Brain structural and functional abnormalities associated with acute post-traumatic headache: iron deposition and functional connectivity.

BACKGROUND: The purpose of this study was to interrogate brain iron accumulation in participants with acute post-traumatic headache (PTH) due to mild traumatic brain injury (mTBI), and to determine if functional connectivity is affected in areas with iron accumulation. We aimed to examine the correlations between iron accumulation and headache frequency, post-concussion symptom severity, number of mTBIs, and time since most recent TBI. METHODS: Sixty participants with acute PTH and 60 age-matched healthy controls (HC) underwent 3T magnetic resonance imaging including quantitative T2* maps and resting-state functional connectivity imaging. Between group T2* differences were determined using T-tests (p < 0.005, cluster size threshold of 90 voxels). For regions with T2* differences, two analyses were conducted. First, the correlations with clinical variables including headache frequency, number of lifetime mTBIs, time since most recent mTBI, and Sport Concussion Assessment Tool (SCAT) symptom severity scale scores were investigated using linear regression. Second, the functional connectivity of these regions with the rest of the brain was examined (significance of p < 0.05 with family wise error correction for multiple comparisons). RESULTS: The acute PTH group consisted of 60 participants (22 male, 38 female) with average age of 42 ± 14 years. The HC group consisted of 60 age-matched controls (17 male, 43 female, average age of 42 ± 13). PTH participants had lower T2* values compared to HC in the left posterior cingulate and the bilateral cuneus. Stronger functional connectivity was observed between bilateral cuneus and right cerebellar areas in PTH compared to HC. Within the PTH group, linear regression showed negative associations of T2* in the left posterior cingulate with SCAT symptom severity score (p = 0.05) and T2* in the left cuneus with headache frequency (p = 0.04). CONCLUSIONS: Iron accumulation in posterior cingulate and cuneus was observed in those with acute PTH relative to HC; stronger functional connectivity was detected between the bilateral cuneus and the right cerebellum. The correlations of decreased T2* (suggesting higher iron content) with headache frequency and post mTBI symptom severity suggest that the iron accumulation that results from mTBI might reflect the severity of underlying mTBI pathophysiology and associate with post-mTBI symptom severity including PTH.

Brain Structural and Functional Abnormalities Associated with Acute Post-Traumatic Headache: Iron Deposition and Functional Connectivity.

Background: The purpose of this study was to interrogate brain iron accumulation in participants with acute post-traumatic headache (PTH) due to mild traumatic brain injury (mTBI), and to determine if functional connectivity is affected in areas with iron accumulation. We aimed to examine the correlations between iron accumulation and headache frequency, post-concussion symptom severity, number of mTBIs and time since most recent TBI. Methods: Sixty participants with acute PTH and 60 age-matched healthy controls (HC) underwent 3T magnetic resonance imaging including quantitative T2* maps and resting-state functional connectivity imaging. Between group T2* differences were determined using T-tests (p < 0.005, cluster size threshold of 10 voxels). For regions with T2* differences, two analyses were conducted. First, the correlations with clinical variables including headache frequency, number of lifetime mTBIs, time since most recent mTBI, and Sport Concussion Assessment Tool (SCAT) symptom severity scale scores were investigated using linear regression. Second, the functional connectivity of these regions with the rest of the brain was examined (significance of p < 0.05 with family wise error correction for multiple comparisons). Results: The acute PTH group consisted of 60 participants (22 male, 38 female) with average age of 42 ± 14 years. The HC group consisted of 60 age-matched controls (17 male, 43 female, average age of 42 ± 13). PTH participants had lower T2* values compared to HC in the left posterior cingulate and the bilateral cuneus. Stronger functional connectivity was observed between bilateral cuneus and right cerebellar areas in PTH compared to HC. Within the PTH group, linear regression showed negative associations of T2* and SCAT symptom severity score in the left posterior cingulate (p = 0.05) and with headache frequency in the left cuneus (p = 0.04). Conclusions: Iron accumulation in posterior cingulate and cuneus was observed in those with acute PTH relative to HC; stronger functional connectivity was detected between the bilateral cuneus and the right cerebellum. The correlations of decreased T2* (suggesting higher iron content) with headache frequency and post mTBI symptom severity suggest that the iron accumulation that results from mTBI might reflect the severity of underlying mTBI pathophysiology and associate with post-mTBI symptom severity including PTH.

Performing Automatic Identification and Staging of Urothelial Carcinoma in Bladder Cancer Patients Using a Hybrid Deep-Machine Learning Approach.

Accurate clinical staging of bladder cancer aids in optimizing the process of clinical decision-making, thereby tailoring the effective treatment and management of patients. While several radiomics approaches have been developed to facilitate the process of clinical diagnosis and staging of bladder cancer using grayscale computed tomography (CT) scans, the performances of these models have been low, with little validation and no clear consensus on specific imaging signatures. We propose a hybrid framework comprising pre-trained deep neural networks for feature extraction, in combination with statistical machine learning techniques for classification, which is capable of performing the following classification tasks: (1) bladder cancer tissue vs. normal tissue, (2) muscle-invasive bladder cancer (MIBC) vs. non-muscle-invasive bladder cancer (NMIBC), and (3) post-treatment changes (PTC) vs. MIBC.

Longitudinal differences in iron deposition in periaqueductal gray matter and anterior cingulate cortex are associated with response to erenumab in migraine.

OBJECTIVES: The objective of this longitudinal study was to determine whether brain iron accumulation, measured using magnetic resonance imaging magnetic transverse relaxation rates (T2*), is associated with response to erenumab for the treatment of migraine. METHODS: Participants (n = 28) with migraine, diagnosed using international classification of headache disorders 3rd edition criteria, were eligible if they had six to 25 migraine days during a four-week headache diary run-in phase. Participants received two treatments with 140 mg erenumab, one immediately following the pre-treatment run-in phase and a second treatment four weeks later. T2* data were collected immediately following the pre-treatment phase, and at two weeks and eight weeks following the first erenumab treatment. Patients were classified as erenumab responders if their migraine-day frequency at five-to-eight weeks post-initial treatment was reduced by at least 50% compared to the pre-treatment run-in phase. A longitudinal Sandwich estimator approach was used to compare longitudinal group differences (responders vs non-responders) in T2* values, associated with iron accumulation. Group visit effects were calculated with a significance threshold of p = 0.005 and cluster forming threshold of 250 voxels. T2* values of 19 healthy controls were used for a reference. The average of each significant region was compared between groups and visits with Bonferroni corrections for multiple comparisons with significance defined as p < 0.05. RESULTS: Pre- and post-treatment longitudinal imaging data were available from 28 participants with migraine for a total of 79 quantitative T2* images. Average subject age was 42 ± 13 years (25 female, three male). Of the 28 subjects studied, 53.6% were erenumab responders. Comparing longitudinal T2* between erenumab responders vs non-responders yielded two comparisons which survived the significance threshold of p < 0.05 after correction for multiple comparisons: the difference at eight weeks between the erenumab-responders and non-responders in the periaqueductal gray (mean ± standard error; responders 43 ± 1 ms vs non-responders 32.5 ± 1 ms, p = 0.002) and the anterior cingulate cortex (mean ± standard error; responders 50 ± 1 ms vs non-responders 40 ± 1 ms, p = 0.01). CONCLUSIONS: Erenumab response is associated with higher T2* in the periaqueductal gray and anterior cingulate cortex, regions that participate in pain processing and modulation. T2* differences between erenumab responders vs non-responders, a measure of brain iron accumulation, are seen at eight weeks post-treatment. Less iron accumulation in the periaqueductal gray and anterior cingulate cortex might play a role in the therapeutic mechanisms of migraine reduction associated with erenumab.

Thalamic subfield iron accumulation after acute mild traumatic brain injury as a marker of future post-traumatic headache intensity.

OBJECTIVE: To explore alterations in thalamic subfield volume and iron accumulation in individuals with post-traumatic headache (PTH) relative to healthy controls. BACKGROUND: The thalamus plays a pivotal role in the pathomechanism of pain and headache, yet the role of the thalamus in PTH attributed to mild traumatic brain injury (mTBI) remains unclear. METHODS: A total of 107 participants underwent multimodal T1-weighted and T2* brain magnetic resonance imaging. Using a clinic-based observational study, thalamic subfield volume and thalamic iron accumulation were explored in 52 individuals with acute PTH (mean age = 41.3; standard deviation [SD] = 13.5), imaged on average 24 days post mTBI, and compared to 55 healthy controls (mean age = 38.3; SD = 11.7) without history of mTBI or migraine. Symptoms of mTBI and headache characteristics were assessed at baseline (0-59 days post mTBI) (n = 52) and 3 months later (n = 46) using the Symptom Evaluation of the Sports Concussion Assessment Tool (SCAT-5) and a detailed headache history questionnaire. RESULTS: Relative to controls, individuals with acute PTH had significantly less volume in the lateral geniculate nucleus (LGN) (mean volume: PTH = 254.1, SD = 43.4 vs. controls = 278.2, SD = 39.8; p = 0.003) as well as more iron deposition in the left LGN (PTH: T2* signal = 38.6, SD = 6.5 vs. controls: T2* signal = 45.3, SD = 2.3; p = 0.048). Correlations in individuals with PTH revealed a positive relationship between left LGN T2* iron deposition and SCAT-5 symptom severity score at baseline (r = -0.29, p = 0.019) and maximum headache intensity at the 3-month follow-up (r = -0.47, p = 0.002). CONCLUSION: Relative to healthy controls, individuals with acute PTH had less volume and higher iron deposition in the left LGN. Higher iron deposition in the left LGN might reflect mTBI severity and poor headache recovery.

Image analysis techniques to map pyramids, pyramid structure, glomerular distribution, and pathology in the intact human kidney from 3-D MRI.

Kidney pathologies are often highly heterogeneous. To comprehensively understand kidney structure and pathology, it is critical to develop tools to map tissue microstructure in the context of the whole, intact organ. Magnetic resonance imaging (MRI) can provide a unique, three-dimensional view of the kidney and allows for measurements of multiple pathological features. Here, we developed a platform to systematically render and map gross and microstructural features of the human kidney based on three-dimensional MRI. These features include pyramid number and morphology as well as the associated medulla and cortex. In a subset of these kidneys, we also mapped individual glomeruli and glomerular volumes using cationic ferritin-enhanced MRI to report intrarenal heterogeneity in glomerular density and size. Finally, we rendered and measured regions of nephron loss due to pathology and individual glomerular volumes in each pyramidal unit. This work provides new tools to comprehensively evaluate the kidney across scales, with potential applications in anatomic and physiological research, transplant allograft evaluation, biomarker development, biopsy guidance, and therapeutic monitoring. These image rendering and analysis tools could eventually impact the field of transplantation medicine to improve longevity matching of donor allografts and recipients and reduce discard rates through the direct assessment of donor kidneys.NEW & NOTEWORTHY We report the application of cutting-edge image analysis approaches to characterize the pyramidal geometry, glomerular microstructure, and heterogeneity of the whole human kidney imaged using MRI. This work establishes a framework to improve the detection of microstructural pathology to potentially facilitate disease monitoring or transplant evaluation in the individual kidney.

Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma.

Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions. We developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each individual tumor-a key factor for future personalized therapeutic paradigms. The model used probability distributions for each sample prediction to quantify uncertainty, and used transductive learning to reduce the overall uncertainty. We compared predictive accuracy and uncertainty of the transductive learning GP model against a standard GP model using leave-one-patient-out cross validation. Additionally, we used a separate dataset containing 24 image-localized biopsies from 7 high-grade glioma patients to validate the model. Predictive uncertainty informed the likelihood of achieving an accurate sample prediction. When stratifying predictions based on uncertainty, we observed substantially higher performance in the group cohort (75% accuracy, n = 95) and amongst sample predictions with the lowest uncertainty (83% accuracy, n = 72) compared to predictions with higher uncertainty (48% accuracy, n = 23), due largely to data interpolation (rather than extrapolation). On the separate validation set, our model achieved 78% accuracy amongst the sample predictions with lowest uncertainty. We present a novel approach to quantify radiogenomics uncertainty to enhance model performance and clinical interpretability. This should help integrate more reliable radiogenomics models for improved medical decision-making.

Defining endogenous TACC3-chTOG-clathrin-GTSE1 interactions at the mitotic spindle using induced relocalization.

A multiprotein complex containing TACC3, clathrin and other proteins has been implicated in mitotic spindle stability. To disrupt this complex in an anti-cancer context, we need to understand its composition and how it interacts with microtubules. Induced relocalization of proteins in cells is a powerful way to analyze protein-protein interactions and, additionally, monitor where and when these interactions occur. We used CRISPR/Cas9 gene editing to add tandem FKBP-GFP tags to each complex member. The relocalization of endogenous tagged protein from the mitotic spindle to mitochondria and assessment of the effect on other proteins allowed us to establish that TACC3 and clathrin are core complex members and that chTOG (also known as CKAP5) and GTSE1 are ancillary to the complex, binding respectively to TACC3 and clathrin, but not each other. We also show that PIK3C2A, a clathrin-binding protein that was proposed to stabilize the TACC3-chTOG-clathrin-GTSE1 complex during mitosis, is not a member of the complex. This work establishes that targeting the TACC3-clathrin interface or their microtubule-binding sites are the two strategies most likely to disrupt spindle stability mediated by this multiprotein complex.

Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis.

Image synthesis is a novel solution in precision medicine for scenarios where important medical imaging is not otherwise available. The convolutional neural network (CNN) is an ideal model for this task because of its powerful learning capabilities through the large number of layers and trainable parameters. In this research, we propose a new architecture of residual inception encoder-decoder neural network (RIED-Net) to learn the nonlinear mapping between the input images and targeting output images. To evaluate the validity of the proposed approach, it is compared with two models from the literature: synthetic CT deep convolutional neural network (sCT-DCNN) and shallow CNN, using both an institutional mammogram dataset from Mayo Clinic Arizona and a public neuroimaging dataset from the Alzheimer's Disease Neuroimaging Initiative. Experimental results show that the proposed RIED-Net outperforms the two models on both datasets significantly in terms of structural similarity index, mean absolute percent error, and peak signal-to-noise ratio.

Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI.

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p < 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy.

Metabolism of Non-Enzymatically Derived Oxysterols: Clues from sterol metabolic disorders.

Cholestane-3ß,5α,6ß-triol (3ß,5α,6ß-triol) is formed from cholestan-5,6-epoxide (5,6-EC) in a reaction catalysed by cholesterol epoxide hydrolase, following formation of 5,6-EC through free radical oxidation of cholesterol. 7-Oxocholesterol (7-OC) and 7ß-hydroxycholesterol (7ß-HC) can also be formed by free radical oxidation of cholesterol. Here we investigate how 3ß,5α,6ß-triol, 7-OC and 7ß-HC are metabolised to bile acids. We show, by monitoring oxysterol metabolites in plasma samples rich in 3ß,5α,6ß-triol, 7-OC and 7ß-HC, that these three oxysterols fall into novel branches of the acidic pathway of bile acid biosynthesis becoming (25R)26-hydroxylated then carboxylated, 24-hydroxylated and side-chain shortened to give the final products 3ß,5α,6ß-trihydroxycholanoic, 3ß-hydroxy-7-oxochol-5-enoic and 3ß,7ß-dihydroxychol-5-enoic acids, respectively. The intermediates in these pathways may be causative of some phenotypical features of, and/or have diagnostic value for, the lysosomal storage diseases, Niemann Pick types C and B and lysosomal acid lipase deficiency. Free radical derived oxysterols are metabolised in human to unusual bile acids via novel branches of the acidic pathway, intermediates in these pathways are observed in plasma.

SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis.

Breast cancer is the second leading cause of cancer death among women worldwide. Nevertheless, it is also one of the most treatable malignances if detected early. Screening for breast cancer with full field digital mammography (FFDM) has been widely used. However, it demonstrates limited performance for women with dense breasts. An emerging technology in the field is contrast-enhanced digital mammography (CEDM), which includes a low energy (LE) image similar to FFDM, and a recombined image leveraging tumor neoangiogenesis similar to breast magnetic resonance imaging (MRI). CEDM has shown better diagnostic accuracy than FFDM. While promising, CEDM is not yet widely available across medical centers. In this research, we propose a Shallow-Deep Convolutional Neural Network (SD-CNN) where a shallow CNN is developed to derive "virtual" recombined images from LE images, and a deep CNN is employed to extract novel features from LE, recombined or "virtual" recombined images for ensemble models to classify the cases as benign vs. cancer. To evaluate the validity of our approach, we first develop a deep-CNN using 49 CEDM cases collected from Mayo Clinic to prove the contributions from recombined images for improved breast cancer diagnosis (0.85 in accuracy, 0.84 in AUC using LE imaging vs. 0.89 in accuracy, 0.91 in AUC using both LE and recombined imaging). We then develop a shallow-CNN using the same 49 CEDM cases to learn the nonlinear mapping from LE to recombined images. Next, we use 89 FFDM cases from INbreast, a public database to generate "virtual" recombined images. Using FFDM alone provides 0.84 in accuracy (AUC = 0.87), whereas SD-CNN improves the diagnostic accuracy to 0.90 (AUC = 0.92).

Sub-classifying patients with mild traumatic brain injury: A clustering approach based on baseline clinical characteristics and 90-day and 180-day outcomes.

BACKGROUND: The current classification of traumatic brain injury (TBI) into "mild", "moderate", or "severe" does not adequately account for the patient heterogeneity that still exists within each of these categories. The objective of this study was to identify "sub-groups" of mild TBI (mTBI) patients based on data available at the time of the initial post-TBI patient evaluation and to determine if the sub-grouping correlates with patient outcomes at 90 and 180 days post-TBI. METHODS: Data from patients in the TRACK-TBI Pilot dataset who had a Glasgow Coma Scale (GCS) score of 13 to 15 at arrival to the Emergency Department and a closed head injury were included. Considering 53 clinical variables that are typically available during the initial evaluation of the patient with mild TBI, sparse heirarchial clustering with cluster quality assessment was used to identify the optimal number of patient sub-groups. Patient sub-groups were then compared for ten outcomes measured at 90 or 180 days post-TBI. RESULTS: Amongst the 485 patients with mTBI, optimal clustering was based on the inclusion of 12 clinical variables that divided the patients into 5 mild TBI sub-groups. Clinical variables driving the sub-clustering included: gender, employment status, marital status, TBI due to falling, brain CT scan result, systolic blood pressure, diastolic blood pressure, administration of IV fluids in the Emergency Department, alcohol use, tobacco use, history of neurologic disease, and history of psychiatric disease. These 5 mild TBI sub-groups differed in their 90 day and 180 day outcomes within several domains including global outcomes, persistence of TBI-related symptoms, and neuropsychological impairment. CONCLUSIONS: Sub-groups of patients with mTBI can be identified according to clinical variables that are relatively easy to obtain at the time of initial patient evaluation. A patient's sub-group assignment is associated with multidimensional patient outcomes at 90 and 180 days. These findings support the notion that there are clinically meaningful subgroups of patients amongst those currently classified as having mTBI.

Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms.

Contrast-enhanced digital mammography (CEDM) is a promising imaging modality in breast cancer diagnosis. This study aims to investigate how to optimally develop a computer-aided diagnosis (CAD) scheme of CEDM images to classify breast masses. A CEDM dataset of 111 patients was assembled, which includes 33 benign and 78 malignant cases. Each CEDM includes two types of images namely, low energy (LE) and dual-energy subtracted (DES) images. A CAD scheme was applied to segment mass regions depicting on LE and DES images separately. Optimal segmentation results generated from DES images were also mapped to LE images or vice versa. After computing image features, multilayer perceptron based machine learning classifiers that integrate with a correlation-based feature subset evaluator and leave-one-case-out cross-validation method were built to classify mass regions. When applying CAD to DES and LE images with original segmentation, areas under ROC curves (AUC) were 0.759 ± 0.053 and 0.753 ± 0.047, respectively. After mapping the mass regions optimally segmented on DES images to LE images, AUC significantly increased to 0.848 ± 0.038 (p < 0.01). Study demonstrated that DES images eliminated overlapping effect of dense breast tissue, which helps improve mass segmentation accuracy. The study demonstrated that applying a novel approach to optimally map mass region segmented from DES images to LE images enabled CAD to yield significantly improved performance.

Identification of unusual oxysterols and bile acids with 7-oxo or 3ß,5α,6ß-trihydroxy functions in human plasma by charge-tagging mass spectrometry with multistage fragmentation.

7-Oxocholesterol (7-OC), 5,6-epoxycholesterol (5,6-EC), and its hydrolysis product cholestane-3ß,5α,6ß-triol (3ß,5α,6ß-triol) are normally minor oxysterols in human samples; however, in disease, their levels may be greatly elevated. This is the case in plasma from patients suffering from some lysosomal storage disorders, e.g., Niemann-Pick disease type C, or the inborn errors of sterol metabolism, e.g., Smith-Lemli-Opitz syndrome and cerebrotendinous xanthomatosis. A complication in the analysis of 7-OC and 5,6-EC is that they can also be formed ex vivo from cholesterol during sample handling in air, causing confusion with molecules formed in vivo. When formed endogenously, 7-OC, 5,6-EC, and 3ß,5α,6ß-triol can be converted to bile acids. Here, we describe methodology based on chemical derivatization and LC/MS with multistage fragmentation (MSn) to identify the necessary intermediates in the conversion of 7-OC to 3ß-hydroxy-7-oxochol-5-enoic acid and 5,6-EC and 3ß,5α,6ß-triol to 3ß,5α,6ß-trihydroxycholanoic acid. Identification of intermediate metabolites is facilitated by their unusual MSn fragmentation patterns. Semiquantitative measurements are possible, but absolute values await the synthesis of isotope-labeled standards.

Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study.

OBJECTIVE: To evaluate whether the use of a computer-aided diagnosis-contrast-enhanced spectral mammography (CAD-CESM) tool can further increase the diagnostic performance of CESM compared with that of experienced radiologists. MATERIALS AND METHODS: This IRB-approved retrospective study analyzed 50 lesions described on CESM from August 2014 to December 2015. Histopathologic analyses, used as the criterion standard, revealed 24 benign and 26 malignant lesions. An expert breast radiologist manually outlined lesion boundaries on the different views. A set of morphologic and textural features were then extracted from the low-energy and recombined images. Machine-learning algorithms with feature selection were used along with statistical analysis to reduce, select, and combine features. Selected features were then used to construct a predictive model using a support vector machine (SVM) classification method in a leave-one-out-cross-validation approach. The classification performance was compared against the diagnostic predictions of 2 breast radiologists with access to the same CESM cases. RESULTS: Based on the SVM classification, CAD-CESM correctly identified 45 of 50 lesions in the cohort, resulting in an overall accuracy of 90%. The detection rate for the malignant group was 88% (3 false-negative cases) and 92% for the benign group (2 false-positive cases). Compared with the model, radiologist 1 had an overall accuracy of 78% and a detection rate of 92% (2 false-negative cases) for the malignant group and 62% (10 false-positive cases) for the benign group. Radiologist 2 had an overall accuracy of 86% and a detection rate of 100% for the malignant group and 71% (8 false-positive cases) for the benign group. CONCLUSIONS: The results of our feasibility study suggest that a CAD-CESM tool can provide complementary information to radiologists, mainly by reducing the number of false-positive findings.

Computed Tomography-Based Texture Analysis to Determine Human Papillomavirus Status of Oropharyngeal Squamous Cell Carcinoma.

OBJECTIVE: To determine whether machine learning can accurately classify human papillomavirus (HPV) status of oropharyngeal squamous cell carcinoma (OPSCC) using computed tomography (CT)-based texture analysis. METHODS: Texture analyses were retrospectively applied to regions of interest from OPSCC primary tumors on contrast-enhanced neck CT, and machine learning was used to create a model that classified HPV status with the highest accuracy. Results were compared against the blinded review of 2 neuroradiologists. RESULTS: The HPV-positive (n = 92) and -negative (n = 15) cohorts were well matched clinically. Neuroradiologist classification accuracies for HPV status (44.9%, 55.1%) were not significantly different (P = 0.13), and there was a lack of agreement between the 2 neuroradiologists (κ = -0.145). The best machine learning model had an accuracy of 75.7%, which was greater than either neuroradiologist (P < 0.001, P = 0.002). CONCLUSIONS: Useful diagnostic information regarding HPV infection can be extracted from the CT appearance of OPSCC beyond what is apparent to the trained human eye.

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.

Content analysis of homeless smokers' perspectives on established and alternative smoking interventions.

BACKGROUND: Cigarette smoking is 5 times more prevalent among homeless individuals than in the general population, and homeless individuals are disproportionately affected by smoking-related morbidity and mortality. Homeless smokers report interest in changing their smoking behavior; however, established smoking cessation interventions are neither desirable to nor highly effective for most members of this population. The aim of this study was to document homeless smokers' perceptions of established smoking interventions as well as self-generated, alternative smoking interventions to elucidate points for intervention enhancement. METHODS: Participants (N=25) were homeless smokers who responded to semistructured interviews regarding smoking and nicotine use as well as experiences with established and alternative smoking interventions. Conventional content analysis was used to organize data and identify themes. RESULTS: Participants appreciated providers' initiation of conversations about smoking. They did not, however, feel simple advice to quit was a helpful approach. Instead, they suggested providers use a nonjudgmental, compassionate style, offer more support, and discuss a broader menu of options, including nonabstinence-based ways to reduce smoking-related harm and improve health-related quality of life. Most participants preferred engaging in their own self-defined, alternative smoking interventions, including obtaining nicotine more safely (e.g., vaping, using smokeless tobacco) and using behavioral (e.g., engaging in creative activities and hobbies) and cognitive strategies (e.g., reminding themselves about the positive aspects of not smoking and the negative consequences of smoking). Abrupt, unaided quit attempts were largely unsuccessful. CONCLUSIONS: The vast majority of participants with the lived experience of homelessness and smoking were uninterested in established smoking cessation approaches. They did, however, have creative ideas about alternative smoking interventions that providers may support to reduce smoking-related harm and enhance quality of life. These ideas included providing information about the relative risks of smoking and the relative benefits of alternative strategies to obtaining nicotine and avoiding smoking.

Extracellular Matrix Molecule-Based Capture of Mesenchymal Stromal Cells Under Flow.

We present a method to capture mesenchymal stromal cells (MSCs) by adhesion to extracellular matrix (ECM) molecules under flow conditions. The technique simulates a physiological system and exploits the natural biological interactions of cells, through integrin receptors, with their ECM. The system offers an insight into how MSCs could be targeted/localized to the site of interest (graft) following intravenous injection.