Coronary Artery Plaque Characterization from CCTA Scans Using Deep Learning and Radiomics

Assessing coronary artery plaque segments in coronary CT angiography scans is an important task to improve patient management and clinical outcomes, as it can help to decide whether invasive investigation and treatment are necessary. In this work, we present three machine learning approaches capable of performing this task. The first approach is based on radiomics, where a plaque segmentation is used to calculate various shape-, intensity- and texture-based features under different image transformations. A second approach is based on deep learning and relies on centerline extraction as sole prerequisite. In the third approach, we fuse the deep learning approach with radiomic features. On our data the methods reached similar scores as simulated fractional flow reserve (FFR) measurements, which - in contrast to our methods - requires an exact segmentation of the whole coronary tree and often time-consuming manual interaction. In literature, the performance of simulated FFR reaches an AUC between 0.79–0.93 predicting an abnormal invasive FFR that demands revascularization. The radiomics approach achieves an AUC of 0.84, the deep learning approach 0.86 and the combined method 0.88 for predicting the revascularization decision directly. While all three proposed methods can be determined within seconds, the FFR simulation typically takes several minutes. Provided representative training data in sufficient quantities, we believe that the presented methods can be used to create systems for fully automatic non-invasive risk assessment for a variety of adverse cardiac events

Whole-genome amplified DNA from stored dried blood spots is reliable in high resolution melting curve and sequencing analysis

<p>Abstract</p> <p>Background</p> <p>The use of dried blood spots (DBS) samples in genomic workup has been limited by the relative low amounts of genomic DNA (gDNA) they contain. It remains to be proven that whole genome amplified DNA (wgaDNA) from stored DBS samples, constitutes a reliable alternative to gDNA.</p> <p>We wanted to compare melting curves and sequencing results from wgaDNA derived from DBS samples with gDNA derived from whole blood.</p> <p>Methods</p> <p>gDNA was extracted from whole blood obtained from 10 patients with lone atrial fibrillation (mean age 22.3 years). From their newborn DBS samples, stored at -24°C, genomic DNA was extracted and whole-genome amplified in triplicates. Using high resolution melting curve analysis and direct sequencing in both wgaDNA and gDNA samples, all coding regions and adjacent intron regions of the genes <it>SCN5A </it>and <it>KCNA5 </it>were investigated.</p> <p>Results</p> <p>Altered melting curves was present in 85 of wgaDNA samples and 81 of gDNA samples. Sequence analysis identified a total of 31 variants in the 10 wgaDNA samples. The same 31 variants were found in the exact same pattern of samples in the gDNA group. There was no false positive or negative sequence variation in the wgaDNA group.</p> <p>Conclusions</p> <p>The use of DNA amplified in triplicates from DBS samples is reliable and can be used both for high resolution curve melting analysis as well as direct sequence analysis. DBS samples therefore can serve as an alternative to whole blood in sequence analysis.</p

Sea-ice-free Arctic during the Last Interglacial supports fast future loss

The Last Interglacial (LIG), a warmer period 130-116 ka before present, is a potential analog for future climate change. Stronger LIG summertime insolation at high northern latitudes drove Arctic land summer temperatures 4-5 °C higher than the preindustrial era. Climate model simulations have previously failed to capture these elevated temperatures, possibly because they were unable to correctly capture LIG sea-ice changes. Here, we show the latest version of the fully-coupled UK Hadley Center climate model (HadGEM3) simulates a more accurate Arctic LIG climate, including elevated temperatures. Improved model physics, including a sophisticated sea-ice melt-pond scheme, result in a complete simulated loss of Arctic sea ice in summer during the LIG, which has yet to be simulated in past generations of models. This ice-free Arctic yields a compelling solution to the longstanding puzzle of what drove LIG Arctic warmth and supports a fast retreat of future Arctic summer sea ice

Risk of surgical site infection and efficacy of antibiotic prophylaxis: a cohort study of appendectomy patients in Thailand

BACKGROUND: No data currently exist about use of antibiotics to prevent surgical site infections (SSI) among patients undergoing appendectomy in Thailand. We therefore examined risk factors, use, and efficacy of prophylactic antibiotics for surgical site infection SSI among patients with uncomplicated open appendectomy. METHODS: From July 1, 2003 to June 30, 2004 we conducted a prospective cohort study in eight hospitals in Thailand. We used the National Nosocomial Infection Surveillance (NNIS) system criteria to identify SSI associated with appendectomy. We used logistic regression analysis to obtain relative risk estimates for predictors of SSI. RESULTS: Among 2139 appendectomy patients, we identified 26 SSIs, yielding a SSI rate of 1.2 infections/100 operations. Ninety-two percent of all patients (95% CI, 91.0–93.3) received antibiotic prophylaxis. Metronidazole and gentamicin were the two most common antibiotic agents, with a combined single dose administered in 39% of cases. In 54% of cases, antibiotic prophylaxis was administered for one day. We found that a prolonged duration of operation was significantly associated with an increased SSI risk. Antibiotic prophylaxis was significantly associated with a decreased risk of SSI regardless of whether the antibiotic was administered preoperatively or intraoperatively. Compared with no antibiotic prophylaxis, SSI relative risks for combined single-dose of metronidazole and gentamicin, one-day prophylaxis, and multiple-day antibiotic prophylaxis were 0.28 (0.09–0.90), 0.30 (0.11–0.88) and 0.32 (0.10–0.98), respectively. CONCLUSION: Single-dose combination of metronidazole and gentamicin seems sufficient to reduce SSIs in uncomplicated appendicitis patients despite whether the antibiotic was administered preoperatively or intraoperatively

In silico assessment of biomedical products: the conundrum of rare but not so rare events in two case studies

In silico clinical trials, defined as “The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention,” have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients’ phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern