The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.

PurposeChanges in tumour 3′-deoxy-3′-[18F]fluorothymidine (FLT) uptake during concurrent chemo-radiotherapy in patients with non-small cell lung cancer (NSCLC) have been reported, at variable time points, in two pilot positron emission tomography (PET) studies. The aim of this study was to assess whether FLT changes occur early in response to radiotherapy (RT) without concurrent chemotherapy and whether such changes exceed test-retest variability.MethodsSixteen patients with NSCLC, scheduled to have radical RT, underwent FLT PET once/twice at baseline to assess reproducibility and/or after 5–11 RT fractions to evaluate response. Primary and nodal malignant lesions were manually delineated on CT and volume, mean and maximum standardized uptake values (SUVmean and SUVmax) estimated. Analysis included descriptive statistics and parameter fitting to a mixed-effects model accounting for patients having different numbers of evaluable lesions.ResultsIn all, 35 FLT PET scans from 7 patients with a total of 18 lesions and 12 patients with a total of 30 lesions were evaluated for reproducibility and response, respectively. SUVmean reproducibility in primary tumours (SD 8.9 %) was better than SUVmax reproducibility (SD 12.6 %). In nodes, SUVmean and SUVmax reproducibilities (SD 18.0 and 17.2 %) were comparable but worse than for primary tumours. After 5–11 RT fractions, primary tumour SUVmean decreased significantly by 25 % (p = 0.0001) in the absence of significant volumetric change, whereas metastatic nodes decreased in volume by 31 % (p = 0.020) with a larger SUVmean decrease of 40 % (p < 0.0001). Similar changes were found for SUVmax.ConclusionAcross this group of NSCLC patients, RT induced an early, significant decrease in lesion FLT uptake exceeding test-retest variability. This effect is variable between patients, appears distinct between primary and metastatic nodal lesions, and in primary tumours is lower than previously reported for concurrent chemo-RT at a similar time point. These results confirm the potential for FLT PET to report early on radiation response and to enhance the clinical development of novel drug-radiation combinations by providing an interpretable, early pharmacodynamic end point.Electronic supplementary materialThe online version of this article (doi:10.1007/s00259-013-2632-3) contains supplementary material, which is available to authorized users.

No abstract

The objective of the research article is to propose and validate a combination of machine learning and radiomics features to detect COVID-19 early and rapidly from chest X-ray (CXR) in presence of other viral/bacterial pneumonia and at different severity levels of diseases. It is vital to assess the performance of any diagnosis method on an independent data set and at very early stage of the disease when the disease severity of is very low. In such cases, most of the diagnosis methods fail. A total of 378 CXR images containing both normal lung and pneumonia (both COVID-19 and others lung conditions) were collected from publically available data set. 71 radiomics features for each lung segment were chosen from 100 extracted features based on Z-score heatmap and one way ANOVA test that can detect COVID-19. Three best performing classical machine learning algorithms during the training phase - 1) fine Gaussian support vector machine (SVM), 2) fine k-nearest neighbor (KNN) and 3) ensemble bagged model (EBM) trees were chosen for further evaluation on an independent test data set. The independent test data set consists of 115 COVID-19 CXR images collected from a local hospital and 100 CXR images collected from publically available data set containing normal lung and viral/bacterial pneumonia. Severity was scored between 0 to 4 by two experienced radiologists for each lung with pneumonia (both COVID-19 and non COVID-19) for the test data set. Ensemble Bagging Model Trees (EBM) with the selected radiomics features is the most suitable to distinguish between COVID-19 and other lung infections with an overall sensitivity of 87.8% and specificity of 97% (95.2% accuracy and 0.9228 area under curve) and is robust across severity levels. The method also can detect COVID-19 from CXR when two experienced radiologists were unable to detect any abnormality in the lung CXR (represented by severity score of 0). Once the CXR is acquired and lung is segmented, it takes less than two minutes for extracting radiomics features and providing diagnosis result. Since the proposed method does not require any manual intervention (e.g., sample collection etc.), it can be straightway integrated with standard X-ray reporting system to be used as an efficient, cost-effective and rapid early diagnosis device.