Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter study.

BACKGROUND: Despite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into treatment planning. In this multicenter study, we investigated the prognostic value of sarcopenia and myosteatosis automatically extracted from routine computed tomography (CT) scans of patients with advanced pancreatic ductal adenocarcinoma (PDAC). PATIENTS AND METHODS: We retrospectively analyzed clinical imaging data of 601 patients from three German cancer centers. We applied a deep learning approach to assess sarcopenia by the abdominal muscle-to-bone ratio (MBR) and myosteatosis by the ratio of abdominal inter- and intramuscular fat to muscle volume. In the pooled cohort, univariable and multivariable analyses were carried out to analyze the association between body composition markers and overall survival (OS). We analyzed the relationship between body composition markers and laboratory values during the first year of therapy in a subgroup using linear regression analysis adjusted for age, sex, and American Joint Committee on Cancer (AJCC) stage. RESULTS: Deep learning-derived MBR [hazard ratio (HR) 0.60, 95% confidence interval (CI) 0.47-0.77, P < 0.005] and myosteatosis (HR 3.73, 95% CI 1.66-8.39, P < 0.005) were significantly associated with OS in univariable analysis. In multivariable analysis, MBR (P = 0.019) and myosteatosis (P = 0.02) were associated with OS independent of age, sex, and AJCC stage. In a subgroup, MBR and myosteatosis were associated with albumin and C-reactive protein levels after initiation of therapy. Additionally, MBR was also associated with hemoglobin and total protein levels. CONCLUSIONS: Our work demonstrates that deep learning can be applied across cancer centers to automatically assess sarcopenia and myosteatosis from routine CT scans. We highlight the prognostic role of our proposed markers and show a strong relationship with protein levels, inflammation, and anemia. In clinical practice, automated body composition analysis holds the potential to further personalize cancer treatment.

A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation.

OBJECTIVE: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). METHODS: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. RESULTS: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%. CONCLUSION: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

A methodology of phenotyping ICU patients from EHR data: high-fidelity, personalized, and interpretable phenotypes estimation.

Objective: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). Methods: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. Results: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83% ± 27%. Conclusion: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

Impact of encorafenib on survival of patients with BRAFV600E-mutant metastatic colorectal cancer in a real-world setting.

PURPOSE: Patients with BRAFV600E-mutant metastatic colorectal cancer (mCRC) have a dismal prognosis. The best strategies in these patients remain elusive. Against this background, we report the clinical course of patients with BRAFV600E-mutant mCRC to retrieve the best treatment strategy. PATIENTS AND METHODS: Clinico-pathological data were extracted from the electronic health records. Kaplan-Meier method was used to estimate overall (OS) and progression-free survival (PFS). Objective response rate (ORR) was assessed according to RECIST 1.1. RESULTS: In total, 51 patients were enrolled. FOLFOXIRI was administered to 12 patients; 29 patients received FOLFOX or FOLFIRI as first-line treatment. Median OS was 17.6 months. Median PFS with FOLFOXIRI (13.0 months) was significantly prolonged (HR 0.325) as compared to FOLFOX/FOLFIRI (4.3 months). However, this failed to translate into an OS benefit (p = 0.433). Interestingly, addition of a monoclonal antibody to chemotherapy associated with superior OS (HR 0.523). A total of 64.7% patients received further-line therapy, which included a BRAF inhibitor in 17 patients. Targeted therapy associated with very favourable OS (25.1 months). CONCLUSION: Patients with BRAFV600E-mutated mCRC benefit from the addition of an antibody to first-line chemotherapy. Further-line treatment including a BRAF inhibitor has a dramatic impact on survival.

Smaller panel, similar results: genomic profiling and molecularly informed therapy in pancreatic cancer.

BACKGROUND: Pancreatic cancer has a dismal prognosis. One reason is resistance to cytotoxic drugs. Molecularly matched therapies might overcome this resistance but the best approach to identify those patients who may benefit is unknown. Therefore, we sought to evaluate a molecularly guided treatment approach. MATERIALS AND METHODS: We retrospectively analyzed the clinical outcome and mutational status of patients with pancreatic cancer who received molecular profiling at the West German Cancer Center Essen from 2016 to 2021. We carried out a 47-gene DNA next-generation sequencing (NGS) panel. Furthermore, we assessed microsatellite instability-high/deficient mismatch repair (MSI-H/dMMR) status and, sequentially and only in case of KRAS wild-type, gene fusions via RNA-based NGS. Patient data and treatment were retrieved from the electronic medical records. RESULTS: Of 190 included patients, 171 had pancreatic ductal adenocarcinoma (90%). One hundred and three patients had stage IV pancreatic cancer at diagnosis (54%). MMR analysis in 94 patients (94/190, 49.5%) identified 3 patients with dMMR (3/94, 3.2%). Notably, we identified 32 patients with KRAS wild-type status (16.8%). To identify driver alterations in these patients, we conducted an RNA-based fusion assay on 13 assessable samples and identified 5 potentially actionable fusions (5/13, 38.5%). Overall, we identified 34 patients with potentially actionable alterations (34/190, 17.9%). Of these 34 patients, 10 patients (10/34, 29.4%) finally received at least one molecularly targeted treatment and 4 patients had an exceptional response (>9 months on treatment). CONCLUSIONS: Here, we show that a small-sized gene panel can suffice to identify relevant therapeutic options for pancreatic cancer patients. Informally comparing with previous large-scale studies, this approach yields a similar detection rate of actionable targets. We propose molecular sequencing of pancreatic cancer as standard of care to identify KRAS wild-type and rare molecular subsets for targeted treatment strategies.

A prognostic systemic inflammation score (SIS) in patients with advanced intrahepatic cholangiocarcinoma.

PURPOSE: Systemic-inflammatory response parameters (SIR) are known prognostic markers in different tumour entities, but have not been evaluated in patients with iCCA treated with systemic chemotherapy. Therefore, we evaluated the impact of different SIR markers on the clinical course of patients with advanced iCCA treated at our center. METHODS: SIR markers were retrospectively evaluated in 219 patients with iCCA at the West-German-Cancer-Center Essen from 2014 to 2019. Markers included neutrophil/lymphocyte ratio (NLR), lymphocyte/monocyte ratio (LMR), CRP, and the modified Glasgow-Prognostic-Score (mGPS), which were correlated with clinico-pathological findings, response to chemotherapy (ORR), progression-free (PFS) and overall survival (OS) using Kaplan-Meier analyses, and Cox proportional models. RESULTS: Median overall survival (OS) of the entire cohort was 14.8 months (95% CI 11.2-24.4). Median disease-free survival (DFS) in 81 patients undergoing resection was 12.3 months (95% CI 9.7-23.1). The median OS from start of palliative CTX (OSpall) was 10.9 months (95% 9.4-14.6). A combined Systemic Inflammatory Score (SIS) comprising all evaluated SIR markers correlated significantly with ORR, PFS, and OSpall. Patients with a high SIS (≥ 2) vs. SIS 0 had a significantly inferior OSpall (HR 8.7 95% CI 3.71-20.38, p < 0.001). Multivariate analysis including known prognostic markers (ECOG, CA19-9, LDH, and N- and M-status) identified the SIS as an independent prognostic factor. CONCLUSIONS: Inflammatory markers associate with inferior survival outcomes in patients with iCCA. A simple SIS may guide treatment decisions in patients treated with systemic chemotherapy.

Multimodal survival prediction in advanced pancreatic cancer using machine learning.

BACKGROUND: Existing risk scores appear insufficient to assess the individual survival risk of patients with advanced pancreatic ductal adenocarcinoma (PDAC) and do not take advantage of the variety of parameters that are collected during clinical care. METHODS: In this retrospective study, we built a random survival forest model from clinical data of 203 patients with advanced PDAC. The parameters were assessed before initiation of systemic treatment and included age, CA19-9, C-reactive protein, metastatic status, neutrophil-to-lymphocyte ratio and total serum protein level. Separate models including imaging and molecular parameters were built for subgroups. RESULTS: Over the entire cohort, a model based on clinical parameters achieved a c-index of 0.71. Our approach outperformed the American Joint Committee on Cancer (AJCC) staging system and the modified Glasgow Prognostic Score (mGPS) in the identification of high- and low-risk subgroups. Inclusion of the KRAS p.G12D mutational status could further improve the prediction, whereas radiomics data of the primary tumor only showed little benefit. In an external validation cohort of PDAC patients with liver metastases, our model achieved a c-index of 0.67 (mGPS: 0.59). CONCLUSIONS: The combination of multimodal data and machine-learning algorithms holds potential for personalized prognostication in advanced PDAC already at diagnosis.

Improving interinstitutional and intertechnology consistency of pulmonary SBRT by dose prescription to the mean internal target volume dose.

PURPOSE: Dose, fractionation, normalization and the dose profile inside the target volume vary substantially in pulmonary stereotactic body radiotherapy (SBRT) between different institutions and SBRT technologies. Published planning studies have shown large variations of the mean dose in planning target volume (PTV) and gross tumor volume (GTV) or internal target volume (ITV) when dose prescription is performed to the PTV covering isodose. This planning study investigated whether dose prescription to the mean dose of the ITV improves consistency in pulmonary SBRT dose distributions. MATERIALS AND METHODS: This was a multi-institutional planning study by the German Society of Radiation Oncology (DEGRO) working group Radiosurgery and Stereotactic Radiotherapy. CT images and structures of ITV, PTV and all relevant organs at risk (OAR) for two patients with early stage non-small cell lung cancer (NSCLC) were distributed to all participating institutions. Each institute created a treatment plan with the technique commonly used in the institute for lung SBRT. The specified dose fractionation was 3â€¯× 21.5 Gy normalized to the mean ITV dose. Additional dose objectives for target volumes and OAR were provided. RESULTS: In all, 52 plans from 25 institutions were included in this analysis: 8 robotic radiosurgery (RRS), 34 intensity-modulated (MOD), and 10 3D-conformal (3D) radiation therapy plans. The distribution of the mean dose in the PTV did not differ significantly between the two patients (median 56.9 Gy vs 56.6 Gy). There was only a small difference between the techniques, with RRS having the lowest mean PTV dose with a median of 55.9 Gy followed by MOD plans with 56.7 Gy and 3D plans with 57.4 Gy having the highest. For the different organs at risk no significant difference between the techniques could be found. CONCLUSIONS: This planning study pointed out that multiparameter dose prescription including normalization on the mean ITV dose in combination with detailed objectives for the PTV and ITV achieve consistent dose distributions for peripheral lung tumors in combination with an ITV concept between different delivery techniques and across institutions.

Assessment of welfare indicators in dairy farms offering pasture at differing levels.

In terms of animal welfare, farming systems of dairy cows are perceived positively by consumers when compared to pigs or poultry. A main reason is that the majority of consumers associate dairy farming with pasture, which in turn they relate with benefits for animal health and welfare. However, holistic scientific assessments of the effects of pasturing on animal welfare are rare. Hence, it was the aim to study the animal welfare level in 61 German loose housing dairy farms by using the measures of the Welfare Quality® protocol for dairy cattle (WQP). Data were collected twice per farm at the end of the pasture season (July to October) and approximately 6 months later at the end of the barn season (December to April). Farms were classified based on the duration cows had access to pasture per day during the pasture season: group 1 (G1)>10 h; group 2 (G2) 6 to 10 h; group 3 (G3)<6 h and group 4 (G4) without pasture access. The average herd size was 129 Holstein-Friesian or Red-Holstein cows (range 58 to 527). In addition to WQP data, performance data were gathered from routine herd data recordings. The indicators were aggregated to criteria applying the scoring system of the WQP. G4 received lower scores at the first than at the second visit for the criterion absence of hunger, while there were no differences between visits in the other groups (P=0.58 - group×farm visit effect). All pasturing groups were scored better at the end of the pasture season than G4 for the criterion comfort around resting (P<0.01). Compared with G1 for both farm visits and G2 for the end of the barn season, G4 reached inferior scores for the criterion absence of injuries, including indicators such as hairless patches, lesions, and swellings and lameness. At both assessments G2 was scored higher than the other groups for the criterion absence of diseases (P=0.04). In conclusion, pasture access had positive effects only on selected welfare indicators, however, these effects were not maintained throughout the barn season.

Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms.

We study the question of how to represent or summarize raw laboratory data taken from an electronic health record (EHR) using parametric model selection to reduce or cope with biases induced through clinical care. It has been previously demonstrated that the health care process (Hripcsak and Albers, 2012, 2013), as defined by measurement context (Hripcsak and Albers, 2013; Albers et al., 2012) and measurement patterns (Albers and Hripcsak, 2010, 2012), can influence how EHR data are distributed statistically (Kohane and Weber, 2013; Pivovarov et al., 2014). We construct an algorithm, PopKLD, which is based on information criterion model selection (Burnham and Anderson, 2002; Claeskens and Hjort, 2008), is intended to reduce and cope with health care process biases and to produce an intuitively understandable continuous summary. The PopKLD algorithm can be automated and is designed to be applicable in high-throughput settings; for example, the output of the PopKLD algorithm can be used as input for phenotyping algorithms. Moreover, we develop the PopKLD-CAT algorithm that transforms the continuous PopKLD summary into a categorical summary useful for applications that require categorical data such as topic modeling. We evaluate our methodology in two ways. First, we apply the method to laboratory data collected in two different health care contexts, primary versus intensive care. We show that the PopKLD preserves known physiologic features in the data that are lost when summarizing the data using more common laboratory data summaries such as mean and standard deviation. Second, for three disease-laboratory measurement pairs, we perform a phenotyping task: we use the PopKLD and PopKLD-CAT algorithms to define high and low values of the laboratory variable that are used for defining a disease state. We then compare the relationship between the PopKLD-CAT summary disease predictions and the same predictions using empirically estimated mean and standard deviation to a gold standard generated by clinical review of patient records. We find that the PopKLD laboratory data summary is substantially better at predicting disease state. The PopKLD or PopKLD-CAT algorithms are not meant to be used as phenotyping algorithms, but we use the phenotyping task to show what information can be gained when using a more informative laboratory data summary. In the process of evaluation our method we show that the different clinical contexts and laboratory measurements necessitate different statistical summaries. Similarly, leveraging the principle of maximum entropy we argue that while some laboratory data only have sufficient information to estimate a mean and standard deviation, other laboratory data captured in an EHR contain substantially more information than can be captured in higher-parameter models.

Endoscopic therapy for infected pancreatic necrosis using fully covered self-expandable metal stents: combination of transluminal necrosectomy, transluminal and percutaneous drainage.

BACKGROUND/AIMS: Endoscopic transluminal therapy has become the standard of care as a less invasive alternative to surgery. In a retrospective case series of two tertiary referral centers we report on an individualized concept combining EUS-guided drainage with self-expanding metal stents, direct transluminal debridement und percutaneous drainage. METHODS: We treated 13 patients with infected pancreatic necrosis. Initially in all patients an EUS-guided drainage with plastic stents was performed under antibiotic protection (transduodenal: 2, transgastral: 11). After clinical consolidation (after 9.6 ±â€Š9.4 days) a covered self-expanding metal stent (Niti-S, Taewoong medical Co., Seoul, Korea) was inserted by performing direct endoscopic necrosectomy in 2.9 ±â€Š1.7 sessions through the stent. In cases of disrupted duct syndromes a pancreatic plastic stent was inserted (5 of 13 patients). In 5 of 13 cases additional percutaneous drainage was applied because of extended necrosis. In one patient percutaneous endoscopic drainage using the percutaneous access was needed. RESULTS: A sustained clinical success was achieved in 12 of 13 cases (CRP before therapy 23.5 ±â€Š14.4 mg/L, after 3.1 ±â€Š2.6 mg/lL). Discharge occurred after 2.5 ±â€Š22.4 days. The self-expanding metal stent was extracted after 82.5 ±â€Š56.6 days. Mean follow up was 8.5 ±â€Š5.9 months. CONCLUSION: Our concept of combining transluminal drainage, direct endoscopic necrosectomy and percutaneuos drainage offers a safe and reliable alternative to surgery, even in case of extended necrosis.

Visualizing Validation of Protein Surface Classifiers.

Many bioinformatics applications construct classifiers that are validated in experiments that compare their results to known ground truth over a corpus. In this paper, we introduce an approach for exploring the results of such classifier validation experiments, focusing on classifiers for regions of molecular surfaces. We provide a tool that allows for examining classification performance patterns over a test corpus. The approach combines a summary view that provides information about an entire corpus of molecules with a detail view that visualizes classifier results directly on protein surfaces. Rather than displaying miniature 3D views of each molecule, the summary provides 2D glyphs of each protein surface arranged in a reorderable, small-multiples grid. Each summary is specifically designed to support visual aggregation to allow the viewer to both get a sense of aggregate properties as well as the details that form them. The detail view provides a 3D visualization of each protein surface coupled with interaction techniques designed to support key tasks, including spatial aggregation and automated camera touring. A prototype implementation of our approach is demonstrated on protein surface classifier experiments.

Dynamical phenotyping: using temporal analysis of clinically collected physiologic data to stratify populations.

Using glucose time series data from a well measured population drawn from an electronic health record (EHR) repository, the variation in predictability of glucose values quantified by the time-delayed mutual information (TDMI) was explained using a mechanistic endocrine model and manual and automated review of written patient records. The results suggest that predictability of glucose varies with health state where the relationship (e.g., linear or inverse) depends on the source of the acuity. It was found that on a fine scale in parameter variation, the less insulin required to process glucose, a condition that correlates with good health, the more predictable glucose values were. Nevertheless, the most powerful effect on predictability in the EHR subpopulation was the presence or absence of variation in health state, specifically, in- and out-of-control glucose versus in-control glucose. Both of these results are clinically and scientifically relevant because the magnitude of glucose is the most commonly used indicator of health as opposed to glucose dynamics, thus providing for a connection between a mechanistic endocrine model and direct insight to human health via clinically collected data.

Population physiology: leveraging electronic health record data to understand human endocrine dynamics.

Studying physiology and pathophysiology over a broad population for long periods of time is difficult primarily because collecting human physiologic data can be intrusive, dangerous, and expensive. One solution is to use data that have been collected for a different purpose. Electronic health record (EHR) data promise to support the development and testing of mechanistic physiologic models on diverse populations and allow correlation with clinical outcomes, but limitations in the data have thus far thwarted such use. For example, using uncontrolled population-scale EHR data to verify the outcome of time dependent behavior of mechanistic, constructive models can be difficult because: (i) aggregation of the population can obscure or generate a signal, (ii) there is often no control population with a well understood health state, and (iii) diversity in how the population is measured can make the data difficult to fit into conventional analysis techniques. This paper shows that it is possible to use EHR data to test a physiological model for a population and over long time scales. Specifically, a methodology is developed and demonstrated for testing a mechanistic, time-dependent, physiological model of serum glucose dynamics with uncontrolled, population-scale, physiological patient data extracted from an EHR repository. It is shown that there is no observable daily variation the normalized mean glucose for any EHR subpopulations. In contrast, a derived value, daily variation in nonlinear correlation quantified by the time-delayed mutual information (TDMI), did reveal the intuitively expected diurnal variation in glucose levels amongst a random population of humans. Moreover, in a population of continuously (tube) fed patients, there was no observable TDMI-based diurnal signal. These TDMI-based signals, via a glucose insulin model, were then connected with human feeding patterns. In particular, a constructive physiological model was shown to correctly predict the difference between the general uncontrolled population and a subpopulation whose feeding was controlled.

Standard value of ultrasound elastography using acoustic radiation force impulse imaging (ARFI) in healthy liver tissue of children and adolescents.

PURPOSE: Ultrasound elastography by acoustic radiation force impulse imaging (ARFI) is used in adults for non invasive measurement of liver stiffness, indicating liver diseases like fibrosis. To establish ARFI in children and adolescents we determined standard values of healthy liver tissue and analysed potentially influencing factors. MATERIALS AND METHODS: 132 patients between 0 and 17 years old were measured using ARFI. None of them had any liver disease or any other disease that could affect the liver secondarily. All patients had a normal ultrasound scan, a normal BMI and normal liver function tests. The mean value of all ARFI measurements was calculated and potentially influencing factors were analysed. RESULTS: The mean value of all ARFI elastography measurements was 1.16 m/sec (SD ±â€Š0.14 m/sec). Neither age (p = 0.533) nor depth of measurement (p = 0.066) had no significant influence on ARFI values, whereas a significant effect of gender was found with lower ARFI values in females (p = 0.025), however, there was no significant interaction between age groups (before or after puberty) and gender (p = 0.276). There was an interlobar difference with lower values in the right liver lobe compared to the left (p = 0.036) and with a significantly lower variance (p < 0.001). Consistend values were measured by different examiners (p = 0.108), however, the inter examiner variance deviated significantly (p < 0.001). CONCLUSION: ARFI elastography is a reliable method to measure liver stiffness in children and adolescents. In relation to studies which analyse liver diseases, the standard value of 1.16 m/sec (±â€Š0.14 m/sec) allows a differentiation of healthy versus pathological liver tissue.

Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations.

This paper addresses how to calculate and interpret the time-delayed mutual information (TDMI) for a complex, diversely and sparsely measured, possibly non-stationary population of time-series of unknown composition and origin. The primary vehicle used for this analysis is a comparison between the time-delayed mutual information averaged over the population and the time-delayed mutual information of an aggregated population (here, aggregation implies the population is conjoined before any statistical estimates are implemented). Through the use of information theoretic tools, a sequence of practically implementable calculations are detailed that allow for the average and aggregate time-delayed mutual information to be interpreted. Moreover, these calculations can also be used to understand the degree of homo or heterogeneity present in the population. To demonstrate that the proposed methods can be used in nearly any situation, the methods are applied and demonstrated on the time series of glucose measurements from two different subpopulations of individuals from the Columbia University Medical Center electronic health record repository, revealing a picture of the composition of the population as well as physiological features.

Estimation of time-delayed mutual information and bias for irregularly and sparsely sampled time-series.

A method to estimate the time-dependent correlation via an empirical bias estimate of the time-delayed mutual information for a time-series is proposed. In particular, the bias of the time-delayed mutual information is shown to often be equivalent to the mutual information between two distributions of points from the same system separated by infinite time. Thus intuitively, estimation of the bias is reduced to estimation of the mutual information between distributions of data points separated by large time intervals. The proposed bias estimation techniques are shown to work for Lorenz equations data and glucose time series data of three patients from the Columbia University Medical Center database.

A statistical dynamics approach to the study of human health data: resolving population scale diurnal variation in laboratory data.

Statistical physics and information theory is applied to the clinical chemistry measurements present in a patient database containing 2.5 million patients' data over a 20-year period. Despite the seemingly naive approach of aggregating all patients over all times (with respect to particular clinical chemistry measurements), both a diurnal signal in the decay of the time-delayed mutual information and the presence of two sub-populations with differing health are detected. This provides a proof in principle that the highly fragmented data in electronic health records has potential for being useful in defining disease and human phenotypes.

Dosimetric evaluation of a 2D pixel ionization chamber for implementation in clinical routine.

In this paper we present the results of a dosimetric evaluation of a 2D ionization chamber array with the objective of its implementation for quality assurance in clinical routine. The pixel ionization chamber MatriXX (Scanditronix Wellhofer, Germany) consists of 32x32 chambers with a distance of 7.6 mm between chamber centres. The effective depth of measurement under the surface of the detector was determined. The dose and energy dependence, the behaviour of the device during its initial phase and its time stability as well as the lateral response of a single chamber of the detector in cross-plane and diagonal directions were analysed. It could be shown, that the detector's response is linear with dose and energy independent. Taking the lateral response into account, two different dose profiles, for a pyramidal and an IMRT dose distribution, were applied to compare the data generated by a treatment planning system with measurements. From these investigations it can be concluded that the detector is a suitable device for quality assurance and 2D dose verifications.