StableLift: Optimized Germline and Somatic Variant Detection Across Genome Builds.

Reference genomes are foundational to modern genomics. Our growing understanding of genome structure leads to continual improvements in reference genomes and new genome "builds" with incompatible coordinate systems. We quantified the impact of genome build on germline and somatic variant calling by analyzing tumour-normal whole-genome pairs against the two most widely used human genome builds.

Molecular Hallmarks of Prostate-specific Membrane Antigen in Treatment-naïve Prostate Cancer.

Background And Objective: We characterized tumor prostate-specific membrane antigen (PSMA) levels as a reflection of cancer biology and treatment sensitivities for treatment-naïve prostate cancer.

Methods: We first correlated PSMA positron emission tomography (PET) maximum standardized uptake values (SUVmax) in primary prostate cancer with tumor FOLH1 (PSMA RNA abundance) to establish RNA as a proxy (n = 55). We then discovered and validated molecular pathways associated with PSMA RNA levels in two large primary tumor cohorts.

Low-parameter supervised learning models can discriminate pseudoprogression and true progression in non-perfusion-based MRI.

Discrimination of pseudoprogression and true progression is one challenge to the treatment of malignant gliomas. Although some techniques such as circulating tumor DNA (ctDNA) and perfusion-weighted imaging (PWI) demonstrate promise in distinguishing PsP from TP, we investigate robust and replicable alternatives to distinguish the two entities based on more widely-available media. In this study, we use low-parametric supervised learning techniques based on geographically-weighted regression (GWR) to investigate the utility of both conventional MRI sequences as well as a diffusion-weighted sequence (apparent diffusion coefficient or ADC) in the discrimination of PsP v TP.

Simulated MRI Artifacts: Testing Machine Learning Failure Modes.

. Seven types of MRI artifacts, including acquisition and preprocessing errors, were simulated to test a machine learning brain tumor segmentation model for potential failure modes. .

Chemical Recycling of Polyethylene by Tandem Catalytic Conversion to Propylene.

Although polyethylene (PE) and polypropylene (PP) are by far the world's largest volume plastics, only a tiny fraction of these energy-rich polyolefins are currently recycled. Depolymerization of PE to its constituent monomer, ethylene, is highly endothermic and conventionally accessible only through unselective, high-temperature pyrolysis. Here, we provide experimental demonstrations of our recently proposed tandem catalysis strategy, which uses ethylene to convert PE to propylene, the commodity monomer used to make PP.

Sex-based differences in conjunctival goblet cell responses to pro-inflammatory and pro-resolving mediators.

Many conjunctival inflammatory diseases differ between the sexes and altered conjunctival goblet cells (CGCs) response is often involved. Inflammation is initiated by the release of pro-inflammatory mediators and terminated by the biosynthesis of specialized pro-resolution mediators (SPMs). Herein, we determined the sex-based difference in the responses of CGCs to inflammatory stimuli or pro-resolving lipid SPMs and their interaction with sex hormones.

Healthy US population reference values for CT visceral fat measurements and the impact of IV contrast, HU range, and spinal levels.

Measurements of visceral adipose tissue cross-sectional area and radiation attenuation from computed tomography (CT) scans provide useful information about risk and mortality. However, scan protocols vary, encompassing differing vertebra levels and utilizing differing phases of contrast enhancement. Furthermore, fat measurements have been extracted from CT using different Hounsfield Unit (HU) ranges.

Stress Testing Pathology Models with Generated Artifacts.

Background: Machine learning models provide significant opportunities for improvement in health care, but their "black-box" nature poses many risks.

Methods: We built a custom Python module as part of a framework for generating artifacts that are meant to be tunable and describable to allow for future testing needs. We conducted an analysis of a previously published digital pathology classification model and an internally developed kidney tissue segmentation model, utilizing a variety of generated artifacts including testing their effects.

Stratification by Tumor Grade Groups in a Holistic Evaluation of Machine Learning for Brain Tumor Segmentation.

Accurate and consistent segmentation plays an important role in the diagnosis, treatment planning, and monitoring of both High Grade Glioma (HGG), including Glioblastoma Multiforme (GBM), and Low Grade Glioma (LGG). Accuracy of segmentation can be affected by the imaging presentation of glioma, which greatly varies between the two tumor grade groups. In recent years, researchers have used Machine Learning (ML) to segment tumor rapidly and consistently, as compared to manual segmentation.

Advanced Imaging and Computational Techniques for the Diagnostic and Prognostic Assessment of Malignant Gliomas.

Advanced imaging techniques provide a powerful tool to assess the intratumoral and intertumoral heterogeneity of gliomas. Advances in the molecular understanding of glioma subgroups may allow improved diagnostic assessment combining imaging and molecular tumor features, with enhanced prognostic utility and implications for patient treatment. In this article, a comprehensive overview of the physiologic basis for conventional and advanced imaging techniques is presented, and clinical applications before and after treatment are discussed.

Automated Measurements of Body Composition in Abdominal CT Scans Using Artificial Intelligence Can Predict Mortality in Patients With Cirrhosis.

Body composition measures derived from already available electronic medical records (computed tomography [CT] scans) can have significant value, but automation of measurements is needed for clinical implementation. We sought to use artificial intelligence to develop an automated method to measure body composition and test the algorithm on a clinical cohort to predict mortality. We constructed a deep learning algorithm using Google's DeepLabv3+ on a cohort of de-identified CT scans (n = 12,067).

Optimal body size adjustment of L3 CT skeletal muscle area for sarcopenia assessment.

Measurements of skeletal muscle cross-sectional area (SMA) at the level of the third lumbar (L3) vertebra derived from clinical computed tomography (CT) scans are commonly used in assessments of sarcopenia, the loss of skeletal muscle mass and function associated with aging. As SMA is correlated with height and Body Mass Index (BMI), body size adjustment is necessary to fairly assess sarcopenic low muscle mass in individuals of different height and BMI. The skeletal muscle index, a widely used measure, adjusts for height as [Formula: see text] but uses no BMI adjustment.

Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning.

Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particularly high grade, which leads to a potential treatment delay for patients with early glioma recurrence. In this study, we proposed to use a multiparametric MRI data as a sequence input for the convolutional neural network with the recurrent neural network based deep learning structure to discriminate between pseudoprogression and true tumor progression. In this study, 43 biopsy-proven patient data identified as diffuse infiltrating glioma patients whose disease progressed/recurred were used.

Automated Measurements of Muscle Mass Using Deep Learning Can Predict Clinical Outcomes in Patients With Liver Disease.

Introduction: There is increasing recognition of the central role of muscle mass in predicting clinical outcomes in patients with liver disease. Muscle size can be extracted from computed tomography (CT) scans, but clinical implementation will require increased automation. We hypothesize that we can achieve this by using artificial intelligence.

Volumetric Differences in the Suprafascial and Subfascial Compartments of Patients with Secondary Unilateral Lower Limb Lymphedema.

Background: The subfascial compartment (deep to the deep fascia) in extremity lymphedema has not been evaluated. This study investigated the volumetric differences between the suprafascial and subfascial compartments of patients with unilateral lower extremity lymphedema.

Methods: Thirty-two female patients with unilateral lower extremity lymphedema were enrolled, with eight patients in each of Cheng lymphedema grades I to IV.

Comparisons of Manual Tape Measurement and Morphomics Measurement of Patients with Upper Extremity Lymphedema.

Background: Lymphedema is a debilitating condition characterized by swelling from lymph fluid exceeding transport capacity. A gold standard for arm measurement is not established, and measurement methods vary. This study evaluates the comparability of the tape measure and Analytic Morphomics in deriving limb circumference measurements in patients with upper extremity lymphedema.

Genomic landscape of ductal carcinoma in situ and association with progression.

Purpose: The detection rate of breast ductal carcinoma in situ (DCIS) has increased significantly, raising the concern that DCIS is overdiagnosed and overtreated. Therefore, there is an unmet clinical need to better predict the risk of progression among DCIS patients. Our hypothesis is that by combining molecular signatures with clinicopathologic features, we can elucidate the biology of breast cancer progression, and risk-stratify patients with DCIS.

Targeting the Mevalonate Pathway to Overcome Acquired Anti-HER2 Treatment Resistance in Breast Cancer.

Despite effective strategies, resistance in HER2 breast cancer remains a challenge. While the mevalonate pathway (MVA) is suggested to promote cell growth and survival, including in HER2 models, its potential role in resistance to HER2-targeted therapy is unknown. Parental HER2 breast cancer cells and their lapatinib-resistant and lapatinib + trastuzumab-resistant derivatives were used for this study.

Prediction of 1p/19q Codeletion in Diffuse Glioma Patients Using Pre-operative Multiparametric Magnetic Resonance Imaging.

This study compared the predictive power and robustness of texture, topological, and convolutional neural network (CNN) based image features for measuring tumors in MRI. These features were used to predict 1p/19q codeletion in the MICCAI BRATS 2017 challenge dataset. Topological data analysis (TDA) based on persistent homology had predictive performance as good as or better than texture-based features and was also less susceptible to image-based perturbations.