Pharmacogenetics to prevent hypersensitivity reactions to antiepileptic drugs: is testing performed when indicated?

Extensive scientific evidence consistently demonstrates the clinical validity and utility of HLA-B*15:02 pre-screening in averting severe cutaneous adverse reactions (SCARs), namely Stevens-Johnson syndrome and toxic epidermal necrolysis, associated with carbamazepine or oxcarbazepine usage. Current practice guidelines and drug labeling actively advocate for pharmacogenetic pre-screening before initiating these antiepileptic drugs (AED), with particular emphasis on patients of Asian descent. However, there is a potential need to strengthen compliance with these recommendations. This retrospective study aimed to describe the pharmacogenetic pre-screening, documentation, and SCARs incidence for patients of Asian ancestry initiated on carbamazepine or oxcarbazepine at a large Northeastern USA healthcare system. Between 1 July 2016 and August 1, 2021, 27 patients with documented Asian heritage in the electronic health record (EHR) were included. The overall rate of HLA-B*15:02 pre-screening before carbamazepine or oxcarbazepine initiation was 4%. None who underwent pharmacogenetic pre-screening carried the associated HLA-B risk allele, and no SCARs were reported. Notably, pharmacogenetic results were not discretely entered into the EHR, and the results were only found as attached documents in the miscellaneous section of the EHR. There remains a significant opportunity for improving HLA-B*15:02 pre-screening for patients starting carbamazepine and oxcarbazepine to prevent SCARs in the USA.

Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.

We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

Automated Pulmonary Tuberculosis Severity Assessment on Chest X-rays.

According to the 2022 World Health Organization's Global Tuberculosis (TB) report, an estimated 10.6 million people fell ill with TB, and 1.6 million died from the disease in 2021. In addition, 2021 saw a reversal of a decades-long trend of declining TB infections and deaths, with an estimated increase of 4.5% in the number of people who fell ill with TB compared to 2020, and an estimated yearly increase of 450,000 cases of drug resistant TB. Estimating the severity of pulmonary TB using frontal chest X-rays (CXR) can enable better resource allocation in resource constrained settings and monitoring of treatment response, enabling prompt treatment modifications if disease severity does not decrease over time. The Timika score is a clinically used TB severity score based on a CXR reading. This work proposes and evaluates three deep learning-based approaches for predicting the Timika score with varying levels of explainability. The first approach uses two deep learning-based models, one to explicitly detect lesion regions using YOLOV5n and another to predict the presence of cavitation using DenseNet121, which are then utilized in score calculation. The second approach uses a DenseNet121-based regression model to directly predict the affected lung percentage and another to predict cavitation presence using a DenseNet121-based classification model. Finally, the third approach directly predicts the Timika score using a DenseNet121-based regression model. The best performance is achieved by the second approach with a mean absolute error of 13-14% and a Pearson correlation of 0.7-0.84 using three held-out datasets for evaluating generalization.

Training pharmacy students to deliver bad news using the SPIKES model.

BACKGROUND AND PURPOSE: Delivery of bad news or negative health information is a complex skill critical to the provision of patient care. While counseling models with this focus exist within other health care professions, their use in pharmacy education is lacking. The purpose of this study is to assess pharmacy students' ability to deliver bad news with the implementation of a counseling model titled SPIKES (Setting, Perception, Invitation, Knowledge, Emotions with Empathy, and Strategy/Summary). EDUCATIONAL ACTIVITY AND SETTING: First-year pharmacy students attended a one-hour training on the SPIKES model and completed three simulations with its application. Pre- and post-training surveys were administered to assess confidence, attitudes, and perceptions. Student performance during the simulations was evaluated by teaching assistants (TAs) as well as a self-assessment using the same grading rubric. A paired t-test was used to test for significant mean improvement in competency scores, confidence, attitudes, and perceptions from Week 1 to Week 3. FINDINGS: One hundred and sixty-seven students were included in the analysis. There was a significant improvement in the student's self-assessment of their performance for each of the SPIKES components and summative scores. For the TA assessment, there was a significant mean improvement in the summative SPIKES score; however, within each component of SPIKES, only the knowledge component showed significant mean improvement. There was also a significant improvement in student confidence in the post-training surveys. SUMMARY: Implementation of the SPIKES protocol in the pharmacy curriculum showed an overall improvement in students' self-assessed performance in delivering bad news.

Combining Radiological and Genomic TB Portals Data for Drug Resistance Analysis.

Tuberculosis (TB) drug resistance is a worldwide public health problem. It decreases the likelihood of a positive outcome for the individual patient and increases the likelihood of disease spread. Therefore, early detection of TB drug resistance is crucial for improving outcomes and controlling disease transmission. While drug-sensitive tuberculosis cases are declining worldwide because of effective treatment, the threat of drug-resistant tuberculosis is growing, and the success rate of drug-resistant tuberculosis treatment is only around 60%. The TB Portals program provides a publicly accessible repository of TB case data with an emphasis on collecting drug-resistant cases. The dataset includes multi-modal information such as socioeconomic/geographic data, clinical characteristics, pathogen genomics, and radiological features. The program is an international collaboration whose participants are typically under a substantial burden of drug-resistant tuberculosis, with data collected from standard clinical care provided to the patients. Consequentially, the TB Portals dataset is heterogenous in nature, with data representing multiple treatment centers in different countries and containing cross-domain information. This study presents the challenges and methods used to address them when working with this real-world dataset. Our goal was to evaluate whether combining radiological features derived from a chest X-ray of the host and genomic features from the pathogen can potentially improve the identification of the drug susceptibility type, drug-sensitive (DS-TB) or drug-resistant (DR-TB), and the length of the first successful drug regimen. To perform these studies, significantly imbalanced data needed to be processed, which included a much larger number of DR-TB cases than DS-TB, many more cases with radiological findings than genomic ones, and the sparse high dimensional nature of the genomic information. Three evaluation studies were carried out. First, the DR-TB/DS-TB classification model achieved an average accuracy of 92.4% when using genomic features alone or when combining radiological and genomic features. Second, the regression model for the length of the first successful treatment had a relative error of 53.5% using radiological features, 25.6% using genomic features, and 22.0% using both radiological and genomic features. Finally, the relative error of the third regression model predicting the length of the first treatment using the most common drug combination varied depending on the feature type used. When using radiological features alone, the relative error was 17.8%. For genomic features alone, the relative error increased to 19.9%. The model had a relative error of 19.0% when both radiological and genomic features were combined. Although combining radiological and genomic features did not improve upon the use of genomic features when classifying DR-TB/DS-TB, the combination of the two feature types improved the relative error of the predictive model for the length of the first successful treatment. Furthermore, the regression model trained on radiological features achieved the best performance when predicting the treatment length of the most common drug combination.

Longitudinal association of epicardial and thoracic adipose tissues with coronary and cardiac characteristics in psoriasis.

Background: s: Psoriasis is a disease of systemic inflammation associated with increased cardiometabolic risk. Epicardial adipose tissue (EAT) and thoracic adipose tissue (TAT) are contributing factors for atherosclerosis and cardiac dysfunction. We strove to assess the longitudinal impact of the EAT and TAT on coronary and cardiac characteristics in psoriasis. Methods: The study consisted of 301 patients with baseline coronary computed tomography angiography (CTA), of which 139 had four-year follow up scans. EAT and TAT volumes from non-contrast computed tomography scans were quantified by an automated segmentation framework. Coronary plaque characteristics and left ventricular (LV) mass were quantified by CTA. Results: When stratified by baseline EAT and TAT volume quartiles, a stepwise significant increase in cardiometabolic parameters was observed. EAT and TAT volumes associated with fibro-fatty burden (FFB) (TAT: ρ = 0.394, P < 0.001; EAT: ρ = 0.459, P < 0.001) in adjusted models. Only EAT had a significant four-year time-dependent association with FFB in fully adjusted models (ß = 0.307 P = 0.003), whereas only TAT volume associated with myocardial injury in fully adjusted models (TAT: OR = 1.57 95 % CI = (1.00-2.60); EAT: OR = 1.46 95 % CI = (0.91-2.45). Higher quartiles of EAT and TAT had increased LV mass and developed strong correlation (TAT: ρ = 0.370, P < 0.001; EAT: ρ = 0.512, P < 0.001). Conclusions: Our study is the first to explore how both EAT and TAT volumes associate with increased cardiometabolic risk profile in an inflamed psoriasis cohorts and highlight the need for further studies on its use as a potential prognostic tool for high-risk coronary plaques and cardiac dysfunction.

Promoting Physical Activity in a Spanish-Speaking Latina Population of Low Socioeconomic Status With Chronic Neurological Disorders: Proof-of-Concept Study.

BACKGROUND: Physical activity (PA) is known to improve quality of life (QoL) as well as reduce mortality and disease progression in individuals with chronic neurological disorders. However, Latina women are less likely to participate in recommended levels of PA due to common socioeconomic barriers, including limited resources and access to exercise programs. Therefore, we developed a community-based intervention with activity monitoring and behavioral coaching to target these barriers and facilitate sustained participation in an exercise program promoting PA. OBJECTIVE: The aim of this study was to determine the feasibility and efficacy of a community-based intervention to promote PA through self-monitoring via a Fitbit and behavioral coaching among Latina participants with chronic neurological disorders. METHODS: We conducted a proof-of-concept study among 21 Spanish-speaking Latina participants recruited from the Los Angeles County and University of Southern California (LAC+USC) neurology clinic; participants enrolled in the 16-week intervention at The Wellness Center at The Historic General Hospital in Los Angeles. Demographic data were assessed at baseline. Feasibility was defined by participant attrition and Fitbit adherence. PA promotion was determined by examining change in time spent performing moderate-to-vigorous PA (MVPA) over the 16-week period. The effect of behavioral coaching was assessed by quantifying the difference in MVPA on days when coaching occurred versus on days without coaching. Change in psychometric measures (baseline vs postintervention) and medical center visits (16 weeks preintervention vs during the intervention) were also examined. RESULTS: Participants were of low socioeconomic status and acculturation. A total of 19 out of 21 (90%) participants completed the study (attrition 10%), with high Fitbit wear adherence (mean 90.31%, SD 10.12%). Time performing MVPA gradually increased by a mean of 0.16 (SD 0.23) minutes per day (P<.001), which was equivalent to an increase of approximately 18 minutes in MVPA over the course of the 16-week study period. Behavioral coaching enhanced intervention effectiveness as evidenced by a higher time spent on MVPA on days when coaching occurred via phone (37 min/day, P=.02) and in person (45.5 min/day, P=.01) relative to days without coaching (24 min/day). Participants improved their illness perception (effect size g=0.30) and self-rated QoL (effect size g=0.32). Additionally, a reduction in the number of medical center visits was observed (effect size r=0.44), and this reduction was associated with a positive change in step count during the study period (P.=04). CONCLUSIONS: Self-monitoring with behavioral coaching is a feasible community-based intervention for PA promotion among Latina women of low socioeconomic status with chronic neurological conditions. PA is known to be important for brain health in neurological conditions but remains relatively unexplored in minority populations. TRIAL REGISTRATION: ClinicalTrials.gov NCT04820153; https://clinicaltrials.gov/ct2/show/NCT04820153.

DeepHeartCT: A fully automatic artificial intelligence hybrid framework based on convolutional neural network and multi-atlas segmentation for multi-structure cardiac computed tomography angiography image segmentation.

Cardiac computed tomography angiography (CTA) is an emerging imaging modality for assessing coronary artery as well as various cardiovascular structures. Recently, deep learning (DL) methods have been successfully applied to many applications of medical image analysis including cardiac CTA structure segmentation. However, DL requires a large amounts of data and high-quality labels for training which can be burdensome to obtain due to its labor-intensive nature. In this study, we aim to develop a fully automatic artificial intelligence (AI) system, named DeepHeartCT, for accurate and rapid cardiac CTA segmentation based on DL. The proposed system was trained using a large clinical dataset with computer-generated labels to segment various cardiovascular structures including left and right ventricles (LV, RV), left and right atria (LA, RA), and LV myocardium (LVM). This new system was trained directly using high-quality computer labels generated from our previously developed multi-atlas based AI system. In addition, a reverse ranking strategy was proposed to assess the segmentation quality in the absence of manual reference labels. This strategy allowed the new framework to assemble optimal computer-generated labels from a large dataset for effective training of a deep convolutional neural network (CNN). A large clinical cardiac CTA studies (n = 1,064) were used to train and validate our framework. The trained model was then tested on another independent dataset with manual labels (n = 60). The Dice score, Hausdorff distance and mean surface distance were used to quantify the segmentation accuracy. The proposed DeepHeartCT framework yields a high median Dice score of 0.90 [interquartile range (IQR), 0.90-0.91], a low median Hausdorff distance of 7 mm (IQR, 4-15 mm) and a low mean surface distance of 0.80 mm (IQR, 0.57-1.29 mm) across all segmented structures. An additional experiment was conducted to evaluate the proposed DL-based AI framework trained with a small vs. large dataset. The results show our framework also performed well when trained on a small optimal training dataset (n = 110) with a significantly reduced training time. These results demonstrated that the proposed DeepHeartCT framework provides accurate and rapid cardiac CTA segmentation that can be readily generalized for handling large-scale medical imaging applications.

Magnetic resonance spectroscopy shows associations between neurometabolite levels and perivascular space volume in Parkinson's disease: a pilot and feasibility study.

OBJECTIVE: Higher volume fraction of perivascular space (PVS) has recently been reported in Parkinson's disease (PD) and related disorders. Both elevated PVS and altered levels of neurometabolites, assayed by proton magnetic resonance spectroscopy (MRS), are suspected indicators of neuroinflammation, but no published reports have concurrently examined PVS and MRS neurometabolites. METHODS: In an exploratory pilot study, we acquired multivoxel 3-T MRS using a semi-Localization by Adiabatic SElective Refocusing (sLASER) pulse-sequence (repetition time/echo time = 2810/60 ms, voxels 10 × 10 × 10 mm3) from a 2D slab sampling bilateral frontal white matter (FWM) and anterior middle cingulate cortex (aMCC). PVS maps obtained from high-resolution (0.8 × 0.8 × 0.8 mm3) T1-weighted MRI were co-registered with MRS. In each MRS voxel, PVS volume and neurometabolite levels were measured. RESULTS: Linear regression accounting for age, sex, and BMI found greater PVS volume for higher levels of choline-containing compounds (Cho; P = 0.047) in FWM and lower PVS volume for higher levels of N-acetyl compounds (NAA; P = 0.012) in aMCC. Since (putatively) higher Cho is associated with inflammation while NAA has anti-inflammatory properties, these observations add to evidence that higher PVS load is a sign of inflammation. Additionally, lower Montreal Cognitive Assessment scores were associated with lower NAA in aMCC (P = 0.002), suggesting that local neuronal dysfunction and inflammation contribute to cognitive impairment in PD. CONCLUSION: These exploratory findings indicate that co-analysis of PVS and MRS is feasible and may help elucidate the cellular and metabolic substrates of glymphatic and inflammatory processes in PD.

Physical activity intensity is associated with cognition and functional connectivity in Parkinson's disease.

BACKGROUND: Cognitive impairment is common in Parkinson's disease (PD) and often leads to dementia, with no effective treatment. Aging studies suggest that physical activity (PA) intensity has a positive impact on cognition and enhanced functional connectivity may underlie these benefits. However, less is known in PD. This cross-sectional study examined the relationship between PA intensity, cognitive performance, and resting state functional connectivity in PD and whether PA intensity influences the relationship between functional connectivity and cognitive performance. METHODS: 96 individuals with mild-moderate PD completed a comprehensive neuropsychological battery. Intensity of PA was objectively captured over a seven-day period using a wearable device (ActiGraph). Time spent in light and moderate intensity PA was determined based on standardized actigraphy cut points. Resting-state fMRI was assessed in a subset of 50 individuals to examine brain-wide functional connectivity. RESULTS: Moderate intensity PA (MIPA), but not light PA, was associated with better global cognition, visuospatial function, memory, and executive function. Individuals who met the WHO recommendation of ≥150 min/week of MIPA demonstrated better global cognition, executive function, and visuospatial function. Resting-state functional connectivity associated with MIPA included a combination of brainstem, hippocampus, and regions in the frontal, cingulate, and parietal cortices, which showed higher connectivity across the brain in those achieving the WHO MIPA recommendation. Meeting this recommendation positively moderated the associations between identified functional connectivity and global cognition, visuospatial function, and language. CONCLUSION: Encouraging MIPA, particularly the WHO recommendation of ≥150 min of MIPA/week, may represent an important prescription for PD cognition.

Assessing the Impact of Substandard and Falsified Antimalarials in Benin.

Substandard and falsified antimalarials contribute to the global malaria burden by increasing the risk of treatment failures, adverse events, unnecessary health expenditures, and avertable deaths, yet no study has examined this impact in western francophone Africa to date. In Benin, where malaria remains endemic and is the leading cause of mortality among children younger than 5 years, there is a lack of robust data to combat the issue effectively and inform policy decisions. We adapted the Substandard and Falsified Antimalarial Research Impact model to assess the health and economic impact of poor-quality antimalarials in this population. The model simulates population characteristics, malaria infection, care-seeking behavior, disease progression, treatment outcomes, and associated costs of malaria. We estimated approximately 1.8 million cases of malaria in Benin among children younger than 5 years, which cost $193 million (95% CI, $192-$193 million) in treatment costs and productivity losses annually. Substandard and falsified antimalarials were responsible for 11% (n = 693) of deaths and nearly $20.8 million in annual costs. Moreover, we found that replacing all antimalarials with quality-ensured artemisinin combination therapies (ACTs) could result in $29.6 million in cost savings and prevent 1,038 deaths per year. These results highlight the value of improving access to quality-ensured artemisinin combination therapies for malaria treatment and increasing care-seeking in Benin. Policymakers and key stakeholders should use these findings to advocate for increased access to quality-ensured antimalarials, inform policies and interventions to improve health-care access and quality, and reduce the burden of malaria.

The relationship between systemic inflammation and increased left ventricular mass is partly mediated by noncalcified coronary artery disease burden in psoriasis.

OBJECTIVE: Increased left ventricular (LV) mass is an important precursor to heart failure. Inflammation plays an important role in increasing LV mass. However, the contribution of subclinical coronary artery disease (CAD) to the inflammation-LV mass relationship is unknown. In subjects with psoriasis, a chronic inflammatory skin disease, we evaluated if systemic inflammation assessed by plasma glycoprotein A (GlycA) associated with LV mass measured on coronary CT angiography (CCTA). Additionally, we analyzed whether this relationship was mediated by early CAD assessed as noncalcified coronary burden (NCB). METHODS: We performed an observational longitudinal study of 213 subjects with psoriasis free of known cardiovascular disease, 189 of whom were followed over one year. All participants had GlycA measurements by nuclear magnetic resonance spectroscopy and LV mass and NCB quantified by CCTA. RESULTS: The cohort had a mean age of 50.3 (±12.9) years and 59% were male. There was moderate psoriasis severity and low cardiovascular risk. LV mass increased by GlycA tertiles [1st tertile:24.6 g/m2.7(3.8), 2nd tertile:25.5 g/m2.7(3.8), 3rd tertile:27.7 g/m2.7(5.5), p<0.001]. Both GlycA (ß=0.24, p = 0.001) and NCB (ß=0.50, p<0.001) associated with LV mass in models adjusted for age, sex, hypertension, hypertension therapy, lipid therapy, biologic therapy for psoriasis, waist:hip ratio, psoriasis disease duration and severity. In multivariable-adjusted mediation analyses, NCB accounted for 32% of the GlycA-LV mass relationship. Finally, over one year, change in NCB independently associated with change in LV mass (ß=0.25, p = 0.002). CONCLUSIONS: Both systemic inflammation and coronary artery NCB were associated with LV mass beyond cardiovascular risk factors in psoriasis. Furthermore, a substantial proportion of the inflammatory-LV mass relationship was mediated by NCB. These findings underscore the possible contribution of early coronary artery disease to the relationship between systemic inflammation and LV mass.

Simultaneous Multi-Structure Segmentation of the Heart and Peripheral Tissues in Contrast Enhanced Cardiac Computed Tomography Angiography.

Contrast enhanced cardiac computed tomography angiography (CTA) is a prominent imaging modality for diagnosing cardiovascular diseases non-invasively. It assists the evaluation of the coronary artery patency and provides a comprehensive assessment of structural features of the heart and great vessels. However, physicians are often required to evaluate different cardiac structures and measure their size manually. Such task is very time-consuming and tedious due to the large number of image slices in 3D data. We present a fully automatic method based on a combined multi-atlas and corrective segmentation approach to label the heart and its associated cardiovascular structures. This method also automatically separates other surrounding intrathoracic structures from CTA images. Quantitative assessment of the proposed method is performed on 36 studies with a reference standard obtained from expert manual segmentation of various cardiac structures. Qualitative evaluation is also performed by expert readers to score 120 studies of the automatic segmentation. The quantitative results showed an overall Dice of 0.93, Hausdorff distance of 7.94 mm, and mean surface distance of 1.03 mm between automatically and manually segmented cardiac structures. The visual assessment also attained an excellent score for the automatic segmentation. The average processing time was 2.79 minutes. Our results indicate the proposed automatic framework significantly improves accuracy and computational speed in conventional multi-atlas based approach, and it provides comprehensive and reliable multi-structural segmentation of CTA images that is valuable for clinical application.

Virtual organelle self-coding for fluorescence imaging via adversarial learning.

SIGNIFICANCE: Our study introduces an application of deep learning to virtually generate fluorescence images to reduce the burdens of cost and time from considerable effort in sample preparation related to chemical fixation and staining. AIM: The objective of our work was to determine how successfully deep learning methods perform on fluorescence prediction that depends on structural and/or a functional relationship between input labels and output labels. APPROACH: We present a virtual-fluorescence-staining method based on deep neural networks (VirFluoNet) to transform co-registered images of cells into subcellular compartment-specific molecular fluorescence labels in the same field-of-view. An algorithm based on conditional generative adversarial networks was developed and trained on microscopy datasets from breast-cancer and bone-osteosarcoma cell lines: MDA-MB-231 and U2OS, respectively. Several established performance metrics-the mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and structural-similarity-index (SSIM)-as well as a novel performance metric, the tolerance level, were measured and compared for the same algorithm and input data. RESULTS: For the MDA-MB-231 cells, F-actin signal performed the fluorescent antibody staining of vinculin prediction better than phase-contrast as an input. For the U2OS cells, satisfactory metrics of performance were archieved in comparison with ground truth. MAE is <0.005, 0.017, 0.012; PSNR is >40 / 34 / 33 dB; and SSIM is >0.925 / 0.926 / 0.925 for 4',6-diamidino-2-phenylindole/hoechst, endoplasmic reticulum, and mitochondria prediction, respectively, from channels of nucleoli and cytoplasmic RNA, Golgi plasma membrane, and F-actin. CONCLUSIONS: These findings contribute to the understanding of the utility and limitations of deep learning image-regression to predict fluorescence microscopy datasets of biological cells. We infer that predicted image labels must have either a structural and/or a functional relationship to input labels. Furthermore, the approach introduced here holds promise for modeling the internal spatial relationships between organelles and biomolecules within living cells, leading to detection and quantification of alterations from a standard training dataset.

Improving multi-atlas cardiac structure segmentation of computed tomography angiography: A performance evaluation based on a heterogeneous dataset.

Multi-atlas based segmentation is an effective technique that transforms a representative set of atlas images and labels into a target image for structural segmentation. However, a significant limitation of this approach relates to the fact that the atlas and the target images need to be similar in volume orientation, coverage, or acquisition protocols in order to prevent image misregistration and avoid segmentation fault. In this study, we aim to evaluate the impact of using a heterogeneous Computed Tomography Angiography (CTA) dataset on the performance of a multi-atlas cardiac structure segmentation framework. We propose a generalized technique based upon using the Simple Linear Iterative Clustering (SLIC) supervoxel method to detect a bounding box region enclosing the heart before subsequent cardiac structure segmentation. This technique facilitates our framework to process CTA datasets acquired from distinct imaging protocols and to improve its segmentation accuracy and speed. In a four-way cross comparison based on 60 CTA studies from our institution and 60 CTA datasets from the Multi-Modality Whole Heart Segmentation MICCAI challenge, we show that the proposed framework performs well in segmenting seven different cardiac structures based upon interchangeable atlas and target datasets acquired from different imaging settings. For the overall results, our automated segmentation framework attains a median Dice, mean distance, and Hausdorff distance of 0.88, 1.5 mm, and 9.69 mm over the entire datasets. The average processing time was 1.55 min for both datasets. Furthermore, this study shows that it is feasible to exploit heterogenous datasets from different imaging protocols and institutions for accurate multi-atlas cardiac structure segmentation.

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging.

SIGNIFICANCE: We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells' morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biopsies. AIM: Our objective was to determine quantitative epithelial and mesenchymal qualities of breast cancer cells through an unbiased, generalizable, and linear score covering the range of observed morphologies. APPROACH: Digital holographic microscopy was used to generate phase height maps of noncancerous epithelial (Gie-No3B11) and fibroblast (human gingival) cell lines, as well as MDA-MB-231 and MCF-7 breast cancer cell lines. Several machine learning algorithms were evaluated as binary classifiers of the noncancerous cells that graded the cancer cells by transfer learning. RESULTS: Epithelial and mesenchymal cells were classified with 96% to 100% accuracy. Breast cancer cells had scores in between the noncancer scores, indicating both epithelial and mesenchymal morphological qualities. The MCF-7 cells skewed toward epithelial scores, while MDA-MB-231 cells skewed toward mesenchymal scores. Linear support vector machines (SVMs) produced the most distinct score distributions for each cell line. CONCLUSIONS: The proposed epithelial-mesenchymal score, derived from linear SVM learning, is a sensitive and quantitative approach for detecting epithelial and mesenchymal characteristics of unknown cells based on well-characterized cell lines. We establish a framework for rapid and accurate morphological evaluation of single cells and subtle phenotypic shifts in imaged cell populations.