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. GWR applied to MRI modality pairs is a unique approach for small sample sizes and is a novel approach in this arena. From our analysis, all modality pairs involving ADC maps, and those involving post-contrast T1 regressed onto T2 showed potential promise. This work on ADC data adds to a growing body of research suggesting the predictive benefits of ADC, and suggests further research on the relationships between post-contrast T1 and T2.Clinical relevance- Few studies have investigated predictive potential of conventional MRI and ADC to detect PsP. Our study adds to the growing research on the topic and presents a new perspective to research by exploiting the utility of ADC in PsP v TP distinction. In addition, our GWR methodology for low-parametric supervised computer vision models demonstrates a unique approach for image processing of small sample sizes.

Genomic DNA activates the AIM2 inflammasome and STING pathways to induce inflammation in lacrimal gland myoepithelial cells.

PURPOSE: Primary Sjögren's syndrome (pSS) is an autoimmune disease that mainly attacks the lacrimal glands causing severe aqueous-deficient dry eye. Clinical evidence indicates the DNA sensing mechanism in the pathogenesis of pSS. The purpose of the present study is to determine the pro-inflammatory effect of self-genomic DNA (gDNA) on myoepithelial cells (MECs), which along with acinar and ductal cells is a major cell type of the lacrimal gland. METHOD: MECs primary culture was acquired from female C57BL6J mice. Genomic DNA was extracted from the spleen of the same animal. The MECs were challenged with self-gDNA. The cytokine secretion was detected using supernatant by enzyme-linked immunosorbent assay (ELISA). The activation of inflammasomes was determined using FAM-FLICA. Cryosections of NOD.B10.H2b mouse model of pSS were obtained for immunofluorescence microscopy (IF), with Balb/C as control. RESULT: Treatment with gDNA activated AIM2 inflammasome assembly and function, leading to secretion of interleukin (IL)-1ß and IL-18 in MECs. The stimulation of IL-1ß secretion by gDNA appeared to be solely at the post-translational level, whereas IL-18 secretion was a combination of increased protein synthesis and post-translational modification. Genomic DNA also induced the activation of STimulators of INterferon Genes (STING), which correlated to the activation of STING in the lacrimal gland from the NOD.B10.H2b mouse. STING activation led to the secretion of IFN-ß via Nuclear Factor-κB (NF-κB). The IFN-ß further enhances the secretion of IL-1ß. The contractility of MECs was disabled by treatment with gDNA or poly AnT, independent of the level of intracellular [Ca2+]. CONCLUSION: Self-gDNA induces a proinflammatory response in lacrimal gland MECs by activating both the AIM2 inflammasome and STING and thus may contribute to the pathogenesis of pSS.

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. The approach combines rapid olefin metathesis with rate-limiting isomerization. Monounsaturated PE is progressively disassembled at modest temperatures via many consecutive ethenolysis events, resulting selectively in propylene. Fully saturated PE can be converted to unsaturated PE starting with a single transfer dehydrogenation to ethylene, which produces a small amount of ethane (1 equiv per dehydrogenation event). These principles are demonstrated using both homogeneous and heterogeneous catalysts. While selectivity under batch conditions is limited at high conversion by the formation of an equilibrium mixture of olefins, high selectivity to propylene (≥94%) is achieved in a semicontinuous process due to the continuous removal of propylene from the reaction mixture.

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. GCs were cultured from pieces of human conjunctiva in RPMI media. CGCs were transferred 24 h before the start of experiments to phenol red-free and FBS-free media to minimize exogenous hormones. RT-PCR, immunofluorescence microscopy (IF), and Western Blot (WB) were performed to determine the presence of sex hormone receptors. Cellular response to pro-inflammatory stimuli or SPMs was studied by measuring the increase in intracellular [Ca2+] ([Ca2+]i) using fura 2/AM microscopy. Use of RT-PCR demonstrated estrogen receptor (ER) α in 4/5 males and 3/3 females; ERß in 2/4 males and 2/3 females; and androgen receptors (AR) in 3/3 male and 3/3 female CGCs. Positive immunoreactivity by IF and protein expression by WB was detected using antibodies for the ERα and ERß in 3/3 males and 3/3 females, while AR were only present in males. Significantly different Ca2+ responses between sexes were found with carbachol only at 10-3 M, but not with histamine or leukotriene (LT) B4 at any concentration used. Incubation with dihydrotestosterone (DHT), estrone (E1), or estradiol (E2) at 10-7 M for 30 min significantly inhibited the LTB4-stimulated [Ca2+]i increase in male and female CGCs. Incubation with DHT, E1, and E2 overnight significantly inhibited the LTB4 response in females, while DHT and E2 significantly inhibited the LTB4 response in males. The SPM lipoxin A4 (LXA4) (10-9-10-8 M), but not the resolvins D1 or D2, induced an [Ca2+]i increase that was significantly higher in males compared to females. We conclude that male and female CGCs showed differences in the expression of sex hormone receptors. Treatment with sex hormones altered pro-inflammatory mediator LTB4-induced response. Males compared to females have a higher response to the ω-6-fatty acid derived SPM LXA4, indicating males may terminate inflammation in conjunctival goblet cells faster than females.

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. To our knowledge, there have been no large studies of healthy cohorts that reported reference values for visceral fat area and radiation attenuation at multiple vertebra levels, for different contrast phases, and using different fat HU ranges. Two-phase CT scans from 1,677 healthy, adult kidney donors (age 18-65) between 1999 and 2017, previously studied to determine healthy reference values for skeletal muscle measures, were utilized. Visceral adipose tissue cross-sectional area (VFA) and radiation attenuation (VFRA) measures were quantified using axial slices at T10 through L4 vertebra levels. T-tests were used to compare males and females, while paired t-tests were conducted to determine the effect (magnitude and direction) of (a) contrast enhancement and (b) different fat HU ranges on each fat measure at each vertebra level. We report the means, standard deviations, and effect sizes of contrast enhancement and fat HU range. Male and female VFA and VFRA were significantly different at all vertebra levels in both contrast and non-contrast scans. Peak VFA was observed at L4 in females and L2 in males, while peak VFRA was observed at L1 in both females and males. In general, non-contrast scans showed significantly greater VFA and VFRA compared to contrast scans. The average paired difference due to contrast ranged from 1.6 to - 8% (VFA) and 3.2 to - 3.0% (VFRA) of the non-contrast value. HU range showed much greater differences in VFA and VFRA than contrast. The average paired differences due to HU range ranged from - 5.3 to 22.2% (VFA) and - 5.9 to 13.6% (VFRA) in non-contrast scans, and - 4.4 to 20.2% (VFA) and - 4.1 to 12.6% (VFRA) in contrast scans. The - 190 to - 30 HU range showed the largest differences in both VFA (10.8% to 22.2%) and VFRA (7.6% to 13.6%) compared to the reference range (- 205 to - 51 HU). Incidentally, we found that differences in lung inflation result in very large differences in visceral fat measures, particularly in the thoracic region. We assessed the independent effects of contrast presence and fat HU ranges on visceral fat cross-sectional area and mean radiation attenuation, finding significant differences particularly between different fat HU ranges. These results demonstrate that CT measurements of visceral fat area and radiation attenuation are strongly dependent upon contrast presence, fat HU range, sex, breath cycle, and vertebra level of measurement. We quantified contrast and non-contrast reference values separately for males and females, using different fat HU ranges, for lumbar and thoracic CT visceral fat measures at multiple vertebra levels in a healthy adult US population.

Simulated MRI Artifacts: Testing Machine Learning Failure Modes.

Objective. 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. Introduction. Real-world medical deployments of machine learning algorithms are less common than the number of medical research papers using machine learning. Part of the gap between the performance of models in research and deployment comes from a lack of hard test cases in the data used to train a model. Methods. These failure modes were simulated for a pretrained brain tumor segmentation model that utilizes standard MRI and used to evaluate the performance of the model under duress. These simulated MRI artifacts consisted of motion, susceptibility induced signal loss, aliasing, field inhomogeneity, sequence mislabeling, sequence misalignment, and skull stripping failures. Results. The artifact with the largest effect was the simplest, sequence mislabeling, though motion, field inhomogeneity, and sequence misalignment also caused significant performance decreases. The model was most susceptible to artifacts affecting the FLAIR (fluid attenuation inversion recovery) sequence. Conclusion. Overall, these simulated artifacts could be used to test other brain MRI models, but this approach could be used across medical imaging applications.

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. However, existing ML validation relies heavily on computing summary statistics and rarely tests the generalizability of an algorithm on clinically heterogeneous data. In this work, our goal is to investigate how to holistically evaluate the performance of ML algorithms on a brain tumor segmentation task. We address the need for rigorous evaluation of ML algorithms and present four axes of model evaluation-diagnostic performance, model confidence, robustness, and data quality. We perform a comprehensive evaluation of a glioma segmentation ML algorithm by stratifying data by specific tumor grade groups (GBM and LGG) and evaluate these algorithms on each of the four axes. The main takeaways of our work are-(1) ML algorithms need to be evaluated on out-of-distribution data to assess generalizability, reflective of tumor heterogeneity. (2) Segmentation metrics alone are limited to evaluate the errors made by ML algorithms and their describe their consequences. (3) Adoption of tools in other domains such as robustness (adversarial attacks) and model uncertainty (prediction intervals) lead to a more comprehensive performance evaluation. Such a holistic evaluation framework could shed light on an algorithm's clinical utility and help it evolve into a more clinically valuable tool.

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

ABSTRACT: 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. An introduction to the principles of radiomics and the advanced integration of imaging, clinical outcomes, and genomic data highlights the future potential for this field of research to better stratify and select patients for standard as well as investigational therapies.

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). To test for the accuracy and clinical usefulness of the algorithm, we used a unique cohort of prospectively followed patients with cirrhosis (n = 238) who had CT scans performed. To assess model performance, we used the confusion matrix and calculated the mean accuracy of 0.977 ± 0.02 (0.975 ± 0.018 for the training and test sets, respectively). To assess for spatial overlap, we measured the mean intersection over union and mean boundary contour scores and found excellent overlap between the manual and automated methods with mean scores of 0.954 ± 0.030, 0.987 ± 0.009, and 0.948 ± 0.039 (0.983 ± 0.013 for the training and test set, respectively). Using these automated measurements, we found that body composition features were predictive of mortality in patients with cirrhosis. On multivariate analysis, the addition of body composition measures significantly improved prediction of mortality for patients with cirrhosis over Model for End-Stage Liver Disease alone (P < 0.001). Conclusion: The measurement of body composition can be automated using artificial intelligence and add significant value for incidental CTs performed for other clinical indications. This is proof of concept that this methodology could allow for wider implementation into the clinical arena.

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. There is no agreed upon standard for body size adjustment. We extracted L3 SMA using non-contrast-enhanced CT scans from healthy adults, split into 'Under-40' and 'Over-40' cohorts. Sex-specific allometric analysis showed that height to the power of one was the optimal integer coefficient for height adjusted SMA in both males and females. We computed two height-adjusted measures [Formula: see text] and [Formula: see text], comparing their Pearson correlations versus age, height, weight, and BMI separately by sex and cohort. Finally, in the 'Under-40' cohort, we used linear regression to convert each height-adjusted measure into a z-score ([Formula: see text], [Formula: see text]) adjusted for BMI. [Formula: see text] was less correlated with height in both males and females ([Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text]) than [Formula: see text] ([Formula: see text] and [Formula: see text], [Formula: see text]). [Formula: see text] was uncorrelated with BMI and weight, and minimally correlated with height in males and females ([Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text]). The final [Formula: see text] equation was: [Formula: see text], where [Formula: see text], [Formula: see text], [Formula: see text], and sex = 1 if male, 0 if female. We propose [Formula: see text] for optimal height adjustment and the [Formula: see text] score for optimal height and BMI adjustment. By minimizing correlations with height and BMI, the [Formula: see text] score produces unbiased assessments of relative L3 skeletal muscle area across the full range of body sizes.

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. The artifacts simulated were bubbles, tissue folds, uneven illumination, marker lines, uneven sectioning, altered staining, and tissue tears. RESULTS: We found that there is some performance degradation on the tiles with artifacts, particularly with altered stains but also with marker lines, tissue folds, and uneven sectioning. We also found that the response of deep learning models to artifacts could be nonlinear. CONCLUSIONS: Generated artifacts can provide a useful tool for testing and building trust in machine learning models by understanding where these models might fail.

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. The dataset consists of five original MRI sequences; pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, FLAIR, and ADC images as well as two engineered sequences; T1post-T1pre and T2-FLAIR. Next, we used three CNN-LSTM models with a different set of sequences as input sequences to pass through CNN-LSTM layers. We performed threefold cross-validation in the training dataset and generated the boxplot, accuracy, and ROC curve, AUC from each trained model with the test dataset to evaluate models. The mean accuracy for VGG16 models ranged from 0.44 to 0.60 and the mean AUC ranged from 0.47 to 0.59. For CNN-LSTM model, the mean accuracy ranged from 0.62 to 0.75 and the mean AUC ranged from 0.64 to 0.81. The performance of the proposed CNN-LSTM with multiparametric sequence data was found to outperform the popular convolutional CNN with a single MRI sequence. In conclusion, incorporating all available MRI sequences into a sequence input for a CNN-LSTM model improved diagnostic performance for discriminating between pseudoprogression and true tumor progression.

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. The volumes of the suprafascial and subfascial compartments were calculated after manually drawing the region of interest on computed tomographic images. The volumetric differences and their ratios in the suprafascial and subfascial compartments between each patient's bilateral limbs were compared. RESULTS: The volume of the lymphedematous limbs (9647 ml) was significantly greater than the volume of unaffected limbs (6906 ml), with a median volumetric difference of 2097 ml (30.6 percent) (p < 0.01). The median suprafascial compartment volumetric difference was 1887 ml (56.6 percent) and the subfascial compartment volumetric difference was 208 ml (4.7 percent) (p < 0.01). The median volumetric difference ratio of the thigh and lower leg was 24.6 percent and 40.6 percent, respectively. The median volumetric differences in Cheng lymphedema grades I to IV were 1012, 1787, 2434, and 4107 ml, respectively, which were statistically significant among the four Cheng lymphedema grades using the Kruskal-Wallis test (p < 0.01). CONCLUSIONS: The volumetric differences in the lymphedematous limb were statistically significantly greater than in the unaffected limb, including both suprafascial and subfascial compartments. The volumetric differences are consistent with the Cheng lymphedema grading system as a reliable indicator of unilateral extremity lymphedema. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, IV.

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. METHODS: Using deep convolutional neural networks, we trained an algorithm on the Reference Analytic Morphomics Population (n = 5,268) and validated the automated methodology in an external cohort of adult kidney donors with a noncontrast CT scan (n = 1,655). To test the clinical usefulness, we examined its ability to predict clinical outcomes in a prospectively followed cohort of patients with clinically diagnosed cirrhosis (n = 254). RESULTS: Between the manual and automated methodologies, we found excellent inter-rater agreement with an intraclass correlation coefficient of 0.957 (confidence interval 0.953-0.961, P < 0.0001) in the adult kidney donor cohort. The calculated dice similarity coefficient was 0.932 ± 0.042, suggesting excellent spatial overlap between manual and automated methodologies. To assess the clinical usefulness, we examined its ability to predict clinical outcomes in a cirrhosis cohort and found that automated psoas muscle index was independently associated with mortality after adjusting for age, gender, and child's classification (P < 0.001). DISCUSSION: We demonstrated that deep learning techniques can allow for automation of muscle measurements on clinical CT scans in a diseased cohort. These automated psoas size measurements were predictive of mortality in patients with cirrhosis showing proof of principal that this methodology may allow for wider implementation in the clinical arena.

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. METHODS: Fifteen participants with diagnosed upper limb lymphedema were included between July 2013 and June 2017 at Chang Gung Memorial Hospital in Taipei, Taiwan. Affected and unaffected arm circumferences were measured using a flexible tape or morphomic measurement at 10 cm above and below the elbow. Computed tomography scans were standardized, processed, smoothed with a piecewise polynomial algorithm for Analytic Morphomics of arm circumference. Comparative plots, mean percent difference, and adjusted coefficient of determination (R 2) were utilized to compare the consistency of both measurement procedures. RESULTS: The tape measure and Analytic Morphomics demonstrated consistent measures of arm circumference. On the affected arm, the mean (95% CI) difference in arm circumference between methods was 1.60 cm (0.99-2.20) above, and 0.57 cm (0.23-0.91) below the elbow. Mean percent differences in circumference was 6.65% (SD 3.52%) above and 1.38% (SD 2.11%) below the elbow. The adjusted R 2 for both methods was 94% above and 96% below the elbow. CONCLUSIONS: Analytic Morphomics showed strong consistency with the manual tape measure of arm circumference measurement in those with upper extremity lymphedema. Analytic Morphomics present an opportunity for a precise, granular measurement of limb composition for assessment of disease state and patient planning.

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. Features from a pre-trained convolutional neural network had similar predictive performances and robustness as TDA, but also performed better using an alternative classification algorithm, k-top scoring pairs. Feature robustness can be used as a filtering technique without greatly impacting model performance and can also be used to evaluate model stability.

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. METHODS: Targeted exon sequencing with a custom panel of 223 genes/regions was performed for 125 DCIS cases. Among them, 60 were from cases having concurrent or subsequent invasive breast cancer (IBC) (DCIS + IBC group), and 65 from cases with no IBC development over a median follow-up of 13 years (DCIS-only group). Copy number alterations in chromosome 1q32, 8q24, and 11q13 were analyzed using fluorescence in situ hybridization (FISH). Multivariable logistic regression models were fit to the outcome of DCIS progression to IBC as functions of demographic and clinical features. RESULTS: We observed recurrent variants of known IBC-related mutations, and the most commonly mutated genes in DCIS were PIK3CA (34.4%) and TP53 (18.4%). There was an inverse association between PIK3CA kinase domain mutations and progression (Odds Ratio [OR] 10.2, p < 0.05). Copy number variations in 1q32 and 8q24 were associated with progression (OR 9.3 and 46, respectively; both p < 0.05). CONCLUSIONS: PIK3CA kinase domain mutations and the absence of copy number gains in DCIS are protective against progression to IBC. These results may guide efforts to distinguish low-risk from high-risk 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. MVA activity was found to be increased in lapatinib-resistant and lapatinib + trastuzumab-resistant cells. Specific blockade of this pathway with lipophilic but not hydrophilic statins and with the N-bisphosphonate zoledronic acid led to apoptosis and substantial growth inhibition of R cells. Inhibition was rescued by mevalonate or the intermediate metabolites farnesyl pyrophosphate or geranylgeranyl pyrophosphate, but not cholesterol. Activated Yes-associated protein (YAP)/transcriptional coactivator with PDZ-binding motif (TAZ) and mTORC1 signaling, and their downstream target gene product Survivin, were inhibited by MVA blockade, especially in the lapatinib-resistant/lapatinib + trastuzumab-resistant models. Overexpression of constitutively active YAP rescued Survivin and phosphorylated-S6 levels, despite blockade of the MVA. These results suggest that the MVA provides alternative signaling leading to cell survival and resistance by activating YAP/TAZ-mTORC1-Survivin signaling when HER2 is blocked, suggesting novel therapeutic targets. MVA inhibitors including lipophilic statins and N-bisphosphonates may circumvent resistance to anti-HER2 therapy warranting further clinical investigation. IMPLICATIONS: The MVA was found to constitute an escape mechanism of survival and growth in HER2+ breast cancer models resistant to anti-HER2 therapies. MVA inhibitors such as simvastatin and zoledronic acid are potential therapeutic agents to resensitize the tumors that depend on the MVA to progress on anti-HER2 therapies.

Computed tomography correlation of skeletal landmarks and vascular anatomy in civilian adult trauma patients: Implications for resuscitative endovascular balloon occlusion of the aorta.

BACKGROUND: Resuscitative endovascular balloon occlusion of the aorta (REBOA) is a valuable resuscitative adjunct in a variety of clinical settings. In resource-limited or emergency environments, REBOA may be required with delayed or absent image-guidance or verification. Catheter insertion lengths may be informed by making computed tomography (CT) correlations of skeletal landmarks with vascular lengths. METHODS: Between 2000 and 2015 at a single civilian tertiary care center, 2,247 trauma patients with CT imaging were identified, yielding 1,789 patients with adequate contrast opacification of the arterial system in the chest, abdomen, and pelvis. Individual scans were analyzed using MATLAB software, with custom high-throughput image processing algorithms applied to correlate centerline vascular anatomy with musculoskeletal landmarks. Data were analyzed using R version 3.3. RESULTS: The median centerline distance from the skin access to the aortic bifurcation was longer by 0.3 cm on the right than on the left side. Median aortic zone I length was 21.6 (interquartile range, 20.3-22.9) cm, while zone III was 8.7 (7.8-9.5) cm. Torso extent (TE) correlation to zone I was much higher than that for zone III (R2, 0.58 vs. 0.26 (right) and 0.58 vs. 0.27 (left); p < 0.001). Assuming a 4-cm balloon length, optimal fixed insertion length would be 48 cm and 28 cm for zones I and III (error, 0.4% vs. 33.3%), respectively, although out of zone placements can be reduced if adjusted for TE (error, 0% vs. 26.4%). CONCLUSION: Computed tomography morphometry suggests that a fixed REBOA catheter insertion length of 48 cm for zone I and 28 cm for zone III is optimal (on average, for average-height individuals), with improved accuracy by formulaic adjustments for TE. High residual error for zone III placement may require redesign of existing catheter balloon lengths or consideration of the relative risk associated with placing the balloon catheter too low or too high. LEVEL OF EVIDENCE: Prognostic/epidemiological, level III.