Background Infection with SARS-CoV-2 causes corona virus disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19-positive and negative persons based on volatile organic compounds (VOCs) analysis. Methods Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, the presence of SARS-CoV-2-specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine learning and used for pattern recognition. The result is a value between − 1 and + 1, indicating the infection probability. Results 219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96. Conclusions The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery.

Background: Infection with SARS-CoV-2 causes Corona Virus Disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19 positive- and negative persons based on volatile organic compounds (VOCs) analysis.Methods: Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, presence of SARS-CoV-2 specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine-learning and used for pattern recognition. The result is a value between -1 and +1, indicating the infection probability.Results: 219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96. Conclusions: The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery.

Background In recent decades there has been growing interest in the use of volatile organic compounds (VOCs) in exhaled breath as biomarkers for the diagnosis of multiple variants of cancer. This review aimed to evaluate the diagnostic accuracy and current status of VOC analysis in exhaled breath for the detection of cancer in the digestive tract. Methods PubMed and the Cochrane Library database were searched for VOC analysis studies, in which exhaled air was used to detect gastro-oesophageal, liver, pancreatic, and intestinal cancer in humans, Quality assessment was performed using the QUADAS-2 criteria. Data on diagnostic performance, VOCs with discriminative power, and methodological information were extracted from the included articles. Results Twenty-three articles were included (gastro-oesophageal cancer n = 14, liver cancer n = 1, pancreatic cancer n = 2, colorectal cancer n = 6). Methodological issues included different modalities of patient preparation and sampling and platform used. The sensitivity and specificity of VOC analysis ranged from 66.7 to 100 per cent and from 48.1 to 97.9 per cent respectively. Owing to heterogeneity of the studies, no pooling of the results could be performed. Of the VOCs found, 32 were identified in more than one study. Nineteen were reported as cancer type-specific, whereas 13 were found in different cancer types. Overall, decanal, nonanal, and acetone were the most frequently identified. Conclusion The literature on VOC analysis has documented a lack of standardization in study designs. Heterogeneity between the studies and insufficient validation of the results make interpretation of the outcomes challenging. To reach clinical applicability, future studies on breath analysis should provide an accurate description of the methodology and validate their findings.

Cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC), often combined with systemic therapy, can be offered to selected colorectal peritoneal metastases (PM) patients. However, clinical heterogeneity and the lack of high-level evidence challenges determination of the correct treatment strategy. This review aims to provide an overview of current strategies to predict survival of colorectal PM patients treated with CRS and HIPEC, guiding clinicians to select a suitable treatment-strategy and to inform patients about their prognosis. First, the prognostic relevance of several clinicopathological prognostic factors, such as extent of PM, location of primary tumor, histology type, and the presence of lymph node or liver metastases will be discussed. Subsequently, special attention will be given to recent developments in several aspects of tumor biology such as RAF/RAS mutations, circulating tumor DNA, immunoprofiling, and consensus molecular subtypes. Finally, currently available prognostic models to predict survival will be evaluated, concluding these models perform moderate to good, but most of them partly rely on intra-operative data. New insights in tumor biology, as well as the reliable assessment of extent of peritoneal disease by diffusion weighted MRI pose promising opportunities to establish an adequate and clinically meaningful preoperative prognostic model in the near future.

For peritoneal metastases (PM), there are few curative treatment options, and they are only available for a select patient group. Recently, new therapies have been developed to deliver intraperitoneal chemotherapy for a prolonged period, suitable for a larger patient group. These drug delivery systems (DDSs) seem promising in the experimental setting. Many types of DDSs have been explored in a variety of animal models, using different cytostatics. This review aimed to provide an overview of animal studies using DDSs containing cytostatics for the treatment of gastro-intestinal PM and identify the most promising therapeutic combinations. The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and Systematic Review Center for Laboratory Animal Experimentation (SYRCLE) guidelines. The 35 studies included revealed similar results: using a cytostatic-loaded DDS to treat PM resulted in a higher median survival time (MST) and a lower intraperitoneal tumor load compared to no treatment or treatment with a ‘free’ cytostatic or an unloaded DDS. In 65% of the studies, the MST was significantly longer and in 24% the tumor load was significantly lower in the animals treated with cytostatic-loaded DDS. The large variety of experimental setups made it impossible to identify the most promising DDS-cytostatic combination. In most studies, the risk of bias was unclear due to poor reporting. Future studies should focus more on improving the clinical relevance of the experiments, standardizing the experimental study setup, and improving their methodological quality and reporting.