Marcis Leja1,2,3, Juha M Kortelainen4, Inese Polaka1, Emmi Turppa4, Jan Mitrovics5, Marta Padilla5, Pawel Mochalski6,7, Gregory Shuster8, Roland Pohle9, Dmitry Kashanin10, Richard Klemm11, Veikko Ikonen12, Linda Mezmale1,2, Yoav Y Broza13, Gidi Shani13, Hossam Haick13. 1. Institute of Clinical and Preventive Medicine & Faculty of Medicine, University of Latvia, Riga, Latvia. 2. Riga East University Hospital, Riga, Latvia. 3. Digestive Diseases Centre GASTRO, Riga, Latvia. 4. Smart Health, VTT Technical Research Centre of Finland, Tampere, Finland. 5. JLM Innovations, Tübingen, Germany. 6. Institute of Breath Research, University of Innsbruck, Innsbruck, Austria. 7. Institute of Chemistry, Jan Kochanowski University, Kielce, Poland. 8. NanoVation-GS, Haifa, Israel. 9. Research in Digitalization and Automation, Siemens, Munich, Germany. 10. Cellix, Dublin, Ireland. 11. Microfluidic ChipShop, Jena, Germany. 12. Ethics and Responsibility of Innovations, VTT Technical Research Centre of Finland, Tampere, Finland. 13. Department of Chemical Engineering and Russel Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, Israel.
Abstract
BACKGROUND: Detection of disease by means of volatile organic compounds from breath samples using sensors is an attractive approach to fast, noninvasive and inexpensive diagnostics. However, these techniques are still limited to applications within the laboratory settings. Here, we report on the development and use of a fast, portable, and IoT-connected point-of-care device (so-called, SniffPhone) to detect and classify gastric cancer to potentially provide new qualitative solutions for cancer screening. METHODS: A validation study of patients with gastric cancer, patients with high-risk precancerous gastric lesions, and controls was conducted with 2 SniffPhone devices. Linear discriminant analysis (LDA) was used as a classifying model of the sensing signals obatined from the examined groups. For the testing step, an additional device was added. The study group included 274 patients: 94 with gastric cancer, 67 who were in the high-risk group, and 113 controls. RESULTS: The results of the test set showed a clear discrimination between patients with gastric cancer and controls using the 2-device LDA model (area under the curve, 93.8%; sensitivity, 100%; specificity, 87.5%; overall accuracy, 91.1%), and acceptable results were also achieved for patients with high-risk lesions (the corresponding values for dysplasia were 84.9%, 45.2%, 87.5%, and 65.9%, respectively). The test-phase analysis showed lower accuracies, though still clinically useful. CONCLUSION: Our results demonstrate that a portable breath sensor device could be useful in point-of-care settings. It shows a promise for detection of gastric cancer as well as for other types of disease. LAY SUMMARY: A portable sensor-based breath analyzer for detection of gastric cancer can be used in point-of-care settings. The results are transferrable between devices via advanced IoT technology. Both the hardware and software of the reported breath analyzer could be easily modified to enable detection and monitirng of other disease states.
BACKGROUND: Detection of disease by means of volatile organic compounds from breath samples using sensors is an attractive approach to fast, noninvasive and inexpensive diagnostics. However, these techniques are still limited to applications within the laboratory settings. Here, we report on the development and use of a fast, portable, and IoT-connected point-of-care device (so-called, SniffPhone) to detect and classify gastric cancer to potentially provide new qualitative solutions for cancer screening. METHODS: A validation study of patients with gastric cancer, patients with high-risk precancerous gastric lesions, and controls was conducted with 2 SniffPhone devices. Linear discriminant analysis (LDA) was used as a classifying model of the sensing signals obatined from the examined groups. For the testing step, an additional device was added. The study group included 274 patients: 94 with gastric cancer, 67 who were in the high-risk group, and 113 controls. RESULTS: The results of the test set showed a clear discrimination between patients with gastric cancer and controls using the 2-device LDA model (area under the curve, 93.8%; sensitivity, 100%; specificity, 87.5%; overall accuracy, 91.1%), and acceptable results were also achieved for patients with high-risk lesions (the corresponding values for dysplasia were 84.9%, 45.2%, 87.5%, and 65.9%, respectively). The test-phase analysis showed lower accuracies, though still clinically useful. CONCLUSION: Our results demonstrate that a portable breath sensor device could be useful in point-of-care settings. It shows a promise for detection of gastric cancer as well as for other types of disease. LAY SUMMARY: A portable sensor-based breath analyzer for detection of gastric cancer can be used in point-of-care settings. The results are transferrable between devices via advanced IoT technology. Both the hardware and software of the reported breath analyzer could be easily modified to enable detection and monitirng of other disease states.
Authors: Max H M C Scheepers; Zaid Al-Difaie; Lloyd Brandts; Andrea Peeters; Bart van Grinsven; Nicole D Bouvy Journal: JAMA Netw Open Date: 2022-06-01
Authors: Daria Ślefarska-Wolak; Christine Heinzle; Andreas Leiherer; Clemens Ager; Axel Muendlein; Linda Mezmale; Marcis Leja; Alejandro H Corvalan; Heinz Drexel; Agnieszka Królicka; Gidi Shani; Christopher A Mayhew; Hossam Haick; Paweł Mochalski Journal: Molecules Date: 2022-06-22 Impact factor: 4.927
Authors: Andrzej Kwiatkowski; Sebastian Borys; Katarzyna Sikorska; Katarzyna Drozdowska; Janusz M Smulko Journal: Sci Rep Date: 2022-09-26 Impact factor: 4.996