A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules.

Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs.

Use of legal and illegal substances in Malé (Republic of Maldives) assessed by wastewater analysis.

This study used wastewater-based epidemiology (WBE) to investigate the lifestyle of the inhabitants of Malé, the capital of the Republic of Maldives. Raw wastewater 12-h composite samples were collected from nine pumping stations serving the city area - thus representative of the whole Malé population. Samples were analysed by liquid chromatography coupled to mass spectrometry for estimating the profile of use of a large number of substances including illicit drugs, alcohol, caffeine, tobacco and pharmaceuticals. The illicit drugs most used were cannabis (THC) and heroin (700 and 18 g/day), with lower consumption of cocaine and amphetamines (0.1-1.2 g/day). It is important to note that the consumption of cannabis in Malé was comparable to that measured in other countries, while the consumption of heroin was higher. Among cathinones, mephedrone was detected at the highest levels similar to other countries. Consumption of alcohol, which is not allowed in Maldives, was found (1.3 L/day/1000 inhabitants), but at a low level compared with other countries (6-44 L/day/1000 inhabitants), while the consumption of caffeine and tobacco was generally in line with reports from other countries. Unique information on pharmaceuticals use was also provided, since no official data were available. Human lifestyle was evaluated by applying for the first time the full set of WBE methodologies available in our laboratory. Results provided valuable epidemiological information, which may be useful for national and international agencies to understand population lifestyles better, including illicit drug issues, and for planning and evaluation of drug prevention programs in Malé.