in the kidney. MRI diffusion tensor imaging (DTI) has emerged as a promising non-invasive technology to evaluate renal fibrosis in vivo both in clinical and preclinical studies. However, these imaging studies have not systematically identified fibrosis particularly deeper in the kidney where biopsy sampling is limited, or completed an extensive analysis of whole organ histology, blood biomarkers, and gene expression to evaluate the relative strengths and weaknesses of MRI for evaluating renal fibrosis. In this study, we performed DTI in the sodium oxalate mouse model of CKD. The DTI parameters fractional anisotropy, apparent diffusion coefficient, and axial diffusivity were compared between the control and oxalate groups with region-of-interest (ROI) analysis to determine changes in the cortex and medulla. Additionally, voxel-based analysis (VBA) was implemented to systematically identify local regions of injury over the whole kidney. DTI parameters were found to be significantly different in the medulla by both ROI analysis and VBA, which also spatially matched with collagen III IHC. The DTI parameters in this medullary region exhibited moderate to strong correlations with histology, blood biomarkers, hydroxyproline and gene expression. Our results thus highlight the sensitivity of DTI to the heterogeneity of renal fibrosis and importance of whole kidney non-invasive imaging.

STatistically Assigned Response Criteria in Solid Tumors (STARCIST).

BACKGROUND: Several reproducibility studies have established good test-retest reliability of FDG-PET in various oncology settings. However, these studies are based on relatively short inter-scan periods of 1-3 days while, in contrast, response assessments based on FDG-PET in early phase drug trials are typically made over an interval of 2-3 weeks during the first treatment cycle. With focus on longer, on-treatment scan intervals, we develop a data-driven approach to calculate baseline-specific cutoff values to determine patient-level changes in glucose uptake that are unlikely to be explained by random variability. Our method takes into account the statistical nature of natural fluctuations in SUV as well as potential bias effects. METHODS: To assess variability in SUV over clinically relevant scan intervals for clinical trials, we analyzed baseline and follow-up FDG-PET scans with a median scan interval of 21 days from 53 advanced stage cancer patients enrolled in a Phase 1 trial. The 53 patients received a sub-pharmacologic drug dose and the trial data is treated as a 'test-retest' data set. A simulation-based tool is presented which takes as input baseline lesion SUVmax values, the variance of spurious changes in SUVmax between scans, the desired Type I error rate, and outputs lesion and patient based cut-off values. Bias corrections are included to account for variations in tracer uptake time. RESULTS: In the training data, changes in SUVmax follow an approximately zero-mean Gaussian distribution with constant variance across levels of the baseline measurements. Because of constant variance, the coefficient of variation is a decreasing function of the magnitude of baseline SUVmax. This finding is consistent with published results, but our data shows greater variability. Application of our method to NSCLC patients treated with erlotinib produces results distinct from those based on the EORTC criteria. Based on data presented here as well as previous repeatability studies, the proposed method has greater statistical power to detect a significant %-decrease on SUVmax compared to published criteria relying on symmetric thresholds. CONCLUSIONS: Defining patient-specific, baseline dependent cut-off values based on the (null) distribution of naturally occurring fluctuations in glucose uptake enable identification of statistically significant changes in SUVmax. For lower baseline values, the produced cutoff values are notably asymmetric with relatively large changes (e.g. >50 %) required for statistical significance. For use with prospectively defined endpoints, the developed method enables the use of one-armed trials to detect pharmacodynamic drug effects based on FDG-PET. The clinical importance of changes in SUVmax is likely to remain dependent on both tumor biology and the type of treatment.