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Background Artificial intelligence can be trained to outperform dermatologists in image-based skin cancer diagnostics. However, the networks' sensitivity to biases and overfitting may hamper their clinical applicability. Objectives The aim of this study was to explain the potential consequences of implementing convolutional neural networks for stand-alone melanoma diagnostics and skin lesion triage. Methods In this algorithm validation study on retrospective data, we reproduced and evaluated the performance of state-of-the-art artificial intelligence (convolutional neural networks) for skin cancer diagnostics. The networks were trained on 25,331 annotated dermoscopic skin lesion images from an open-source data set (ISIC-2019) and tested using a novel data set (AISC-2021) consisting of 26,591 annotated dermoscopic skin lesion images. We tested the trained algorithms' ability to generalize to new data and their diagnostic performance in two simulations (melanoma diagnostics and skin lesion triage). Results The trained algorithms performed significantly less accurate diagnostics on images of nevi, melanomas and actinic keratoses from the AISC-2021 data set than the ISIC-2019 data set (p < 0.003). Almost one-third (31.1%) of the melanomas were misclassified during the melanoma diagnostics simulation, irrespective of their Breslow thickness. Furthermore, the algorithms marked 92.7% of the lesions ‘suspicious’ during the triage simulation, which yielded a triage sensitivity and specificity of 99.7% and 8.2%, respectively. Conclusions Although state-of-the-art artificial intelligence outperforms dermatologists on image-based skin lesion classification within an artificial setting, additional data and technological advances are needed before clinical implementation

Diagnostic imaging often outperforms the surgeon's ability to identify small structures during therapeutic procedures. Smart soft tissue markers that translate the sensitivity of diagnostic imaging into optimal therapeutic intervention are therefore highly warranted. This paper presents a unique adaptable liquid soft tissue marker system based on functionalized carbohydrates (Carbo-gel). The liquid state of these markers allows for high-precision placement under image guidance using thin needles. Based on step-by-step modifications, the image features and mechanical properties of markers can be optimized to bridge diagnostic imaging and specific therapeutic interventions. The performance of Carbo-gel is demonstrated for markers that (i) have radiographic, magnetic resonance, and ultrasound visibility; (ii) are palpable and visible; and (iii) are localizable by near-infrared fluorescence and radio guidance. The study demonstrates encouraging proof of concept for the liquid marker system as a well-tolerated multimodal imaging marker that can improve image-guided radiotherapy and surgical interventions, including robotic surgery.

BACKGROUND: Abdominal fat distribution is associated with development of cardio-metabolic disease, independently of BMI. We assessed anthropometry, serum adipokines and DXA as markers of abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) by MRIMETHODS:We performed a cross-sectional study including 197 healthy adolescents (114 boys) aged 10-15 years nested within a longitudinal population-based cohort. Clinical examination, blood sampling, DXA and abdominal MRI was performed. SAT% and VAT% was adjusted to total abdominal volume.RESULTS: Girls had a higher SAT% than boys in early and late puberty (16 vs. 13%, P<0.01 and 20 vs. 15%, P=0.001, respectively), whereas VAT% was comparable (7% in both genders, independent of puberty). DXA android fat% (Standard Deviation Score [SDS]), suprailliac skinfold thickness (SDS), leptin, BMI (SDS), waist-to-height ratio (WHtR) and waist circumference (SDS) correlated strongly with SAT% (descending order: r=0.90 to r=0.55, all P<0.001), but weaker to VAT% (r=0.49 to r=0.06). Suprailiac skinfold was the best anthropometric marker of SAT% (girls: R(2)=48.6%, boys: R(2)=65.0%, P<0.001) and VAT% in boys (R(2)=16.4%, P<0.001). WHtR was the best marker of VAT% in girls (R(2)=7.6%, P=0.007).CONCLUSIONS: Healthy girls have a higher SAT% than boys, whereas VAT% is comparable, independent of puberty. Anthropometry and circulating leptin are valid markers of SAT%, but not VAT%.Pediatric Research accepted article preview online, 12 June 2017. doi:10.1038/pr.2017.138.

PURPOSE: We investigated the use of a simple calibration method to remove bias in previously proposed approaches to image-derived input functions (IDIFs) when used to calculate the metabolic uptake rate of glucose (K(m)) from dynamic [(18)F]-FDG PET scans of the thigh. Our objective was to obtain nonbiased, low-variance K(m) values without blood sampling.MATERIALS AND METHODS: We evaluated eight previously proposed IDIF methods. K(m) values derived from these IDIFs were compared with Km values calculated from the arterial blood samples (gold standard). We used linear regression to extract calibration parameters to remove bias. Following calibration, cross-validation and bootstrapping were used to estimate the mean square error and variance.RESULTS: Three of the previously proposed methods failed mainly because of zero-crossings of the IDIF. The remaining five methods were improved by calibration, yielding unbiased Km values. The method with the lowest SD yielded an SD of 0.0017/min--that is, below 10% of the muscle K(m) value in this study.CONCLUSION: Previously proposed IDIF methods can be improved by using a simple calibration procedure. The calibration procedure may be used in other studies, thus obviating the need for arterial blood sampling, once the calibration parameters have been established in a subgroup of participants. The method has potential for use in other parts of the body as it is robust with regard to partial volume effects.

Physical exercise increases peripheral insulin sensitivity, but regional differences are poorly elucidated in humans. We investigated the effect of aerobic exercise training on insulin-stimulated glucose uptake in five individual femoral muscle groups and four different adipose tissue regions using dynamic (femoral region) and static (abdominal region) 2-deoxy-2-(18F)fluoro-D-glucose (FDG) PET/CT methodology during steady state insulin infusion (40 mU*m-2*min-1). Body composition was measured by Dual X-ray Absorptiometry and MRI. Sixty-one healthy, sedentary (VO2max: 36(5) ml*kg-1*min-1)(mean(SD)), moderately overweight (BMI: 28.1(1.8)), young (age: 30(6) years) men were randomized to sedentary living (CON; n=17 completers), moderate (MOD; 300 kcal/day, n=18) or high (HIGH; 600 kcal/day, n=18) dose physical exercise for 11 weeks. At baseline, insulin-stimulated glucose uptake was highest in femoral skeletal muscle followed by intraperitoneal visceral adipose tissue (VAT), retroperitoneal VAT, abdominal (anterior+posterior) subcutaneous adipose tissue (SAT) and femoral SAT (P<0.0001 between tissues). Metabolic rate of glucose increased similarly (~30%) in the two exercise groups in femoral skeletal muscle (MOD: 24[9; 39] µmol*kg-1*min-1, P=0.004; HIGH: 22[9; 35] µmol*kg-1*min-1, P=0.003) (mean[95% CI]) and in five individual femoral muscle groups but not in femoral SAT. Standardized Uptake Value of FDG decreased ~24% in anterior abdominal SAT and ~20% in posterior abdominal SAT when compared to CON but not in either intra- or retroperitoneal VAT. Total adipose tissue mass decreased in both exercise groups and the decrease was distributed equally among subcutaneous and intraabdominal depots. In conclusion, aerobic exercise training increases insulin-stimulated glucose uptake in skeletal muscle but not in adipose tissue, which demonstrates some interregional differences.