Electronic nose technology in the diagnosis of prostate cancer

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Abstract

Prostate cancer is a significant problem in modern oncourology due to its high incidence and mortality, largely due to untimely diagnosis of the disease. This article provides an overview of current diagnostic methods, including biopsy and magnetic resonance imaging, highlighting their limitations such as invasiveness and insufficient sensitivity. Given the need for more accurate and non-invasive diagnostic techniques, the potential use of an “electronic nose” — a multisensory system capable of detecting volatile organic compounds in urine samples — is explored. The literature review indicates that the use of this technique may offer high sensitivity and specificity in detecting prostate cancer, comparable to results obtained from specially trained detection dogs. The article analyzes recent clinical studies that validate the effectiveness of the electronic nose in identifying prostate cancer and describes the machine learning methodologies employed for recognizing urine samples. It is important to create uniform standards for the analysis of the gas composition of urine using the electronic nose. For the widespread implementation of this diagnostic method, it is necessary to conduct large randomized studies with the formation of a sufficient evidence base.

About the authors

Mkrtich S. Mosoyan

Almazov National Medical Research Centre; Academician I.P. Pavlov First St. Petersburg State Medical University

Email: moso03@yandex.ru
ORCID iD: 0000-0003-3639-6863

MD, Dr. Sci. (Medicine), Professor

Russian Federation, Saint Petersburg; Saint Petersburg

Igor E. Jahatspanian

Scientific and Production Association “Pribor”

Email: drjie@mail.ru
ORCID iD: 0000-0002-4858-6499

Cand. Sci. (Engineering)

Russian Federation, Saint Petersburg

Artyom A. Vasilev

Almazov National Medical Research Centre

Email: scapaflow12@gmail.com
SPIN-code: 3359-1097

MD

Russian Federation, Saint Petersburg

Vladimir A. Makeev

Almazov National Medical Research Centre

Author for correspondence.
Email: dr.makeev2016@mail.ru
SPIN-code: 9408-7310

MD

Russian Federation, Saint Petersburg

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2. Fig. 1. Algorithm for odor recognition using an electronic nose (comparative diagram of biological and electronic noses). Photo: Marianna Yerknapeshyan / Scientific Russia. Information sourced from the portal “Scientific Russia” (https://scientificrussia.ru/).

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3. Fig. 2. Electronic nose “Aramos 7”. Photo source: NPO “Pribor” (Saint Petersburg).

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