TY - JOUR
T1 - Sensing gastric cancer via point-of-care sensor breath analyzer
AU - on behalf of the SniffPhone Project Research Group
AU - Leja, Mārcis
AU - Kortelainen, Juha M.
AU - Poļaka, Inese
AU - Turppa, Emmi
AU - Mitrovics, Jan
AU - Padilla, Marta
AU - Mochalski, Pawel
AU - Shuster, Gregory
AU - Pohle, Roland
AU - Kashanin, Dmitry
AU - Klemm, Richard
AU - Ikonen, Veikko
AU - Mežmale, Linda
AU - Broza, Yoav Y.
AU - Shani, Gidi
AU - Haick, Hossam
AU - Kloper, Viki
AU - Milyutin, Yana
AU - Abboud, Manal
AU - Saliba, Walaa
AU - Bdarneh, Shifaa
AU - Khateb, Salam
AU - Gharra, Alaa
AU - Zuri, Liat
AU - Vasiļjevs, Edgars
AU - Lauka, Lelde
AU - Gašenko, Evita
AU - Škapars, Roberts
AU - Sīviņš, Armands
AU - Bogdanova, Inga
AU - Isajevs, Sergejs
AU - Kikuste, Ilze
AU - Vanags, Aigars
AU - Tolmanis, Ivars
AU - Kojalo, Ilona
AU - Veliks, Viktors
AU - Jaeschke, Carsten
AU - Fleischer, Max
AU - Sramek, Maria
AU - nav Gils, Mark
AU - Kulju, Minna
AU - Miettinen, Janika
N1 - Publisher Copyright:
© 2021 American Cancer Society
PY - 2021/4/15
Y1 - 2021/4/15
N2 - 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.
AB - 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.
KW - breath analyzer
KW - gastric cancer
KW - personalized
KW - precancerous lesion
KW - screening
KW - volatile organic compound
UR - https://acsjournals.onlinelibrary.wiley.com/doi/10.1002/cncr.33437
UR - https://www.scopus.com/pages/publications/85102740693
U2 - 10.1002/cncr.33437
DO - 10.1002/cncr.33437
M3 - Article
C2 - 33739456
SN - 0008-543X
VL - 127
SP - 1286
EP - 1292
JO - Cancer
JF - Cancer
IS - 8
ER -