Rapid kV-switching single-source dual-energy CT ex vivo renal calculi characterization using a multiparametric approach: refining parameters on an expanded dataset.

PURPOSE: We aimed to determine the best algorithms for renal stone composition characterization using rapid kV-switching single-source dual-energy computed tomography (rsDECT) and a multiparametric approach after dataset expansion and refinement of variables. METHODS: rsDECT scans (80 and 140 kVp) were performed on 38 ex vivo 5- to 10-mm renal stones composed of uric acid (UA; n = 21), struvite (STR; n = 5), cystine (CYS; n = 5), and calcium oxalate monohydrate (COM; n = 7). Measurements were obtained for 17 variables: mean Hounsfield units (HU) at 11 monochromatic keV levels, effective Z, 2 iodine-water material basis pairs, and 3 mean monochromatic keV ratios (40/140, 70/120, 70/140). Analysis included using 5 multiparametric algorithms: Support Vector Machine, RandomTree, Artificial Neural Network, Naïve Bayes Tree, and Decision Tree (C4.5). RESULTS: Separating UA from non-UA stones was 100% accurate using multiple methods. For non-UA stones, using a 70-keV mean cutoff value of 694 HU had 100% accuracy for distinguishing COM from non-COM (CYS, STR) stones. The best result for distinguishing all 3 non-UA subtypes was obtained using RandomTree (15/17, 88%). CONCLUSIONS: For stones 5 mm or larger, multiple methods can distinguish UA from non-UA and COM from non-COM stones with 100% accuracy. Thus, the choice for analysis is per the user's preference. The best model for separating all three non-UA subtypes was 88% accurate, although with considerable individual overlap between CYS and STR stones. Larger, more diverse datasets, including in vivo data and technical improvements in material separation, may offer more guidance in distinguishing non-UA stone subtypes in the clinical setting.

Migraine classification using magnetic resonance imaging resting-state functional connectivity data.

Background This study used machine-learning techniques to develop discriminative brain-connectivity biomarkers from resting-state functional magnetic resonance neuroimaging ( rs-fMRI) data that distinguish between individual migraine patients and healthy controls. Methods This study included 58 migraine patients (mean age = 36.3 years; SD = 11.5) and 50 healthy controls (mean age = 35.9 years; SD = 11.0). The functional connections of 33 seeded pain-related regions were used as input for a brain classification algorithm that tested the accuracy of determining whether an individual brain MRI belongs to someone with migraine or to a healthy control. Results The best classification accuracy using a 10-fold cross-validation method was 86.1%. Resting functional connectivity of the right middle temporal, posterior insula, middle cingulate, left ventromedial prefrontal and bilateral amygdala regions best discriminated the migraine brain from that of a healthy control. Migraineurs with longer disease durations were classified more accurately (>14 years; 96.7% accuracy) compared to migraineurs with shorter disease durations (≤14 years; 82.1% accuracy). Conclusions Classification of migraine using rs-fMRI provides insights into pain circuits that are altered in migraine and could potentially contribute to the development of a new, noninvasive migraine biomarker. Migraineurs with longer disease burden were classified more accurately than migraineurs with shorter disease burden, potentially indicating that disease duration leads to reorganization of brain circuitry.

Ex Vivo Renal Stone Characterization with Single-Source Dual-Energy Computed Tomography: A Multiparametric Approach.

RATIONALE AND OBJECTIVES: We aimed to investigate a multiparametric approach using single-source dual-energy computed tomography (ssDECT) for the characterization of renal stones. MATERIALS AND METHODS: ssDECT scans were performed at 80 and 140 kVp on 32 ex vivo kidney stones of 3-10 mm in a phantom. True composition was determined by infrared spectroscopy to be uric acid (UA; n = 14), struvite (n = 7), cystine (n = 7), or calcium oxalate monohydrate (n = 4). Measurements were obtained for up to 52 variables, including mean density at 11 monochromatic keV levels, effective Z, and multiple material basis pairs. The data were analyzed with five multiparametric algorithms. After omitting 8 stones smaller than 5 mm, the remaining 24-stone dataset was similarly analyzed. Both stone datasets were also analyzed with a subset of 14 commonly used variables in the same fashion. RESULTS: For the 32-stone dataset, the best method for distinguishing UA from non-UA stones was 97% accurate, and for distinguishing the non-UA subtypes was 72% accurate. For the 24-stone dataset, the best method for distinguishing UA from non-UA stones was 100% accurate, and for distinguishing the non-UA subtypes was 75% accurate. CONCLUSION: Multiparametric ssDECT methods can distinguish UA from non-UA stones of 5 mm or larger with 100% accuracy. The best model to distinguish the non-UA renal stone subtypes was 75% accurate. Further refinement of this multiparametric approach may increase the diagnostic accuracy of separating non-UA subtypes and assist in the development of a clinical paradigm for in vivo use.

Accurate Classification of Chronic Migraine via Brain Magnetic Resonance Imaging.

BACKGROUND: The International Classification of Headache Disorders provides criteria for the diagnosis and subclassification of migraine. Since there is no objective gold standard by which to test these diagnostic criteria, the criteria are based on the consensus opinion of content experts. Accurate migraine classifiers consisting of brain structural measures could serve as an objective gold standard by which to test and revise diagnostic criteria. The objectives of this study were to utilize magnetic resonance imaging measures of brain structure for constructing classifiers: (1) that accurately identify individuals as having chronic vs episodic migraine vs being a healthy control; and (2) that test the currently used threshold of 15 headache days/month for differentiating chronic migraine from episodic migraine. METHODS: Study participants underwent magnetic resonance imaging for determination of regional cortical thickness, cortical surface area, and volume. Principal components analysis combined structural measurements into principal components accounting for 85% of variability in brain structure. Models consisting of these principal components were developed to achieve the classification objectives. Tenfold cross validation assessed classification accuracy within each of the 10 runs, with data from 90% of participants randomly selected for classifier development and data from the remaining 10% of participants used to test classification performance. Headache frequency thresholds ranging from 5-15 headache days/month were evaluated to determine the threshold allowing for the most accurate subclassification of individuals into lower and higher frequency subgroups. RESULTS: Participants were 66 migraineurs and 54 healthy controls, 75.8% female, with an average age of 36 +/- 11 years. Average classifier accuracies were: (1) 68% for migraine (episodic + chronic) vs. healthy controls; (2) 67.2% for episodic migraine vs healthy controls; (3) 86.3% for chronic migraine vs. healthy controls; and (4) 84.2% for chronic migraine vs episodic migraine. The classifiers contained principal components consisting of several structural measures, commonly including the temporal pole, anterior cingulate cortex, superior temporal lobe, entorhinal cortex, medial orbital frontal gyrus, and pars triangularis. A threshold of 15 headache days/month allowed for the most accurate subclassification of migraineurs into lower frequency and higher frequency subgroups. CONCLUSIONS: Classifiers consisting of cortical surface area, cortical thickness, and regional volumes were highly accurate for determining if individuals have chronic migraine. Furthermore, results provide objective support for the current use of 15 headache days/month as a threshold for dividing migraineurs into lower frequency (i.e., episodic migraine) and higher frequency (i.e., chronic migraine) subgroups.

Magnetic resonance elastography: evaluation of new inversion algorithm and quantitative analysis method.

PURPOSE: To assess mean shear hepatic stiffness calculations using various region of interest (ROI) techniques, a new inversion algorithm, and a confidence threshold mask. METHODS: Seventy-three patients (49 with abnormal liver function tests/known chronic liver disease and 24 healthy liver transplant donors) underwent liver biopsy and magnetic resonance elastography (MRE). MRE data processed with the current inversion algorithm [multiscale direct inversion (MSDI)] was assessed using 2 ROI methods (single vs. triple). The data were then reprocessed using the new inversion algorithm (multimodel direct inversion [MMDI]) Hepatic stiffness calculations were performed using a single (70%) ROI method, with/without a 95% confidence threshold mask, and compared with MSDI. RESULTS: For MSDI, average stiffness difference between single and triple ROI methods was not statistically significant by the 2-sample t test [0.15 kilopascals (kPa); P = .77]. For the 2 algorithms, there was little difference in average stiffness measurements of MSDI and MMDI (mean, 0.32 kPa; 9%) using a confidence mask with good agreement [intraclass correlation coefficient (ICC), 0.986 (95% CI 0.975-0.994)]. Use of the confidence mask showed excellent consistency and less variance [ICC, 0.995 (95% CI 0.993-0.998)] compared to either the inter-observer or intra-observer freehand technique. CONCLUSION: MRE analysis showed no significant difference between the 2 freehand ROI techniques. With a 9% average kPa variance, stiffness measurements for MSDI and MMDI were also not significantly different. The use of the confidence mask reduces calculated stiffness variability, which impacts the use of MRE for assessing therapy response and initial/longitudinal assessment of chronic liver disease.